9 research outputs found
Documents as functions
Treating variable data documents as functions over their data bindings opens opportunities for building more powerful, robust and flexible document architectures to meet the needs arising from the confluence of developments in document engineering, digital printing technologies and marketing analysis.
This thesis describes a combination of several XML-based technologies both to represent and to process variable documents and their data, leading to extensible, high-quality and 'higher-order' document generation solutions. The architecture (DDF) uses XML uniformly throughout the documents and their processing tools with interspersing of different semantic spaces being achieved through namespacing.
An XML-based functional programming language (XSLT) is used to describe all intra-document variability and for implementing most of the tools. Document layout intent is declared within a document as a hierarchical set of combinators attached to a tree-based graphical presentation. Evaluation of a document bound to an instance of data involves using a compiler to create an executable from the document, running this with the data instance as argument to create a new document with layout intent described, followed by resolution of that layout by an extensible layout processor.
The use of these technologies, with design paradigms and coding protocols, makes it possible to construct documents that not only have high flexibility and quality, but also perform in higher-order ways. A document can be partially bound to data and evaluated, modifying its presentation and still remaining variably responsive to future data. Layout intent can be re-satisfied as presentation trees are modified by programmatic sections embedded within them. The key enablers are described and illustrated through example
Recommended from our members
Statistical inference in stochastic/deterministic epidemic models to jointly estimate transmission and severity
This thesis explores the joint estimation of transmission and severity of infectious diseases, focussing on the specific case of influenza. Transmission governs the speed and magnitude of viral spread in a population, while severity determines morbidity and mortality and the resulting effect on health care facilities. Their quantification is crucial to inform public health policies, motivating the routine collection of data on influenza cases.
The estimation of severity is compromised by the high degree of censoring affecting the data early during the epidemic. The challenge of estimating transmission is that each influenza data source is often affected by noise and selection bias and individually provides only partial information on the underlying process.
To address severity estimation with high censored data, new methods, inspired by demographic models and by parametric survival analysis, are formulated. A comprehensive review of these methods and existing methods is also carried out.
To jointly estimate transmission and severity, an initial Bayesian epidemic model is fitted to historical data on severe cases, assuming a deterministic severity process and using a single data source. This model is then extended to describe a more stochastic and hence more realistic process of severe events, with the data generating process governed by hidden random variables in a state-space framework. Such increased realism necessitates the use of multiple data sources to enhance parameter identifiability, in a Bayesian evidence synthesis context. In contrast to the literature in the field, the model introduced accounts for dependencies between datasets. The added stochasticity and unmeasured dependencies result in an intractable likelihood. Inference therefore requires a new approach based on Monte Carlo methods.
The method proposed proves its potential and usefulness in the concluding application to real data from the latest (2017/18) epidemic of influenza in England.MRC PhD scholarship
Cambridge Philosophical Society scholarshi
Metadatos y recuperación de información: estándares, problemas y aplicabilidad en bibliotecas digitales
Programa de Doctorado en DocumentaciónPresidente: Mercedes Caridad Sebastián. - Secretario: Antonio Hernández Pérez. - Vocales: José Carlos Rovira Soler, Eulalia Fuentes i Pujol, José Antonio Gómez Hernánde