A knowledge-based approach to scientific workflow composition

Scientific Workflow Systems have been developed as a means to enable scientists to carry out complex analysis operations on local and remote data sources in order to achieve their research goals. Systems typically provide a large number of components and facilities to enable such analysis to be performed and have matured to a point where they offer many complex capabilities. This complexity makes it difficult for scientists working with these systems to readily achieve their goals. In this thesis we describe the increasing burden of knowledge required of these scientists in order for them to specify the outcomes they wish to achieve within the workflow systems. We consider ways in which the challenges presented by these systems can be reduced, focusing on the following questions: How can metadata describing the resources available assist users in composing workflows? Can automated assistance be provided to guide users through the composition process? Can such an approach be implemented so as to work with the resources provided by existing Scientific Workflow Systems? We have developed a new approach to workflow composition which makes use of a number of features: an ontology for recording metadata relating to workflow components, a set of algorithms for analyzing the state of a workflow composition and providing suggestions for how to progress based on this metadata, an API to enable both the algorithms and metadata to utilise the resources provided by existing Scientific Workflow Systems, and a prototype user interface to demonstrate how our proposed approach to workflow composition can work in practice. We evaluate the system to show the approach is valid and capable of reducing some of the difficulties presented by existing systems, but that limitations exist regarding the complexity of workflows which can be composed, and also regarding the challenge of initially populating the metadata ontology

INDIGO-DataCloud: A data and computing platform to facilitate seamless access to e-infrastructures

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications

Scientific Analysis by the Crowd: A System for Implicit Collaboration between Experts, Algorithms, and Novices in Distributed Work.

Crowd sourced strategies have the potential to increase the throughput of tasks historically constrained by the performance of individual experts. A critical open question is how to configure crowd-based mechanisms, such as online micro-task markets, to accomplish work normally done by experts. In the context of one kind of expert work, feature extraction from electron microscope images, this thesis describes three experiments conducted with Amazon’s Mechanical Turk to explore the feasibility of crowdsourcing for tasks that traditionally rely on experts. The first experiment combined the output from learning algorithms with judgments made by non-experts to see whether the crowd could efficiently and accurately detect the best algorithmic performance for image segmentation. Image segmentation is an important but rate limiting step in analyzing biological imagery. Current best practice relies on extracting features by hand. Results showed that crowd workers were able to match the results of expert workers in 87.5% of the cases given the same task and that they did so with very little training. The second experiment used crowd responses to progressively refine task instructions. Results showed that crowd workers were able to consistently add information to the instructions and produced results the crowd perceived as more clear by an average of 8.7%. Finally, the third experiment mapped images to abstract representations to see whether the crowd could efficiently and accurately identify target structures. Results showed that crowd workers were able to find 100% of known structures with an 82% decrease in false positives compared to conventional automated image processing. This thesis makes a number of contributions. First, the work demonstrates that tasks previously performed by highly-trained experts, such as image extraction, can be accomplished by non-experts in less time and with comparable accuracy when organized through a micro-task market. Second, the work shows that engaging crowd workers to reflect on the description of tasks can be used to have them refine tasks to produce increased engagement by subsequent crowd workers. Finally, the work shows that abstract representations perform nearly as well as actual images in terms of using a crowd of non-experts to locate targeted features.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102368/1/dlzz_1.pd

Use of Software Tools to Implement Quality Control of Ultrasound Images in a Large Clinical Trial

Research Question This thesis aims to answer the question as to whether software tools might be developed for automating the analysis of images used to measure ovaries in transvaginal sonography (TVS) exams. Such tools would allow the routine collection of independent and objective metrics at low cost and might be used to drive a programme of continuous Quality Improvement (QI) in TVS scanning. The tools will be assessed by processing images from thousands of TVS exams performed by the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Background This research is important because TVS is core to any ovarian cancer (OC) screening strategy yet independent and objective quality control (QC) metrics for this procedure are not routinely obtained due to the high cost of manual image inspection. Improving the quality of TVS in the National Health Service (NHS) would assist in the early diagnosis of the disease and result in improved outcome for some women. Therefore, the research has clear translational potential for the >1.2 million scans performed annually by the NHS. Research Findings A study performed to process images from 1,000 TVS exams has shown the tool produces accurate and reliable QC metrics. A further study revealed that over half of these exams should have been classified as unsatisfactory as an expert review of the images showed that that the sonographer had mistakenly measured a structure that was not an ovary. It also reported a correlation between such ovary visualisation and a novel metric (DCR) measured by the tools from the examination images. Conclusion The research results suggest both a need to improve the quality of TVS scanning and the viability of achieving this objective by introducing a QI programme driven by metrics gathered by software tools able to analyze the images used to measure ovaries

ADVANCED MOTION MODELS FOR RIGID AND DEFORMABLE REGISTRATION IN IMAGE-GUIDED INTERVENTIONS

Image-guided surgery (IGS) has been a major area of interest in recent decades that continues to transform surgical interventions and enable safer, less invasive procedures. In the preoperative contexts, diagnostic imaging, including computed tomography (CT) and magnetic resonance (MR) imaging, offers a basis for surgical planning (e.g., definition of target, adjacent anatomy, and the surgical path or trajectory to the target). At the intraoperative stage, such preoperative images and the associated planning information are registered to intraoperative coordinates via a navigation system to enable visualization of (tracked) instrumentation relative to preoperative images. A major limitation to such an approach is that motions during surgery, either rigid motions of bones manipulated during orthopaedic surgery or brain soft-tissue deformation in neurosurgery, are not captured, diminishing the accuracy of navigation systems. This dissertation seeks to use intraoperative images (e.g., x-ray fluoroscopy and cone-beam CT) to provide more up-to-date anatomical context that properly reflects the state of the patient during interventions to improve the performance of IGS. Advanced motion models for inter-modality image registration are developed to improve the accuracy of both preoperative planning and intraoperative guidance for applications in orthopaedic pelvic trauma surgery and minimally invasive intracranial neurosurgery. Image registration algorithms are developed with increasing complexity of motion that can be accommodated (single-body rigid, multi-body rigid, and deformable) and increasing complexity of registration models (statistical models, physics-based models, and deep learning-based models). For orthopaedic pelvic trauma surgery, the dissertation includes work encompassing: (i) a series of statistical models to model shape and pose variations of one or more pelvic bones and an atlas of trajectory annotations; (ii) frameworks for automatic segmentation via registration of the statistical models to preoperative CT and planning of fixation trajectories and dislocation / fracture reduction; and (iii) 3D-2D guidance using intraoperative fluoroscopy. For intracranial neurosurgery, the dissertation includes three inter-modality deformable registrations using physic-based Demons and deep learning models for CT-guided and CBCT-guided procedures

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal