Developing Typologies of City-Regional Growth

The economic performance of city-regions is closely linked to the performance of the national economy. However, the performance of the national economy can also depend on the performance of one or more major city-regions, that act as growth poles. Because of their sectoral structure and other characteristics, some cities are better equipped to become growth poles than others. This paper studies 46 major city-regions across Europe. The sectoral structure and changes in the sectoral structure of city-regions are studied using data from CE’s European Regional Database, itself based on Eurostat’s Regio database. The data analysis attempts to explain city-region performance by drawing parallels between sectoral structure and economic performance. The data analysis is supplemented by local anecdotal evidence provided by CE’s annual European reporting system ‘European Regional Prospects’, for example the historical importance of river and seafront activities. The paper goes on to discuss the extent to which the sectoral structure of cities can explain why some city-regions grow faster than others. The data analysis will be used to group cities in ‘hard’ typologies according to sectoral specialisation. These sectoral typologies are then compared with typologies according to the local, ‘softer’, evidence provided by CE’s regional consultants. This evidence will also be used to draw out the more subtle influences on city-region growth and these will be used to group cities in ‘soft’ typologies.

On Modeling and Estimation for the Relative Risk and Risk Difference

A common problem in formulating models for the relative risk and risk difference is the variation dependence between these parameters and the baseline risk, which is a nuisance model. We address this problem by proposing the conditional log odds-product as a preferred nuisance model. This novel nuisance model facilitates maximum-likelihood estimation, but also permits doubly-robust estimation for the parameters of interest. Our approach is illustrated via simulations and a data analysis.Comment: To appear in Journal of the American Statistical Association: Theory and Method

Congenial Causal Inference with Binary Structural Nested Mean Models

Structural nested mean models (SNMMs) are among the fundamental tools for inferring causal effects of time-dependent exposures from longitudinal studies. With binary outcomes, however, current methods for estimating multiplicative and additive SNMM parameters suffer from variation dependence between the causal SNMM parameters and the non-causal nuisance parameters. Estimating methods for logistic SNMMs do not suffer from this dependence. Unfortunately, in contrast with the multiplicative and additive models, unbiased estimation of the causal parameters of a logistic SNMM rely on additional modeling assumptions even when the treatment probabilities are known. These difficulties have hindered the uptake of SNMMs in epidemiological practice, where binary outcomes are common. We solve the variation dependence problem for the binary multiplicative SNMM by a reparametrization of the non-causal nuisance parameters. Our novel nuisance parameters are variation independent of the causal parameters, and hence allows the fitting of a multiplicative SNMM by unconstrained maximum likelihood. It also allows one to construct true (i.e. congenial) doubly robust estimators of the causal parameters. Along the way, we prove that an additive SNMM with binary outcomes does not admit a variation independent parametrization, thus explaining why we restrict ourselves to the multiplicative SNMM

Towards ultrasound full-waveform inversion in medical imaging

Ultrasound imaging is a front-line clinical modality with a wide range of applications. However, there are limitations to conventional methods for some medical imaging problems, including the imaging of the intact brain. The goal of this thesis is to explore and build on recent technological advances in ultrasonics and related areas such as geophysics, including the ultrasound data parallel acquisition hardware, advanced computational techniques for field modelling and for inverse problem solving. With the significant increase in the computational power now available, a particular focus will be put on exploring the potential of full-waveform inversion (FWI), a high-resolution image reconstruction technique which has shown significant success in seismic exploration, for medical imaging applications. In this thesis a range of technologies and systems have been developed in order to improve ultrasound imaging by taking advantage of these recent advances. In the first part of this thesis the application of dual frequency ultrasound for contrast enhanced imaging of neurovasculature in the mouse brain is investigated. Here we demonstrated a significant improvement in the contrast-to-tissue ratio that could be achieved by using a multi-probe, dual frequency imaging system when compared to a conventional approach using a single high frequency probe. However, without a sufficiently accurate calibration method to determine the positioning of these probes the image resolution was found to be significantly reduced. To mitigate the impact of these positioning errors, a second study was carried out to develop a sophisticated dual probe ultrasound tomography acquisition system with a robust methodology for the calibration of transducer positions. This led to a greater focus on the development of ultrasound tomography applications in medical imaging using FWI. A 2.5D brain phantom was designed that consisted of a soft tissue brain model surrounded by a hard skull mimicking material to simulate a transcranial imaging problem. This was used to demonstrate for the first time, as far as we are aware, the experimental feasibility of imaging the brain through skull using FWI. Furthermore, to address the lack of broadband sensors available for medical FWI reconstruction applications, a deep learning neural network was proposed for the bandwidth extension of observed narrowband data. A demonstration of this proposed technique was then carried out by improving the FWI image reconstruction of experimentally acquired breast phantom imaging data. Finally, the FWI imaging method was expanded for3D neuroimaging applications and an in silico feasibility of reconstructing the mouse brain with commercial transducers is demonstrated.Open Acces

