Coherent two-dimensional Fourier transform spectroscopy using a 25 Tesla resistive magnet.

We performed nonlinear optical two-dimensional Fourier transform spectroscopy measurements using an optical resistive high-field magnet on GaAs quantum wells. Magnetic fields up to 25 T can be achieved using the split helix resistive magnet. Two-dimensional spectroscopy measurements based on the coherent four-wave mixing signal require phase stability. Therefore, these measurements are difficult to perform in environments prone to mechanical vibrations. Large resistive magnets use extensive quantities of cooling water, which causes mechanical vibrations, making two-dimensional Fourier transform spectroscopy very challenging. Here, we report on the strategies we used to overcome these challenges and maintain the required phase-stability throughout the measurement. A self-contained portable platform was used to set up the experiments within the time frame provided by a user facility. Furthermore, this platform was floated above the optical table in order to isolate it from vibrations originating from the resistive magnet. Finally, we present two-dimensional Fourier transform spectra obtained from GaAs quantum wells at magnetic fields up to 25 T and demonstrate the utility of this technique in providing important details, which are obscured in one dimensional spectroscopy

A Simple Parallel Implementation of Interaction Nets in Haskell

Due to their "inherent parallelism", interaction nets have since their introduction been considered as an attractive implementation mechanism for functional programming. We show that a simple highly-concurrent implementation in Haskell can achieve promising speed-ups on multiple cores

Activities of the Remote Sensing Information Sciences Research Group

Topics on the analysis and processing of remotely sensed data in the areas of vegetation analysis and modelling, georeferenced information systems, machine assisted information extraction from image data, and artificial intelligence are investigated. Discussions on support field data and specific applications of the proposed technologies are also included

Synthesis-Based Harmonization of Multi-Contrast Structural MRI

Magnetic resonance imaging (MRI) is a flexible, noninvasive medical imaging modality that uses magnetic fields and radiofrequency (RF) pulses to produce images. MRI is especially useful in diagnosing and monitoring disorders of the central nervous system such as multiple sclerosis (MS). The flexible design of the MRI system allows for the collection of multiple images with different acquisition parameters in a single scanning session. This flexibility also poses challenges when pooling data collected on multiple scanners or at different sites. Since MRI does not have consistent standards that regulate image acquisition, differences in acquisition lead to variability in images that can cause problems in analysis. This problem sets the stage for harmonization. This dissertation describes developments in harmonization strategies for structural MRI of the brain. These strategies allow us to create similar harmonized images from varying source images. Harmonized images can then be used in automated analysis pipelines where image variability can cause inconsistent results. In this work, we make a number of contributions to research in harmonization of MRI. In our first contribution, we acquired a cross-domain dataset to provide training and validation data for our harmonization methods. These data were acquired on two different scanners with different acquisition protocols in a short time frame, providing examples of the same subjects under two different acquisition environments. Since the imaged object is the same between the two, this can be used as training and validation data in harmonization experiments. In our second contribution, we used this dataset to develop a supervised method of harmonization, called DeepHarmony, which uses state-of-the-art deep learning architecture and training strategies to provide improved image harmonization. This method provides a baseline for image harmonization, but the requirement for cross-domain training data is a major limitation. In our third contribution, we proposed an unsupervised harmonization framework. We used multi-contrast MRI images from the same scanning session to encourage a disentangled latent representation and we demonstrated that this regularization was able to generate disentanglement and allow for harmonization. In our final contribution, we extended our unsupervised work for a more diverse clinical trial dataset, which included T2-FLAIR and PD-weighted images. In this more complex dataset, we included a new adversarial loss to encourage consistency in the anatomy space and a distribution loss to impose a well-defined distribution on the acquisition space

Implementing Interaction Nets In Monstr

Two superficially similar graph rewriting formalisms, Interaction Nets and MONSTR, are studied. Interaction Nets come from multiplicative Linear Logic and feature undirected graph edges, while MONSTR arose from the desire to implement generalised Term Graph Rewriting efficiently on a distributed architecture and utilises directed graph arcs. Both formalisms feature rules with small left hand sides consisting of two main graph nodes. A translation of Interaction Nets into MONSTR is described, thus providing an implementation route for the former based on the latter and particularly suited to distributed implementations. Keywords: Term Graph Rewriting Systems; MONSTR; Interaction Nets; Distributed Systems. INTRODUCTION There are many different kinds of graph that have been studied over the years, and inevitably, people have invented a rather large number of ways of rewriting them, yielding a vast number of different models of computation. In this paper we study the relationship between t..