An overview of Mirjam and WeaveC

In this chapter, we elaborate on the design of an industrial-strength aspectoriented programming language and weaver for large-scale software development. First, we present an analysis on the requirements of a general purpose aspect-oriented language that can handle crosscutting concerns in ASML software. We also outline a strategy on working with aspects in large-scale software development processes. In our design, we both re-use existing aspect-oriented language abstractions and propose new ones to address the issues that we identified in our analysis. The quality of the code ensured by the realized language and weaver has a positive impact both on maintenance effort and lead-time in the first line software development process. As evidence, we present a short evaluation of the language and weaver as applied today in the software development process of ASML

Lensing reconstruction from line intensity maps: the impact of gravitational nonlinearity

We investigate the detection prospects for gravitational lensing of three-dimensional maps from upcoming line intensity surveys, focusing in particular on the impact of gravitational nonlinearities on standard quadratic lensing estimators. Using perturbation theory, we show that these nonlinearities can provide a significant contaminant to lensing reconstruction, even for observations at reionization-era redshifts. However, we show how this contamination can be mitigated with the use of a "bias-hardened" estimator. Along the way, we present an estimator for reconstructing long-wavelength density modes, in the spirit of the "tidal reconstruction" technique that has been proposed elsewhere, and discuss the dominant biases on this estimator. After applying bias-hardening, we find that a detection of the lensing potential power spectrum will still be challenging for the first phase of SKA-Low, CHIME, and HIRAX, with gravitational nonlinearities decreasing the signal to noise by a factor of a few compared to forecasts that ignore these effects. On the other hand, cross-correlations between lensing and galaxy clustering or cosmic shear from a large photometric survey look promising, provided that systematics can be sufficiently controlled. We reach similar conclusions for a single-dish survey inspired by CII measurements planned for CCAT-prime, suggesting that lensing is an interesting science target not just for 21cm surveys, but also for intensity maps of other lines.Comment: 40+18 pages, 13 figures, 5 tables. v2: JCAP published version, with typos fixed and clarifications adde

Multimodal Image Analysis for Carotid Artery Plaque Characterization

Atherosclerosis of the carotid artery is a main cause of ischemic cerebrovascular events. There is evidence that the composition of the vessel wall is more strongly related to plaque vulnerability and subsequent events than luminal stenosis, which is currently used for risk stratification in clinical practice. Noninvasive imaging can characterize the composition of the vessel wall. In order to incorporate measures of plaque composition into clinical practice, accurate and robust image segmentation methods are required. This thesis describes the development and validation of image analysis techniques that aim at the automated characterization of the carotid atherosclerotic vessel wall. The first part of this thesis makes use of a dataset in which ex vivo and in vivo MRI and CT, and annotated histology sections are available and have been spatially aligned. We firstly perform segmentation of plaque components in ex vivo MRI. Voxel classifiers are trained on a ground truth of registered histology and μCT images. We show the importance of different groups of features: intensities, Gaussian filters and wall distances, and use these features in subsequent work on in vivo data. Here we address the problems that arise in training and evaluation of segmentation methods when misregistration between histology and in vivo