Retribution, the Evolving Standard of Decency, and Methods of Execution: The Inevitable Collision in Eighth Amendment Jurisprudence

There exists a curious truce between death penalty advocates and detractors: both sides agree that lethal injection is the appropriate means of executing this country\u27s convicted murderers. Ostensibly, the reason for this agreement is that both detractors and supporters view lethal injection as the most humane means of execution. Detractors favor lethal injection because it is less painless than alternative methods, supporters because the more humane the death penalty method, the more likely the death penalty will remain constitutional. This Article will argue, however, that this alliance belies an untenable problem in Eighth Amendment jurisprudence: retribution and the evolving standard of decency have come into direct conflict with regard to methods of execution. If the focus of the Eighth Amendment is whether a particular method of execution involves the unnecessary or wanton infliction of pain, Gregg v. Georgia, 428 U.S. 153, 174 (1976), but retribution is a constitutional rationale for the imposition of the death penalty, the logical result is an intellectual quagmire. Most illustrative of this problematic reasoning is Baze v. Rees, in which Justice Stevens wrote that requiring that an execution be relatively painless . . . actually undermines the very premise on which public approval of the retribution rationale [for the death penalty] rests. 553 U.S. 35, 80 (2013). Baze sets a paradoxical standard that highlights the tension between retribution and the evolving standard of decency. One needs only look to the parade of horribles touted in cases like State v. Mata and Provenzano v. Moore to see the result of this Eighth Amendment tension in practice. What is more, the tension between these competing concepts is not merely academic: as states turn to new methods of execution in light of drug shortages, questions will be raised regarding the constitutionality of those protocols. The tension between retribution and the evolving standard of decency in method of execution jurisprudence has yet to be fully explored, but will be the future of death penalty litigation. This Article will advocate for a modified test for the application of the Eighth Amendment to methods of execution, based on the concurring opinion of Justices Thomas and Scalia in Baze. Instead of focusing on the risk of harm inherent in any mode of capital punishment, the state should be required only to refrain from causing intentional or reckless harm. This line of reasoning, although not without flaws, will at least preserve the popular sentiment in the states (either for or against the death penalty) while preventing the state from causing unnecessary harm to convicted murderers

Nested Markov Properties for Acyclic Directed Mixed Graphs

Directed acyclic graph (DAG) models may be characterized in at least four different ways: via a factorization, the d-separation criterion, the moralization criterion, and the local Markov property. As pointed out by Robins (1986, 1999), Verma and Pearl (1990), and Tian and Pearl (2002b), marginals of DAG models also imply equality constraints that are not conditional independences. The well-known `Verma constraint' is an example. Constraints of this type were used for testing edges (Shpitser et al., 2009), and an efficient marginalization scheme via variable elimination (Shpitser et al., 2011). We show that equality constraints like the `Verma constraint' can be viewed as conditional independences in kernel objects obtained from joint distributions via a fixing operation that generalizes conditioning and marginalization. We use these constraints to define, via Markov properties and a factorization, a graphical model associated with acyclic directed mixed graphs (ADMGs). We show that marginal distributions of DAG models lie in this model, prove that a characterization of these constraints given in (Tian and Pearl, 2002b) gives an alternative definition of the model, and finally show that the fixing operation we used to define the model can be used to give a particularly simple characterization of identifiable causal effects in hidden variable graphical causal models.Comment: 67 pages (not including appendix and references), 8 figure

Sparse Nested Markov models with Log-linear Parameters

Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference. Recently, a new type of graphical model, called the nested Markov model, was developed which captures equality constraints found in marginals of directed acyclic graph (DAG) models. Some of these constraints, such as the so called `Verma constraint', strictly generalize conditional independence. To make modeling and inference with nested Markov models practical, it is necessary to limit the number of parameters in the model, while still correctly capturing the constraints in the marginal of a DAG model. Placing such limits is similar in spirit to sparsity methods for undirected graphical models, and regression models. In this paper, we give a log-linear parameterization which allows sparse modeling with nested Markov models. We illustrate the advantages of this parameterization with a simulation study.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013