arXiv subjects
Publications and source records attributed to Alison Gray.
We study continuous-time quantum walks (CTQWs) on cycles. In particular, we prove that the cycle $C_9$ does not admit uniform mixing at any time via algebraic geometry and Gr\"obner basis techniques to rule out all cyclic 9-roots.
A software system named AutoMRISimQA was developed to monitor the daily performance of a wide-bore 3T scanner(MRI) which was designed and dedicated to radiotherapy simulation. The system can monitor the performance of the MRI simulator not only by using image quality indices such as signal-to-noise ratio (SNR), uniformity, ghosting and contrast but also performing a quick check of geometry accuracy as well as the external lasers quantitatively. It was implemented into the daily clinically workflow in 2013 and has been used for more than 10 years. It was also seamlessly integrated with QAtrack, allowing continuous monitoring of the consistency of the MRI simulator's performance.
The integral quality monitor (IQM) system compares the signal measured with a large volume chamber mounted to the linear accelerator's head to the signal calculated using the patient DICOM RT plan for patient-specific quality assurance (PSQA). A method was developed to reconstruct the dose in patients using the signal measured by IQM chamber and patient planning CT images. A software tool named IQMDose3D was implemented to automate this procedure and integrated into the IQM-based PSQA workflow. IQMDose3D enables the physicists to evaluate PSQA by focusing on the clinical perspective by comparing the delivered plan to the approved clinical plan in terms of the clinical goals, dose-volume histogram (DVH) in addition to the three-dimensional (3D) gamma map and gamma pass rate.
The world ocean plays a key role in redistributing heat in the climate system and hence in regulating Earth's climate. Yet statistical analysis of ocean heat transport suffers from partially incomplete large-scale data intertwined with complex spatio-temporal dynamics, as well as from potential model misspecification. We present a comprehensive spatio-temporal statistical framework tailored to interpolating the global ocean heat transport using in-situ Argo profiling float measurements. We formalize the statistical challenges using latent local Gaussian process regression accompanied by a two-stage fitting procedure. We introduce an approximate Expectation-Maximization algorithm to jointly estimate both the mean field and the covariance parameters, and refine the potentially under-specified mean field model with a debiasing procedure. This approach provides data-driven global ocean heat transport fields that vary in both space and time and can provide insights into crucial dynamical phenomena, such as El Ni{\~n}o \& La Ni{\~n}a, as well as the global climatological mean heat transport field, which by itself is of scientific interest. The proposed framework and the Argo-based estimates are thoroughly validated with state-of-the-art multimission satellite products and shown to yield realistic subsurface ocean heat transport estimates.