Agency, Delayed Compensation, and the Structure of Executive Remuneration

In this paper we examine the factors affecting the structure of executives' compensation packages. We focus particularly on the role of various types of delayed compensation as means of "bonding" executives to their firms. The basic problem is to design a compensation package that rewards actions that are in the long-run interest of the stockholders. Firms must take into account (1) their ability to discern unfortunate circumstances from mismanagement; (2) the extent to which a compensation package forces the executive to face risks, beyond his control; and (3) the willingness of a given executive to bear this risk. We use our theory to interpret some executive compensation data from the early 1970's. The results are generally in line with the theoretical predictions.

Using compartment models of diffusion MRI to investigate the preterm brain

Preterm birth is the leading cause of neonatal mortality, with survivors experiencing motor, cognitive and other deficits at increased rates. In preterm infancy, the developing brain undergoes folding, myelination, and rapid cellular growth. Diffusion-Weighted Magnetic Resonance Imaging (DW MRI) is an imaging modality that allows noninvasive inference of cellular microstructure in living tissue, and its parameters reflect changes in brain tissue composition. In this thesis, we employ compartment models of DW MRI to investigate the microstructure in preterm-born subjects at different ages. Within infants, we have used the NODDI model to investigate longitudinal changes in neurite density and orientation dispersion within the white matter, cerebral cortex and thalamus, explaining known trends in diffusion tensor parameters with greater specificity. We then used a quantitative T2 sequence to develop and investigate a novel, multi-modal parameter known as the ‘g-ratio’. We have also investigated changing microstructural geometry within the cortex. Immediately after preterm birth, the highly-ordered underlying cellular structure makes diffusion in the cortex almost entirely radial. This undergoes a transition to a disordered and isotropic state over the first weeks of life, which we have used the DIAMOND model to quantify. This radiality decreases at a rate that depends on the cortical lobe. In a cohort of young adults born extremely preterm, we have quantified differences in brain microstructure compared to term-born controls. In preterm subjects, the brain structures are smaller than for controls, leading to increased partial volume in some regions of interest. We introduce a method to infer diffusion parameters in partial volume, even for regions which are smaller than the diffusion resolution. Overall, this thesis utilises and evaluates a variety of compartment models of DW MRI. By developing and applying principled and robust methodology, we present new insights into microstructure within the preterm-born brain

Use of Rapid-Scan EPR to Improve Detection Sensitivity for Spin-Trapped Radicals

AbstractThe short lifetime of superoxide and the low rates of formation expected in vivo make detection by standard continuous wave (CW) electron paramagnetic resonance (EPR) challenging. The new rapid-scan EPR method offers improved sensitivity for these types of samples. In rapid-scan EPR, the magnetic field is scanned through resonance in a time that is short relative to electron spin relaxation times, and data are processed to obtain the absorption spectrum. To validate the application of rapid-scan EPR to spin trapping, superoxide was generated by the reaction of xanthine oxidase and hypoxanthine with rates of 0.1–6.0 μM/min and trapped with 5-tert-butoxycarbonyl-5-methyl-1-pyrroline-N-oxide (BMPO). Spin trapping with BMPO to form the BMPO-OOH adduct converts the very short-lived superoxide radical into a more stable spin adduct. There is good agreement between the hyperfine splitting parameters obtained for BMPO-OOH by CW and rapid-scan EPR. For the same signal acquisition time, the signal/noise ratio is >40 times higher for rapid-scan than for CW EPR. Rapid-scan EPR can detect superoxide produced by Enterococcus faecalis at rates that are too low for detection by CW EPR

Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI.

Preterm birth is a major public health concern, with the severity and occurrence of adverse outcome increasing with earlier delivery. Being born preterm disrupts a time of rapid brain development: in addition to volumetric growth, the cortex folds, myelination is occurring and there are changes on the cellular level. These neurological events have been imaged non-invasively using diffusion-weighted (DW) MRI. In this population, there has been a focus on examining diffusion in the white matter, but the grey matter is also critically important for neurological health. We acquired multi-shell high-resolution diffusion data on 12 infants born at ≤28weeks of gestational age at two time-points: once when stable after birth, and again at term-equivalent age. We used the Neurite Orientation Dispersion and Density Imaging model (NODDI) (Zhang et al., 2012) to analyse the changes in the cerebral cortex and the thalamus, both grey matter regions. We showed region-dependent changes in NODDI parameters over the preterm period, highlighting underlying changes specific to the microstructure. This work is the first time that NODDI parameters have been evaluated in both the cortical and the thalamic grey matter as a function of age in preterm infants, offering a unique insight into neuro-development in this at-risk population

Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning

Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: 1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and 2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network representation. We test our model on prostate cancer scans and show that it produces more accurate and consistent synCTs with a better estimation in the variance of the errors, state of the art results in OAR segmentation and a methodology for quality assurance in radiotherapy treatment planning.Comment: Early-accept at MICCAI 2018, 8 pages, 4 figure

Notes on Optimal Wage Taxation and Uncertainty

Most contributions to optimal tax theory have assumed that all prices, including that of leisure, are known with certainty. The purpose of this paper is to analyze optimal taxation when workers have imperfect information about their wages at the time they choose their labor supplies. Both efficiency and redistributive aspects of the problem are considered. The paper begins with a discussion of the positive theory of wage taxation and labor supply under uncertainty. This is followed by a discussion of optimal taxation when individuals are identical, but their wages are stochastic. Finally, the case of simultaneous uncertainty and inequality is discussed. In this part of the paper it is assumed that the government's objective is to maximize a utilitarian social welfare function.