Field Strength and Monopoles in Dual U(1) Lattice Gauge Theory

In any Abelian gauge theory with an action periodic in the link variables one can perform a duality transformation not only in the partition function, but also in correlation functions including Polyakov loops. The calculation of expectation values in the confinement phase, like electric field strength or monopole currents in the presence of external charges, becomes significantly more efficient simulating the dual theory. We demonstrate this using the ordinary Wilson action. This approach also allows a quantitative analysis of the dual superconductor model, because the dual transformed U(1) theory can be regarded as limit of a dual non-compact Abelian Higgs model. In this way we also try to interpret the behaviour of monopole condensate and string fluctuations. Finally we present some applications for simulating the dual U(1) gauge theory.Comment: Talk presented at LATTICE96(topology) ; 3 pages, latex, 4 figures; complete postscript file also available at ftp://is1.kph.tuwien.ac.at/pub/zach/stl96.ps.g

Flux tubes and their interaction in U(1) lattice gauge theory

We investigate singly and doubly charged flux tubes in U(1) lattice gauge theory. By simulating the dually transformed path integral we are able to consider large flux tube lengths, low temperatures, and multiply charged systems without loss of numerical precision. We simulate flux tubes between static sources as well as periodically closed flux tubes, calculating flux tube profiles, the total field energy and the free energy. Our main results are that the string tension in both three and four dimensions scales proportionally to the charge -- which is in contrast to previous lattice results -- and that in four-dimensional U(1) there is an attractive interaction between flux tubes for beta approaching the phase transition.Comment: 19 pages, latex2e with tex- and eps-figures; complete postscript file also available at ftp://is1.kph.tuwien.ac.at/pub/zach/np97.ps.g

Joint Non-Linear MRI Inversion with Diffusion Priors

Magnetic resonance imaging (MRI) is a potent diagnostic tool, but suffers from long examination times. To accelerate the process, modern MRI machines typically utilize multiple coils that acquire sub-sampled data in parallel. Data-driven reconstruction approaches, in particular diffusion models, recently achieved remarkable success in reconstructing these data, but typically rely on estimating the coil sensitivities in an off-line step. This suffers from potential movement and misalignment artifacts and limits the application to Cartesian sampling trajectories. To obviate the need for off-line sensitivity estimation, we propose to jointly estimate the sensitivity maps with the image. In particular, we utilize a diffusion model -- trained on magnitude images only -- to generate high-fidelity images while imposing spatial smoothness of the sensitivity maps in the reverse diffusion. The proposed approach demonstrates consistent qualitative and quantitative performance across different sub-sampling patterns. In addition, experiments indicate a good fit of the estimated coil sensitivities

Using Digital Content to Provide Students with Virtual Experiences in an Online History of the Book Course

The History of the Book course is a traditional mainstay of library and information science (LIS) education and a perennial favorite among students valuing contact with physical artifacts. In the digital age, knowledge representation has become independent of individual objects and such classes need to reflect these changes. Working collaboratively with experts from the University of Pennsylvania Libraries’ Kislak Center for Special Collections, Rare Books, and Manuscripts, Drexel University and UPenn faculty have developed a new online version of this discussion based course offered as part of the MS(LIS) degree in the College of Computing and Informatics. This new version, augmented with video created specifically for the course and other digital materials available on the Internet, draws on traditional content but situates it in the context of knowledge representation through the ages, with a special emphasis on the role of information in the 21st century and beyond. The new online version of the course was beta tested in the winter 2014 term with 26 students; a companion section of the traditional face-to-face version of the course was also offered in the winter 2014 term. Only eleven students registered for the face-to-face version of the course, suggesting that the online format appeals to many students on the basis of convenience. The challenge for the two instructors was to keep the two sections of the course aligned in terms of the intellectual content and provide similar educational experienced for both groups of students. For example, students in the online version of the course “visited” virtual collections of rare books while students in the face-to-face section visited the physical collections held at the University of Pennsylvania. Both groups of students wrote and presented research projects on some aspect of the history of the book; presentations delivered by the online group were done through video using a variety of presentation media including Jing, iMovie, and YouTube. The wealth of digital content related to the history of the book now available from many of the major libraries and museum worldwide offers students in the online environment new opportunities for exploring the development of knowledge representation. While the digital content does not provide the same experience as the physical artifacts (e.g., the smell and feel of old manuscripts), it can often facilitate a higher level of detailed examination than would be allowed to students working with the physical artifacts. The following paper will discuss the process involved with developing, delivering, and evaluating the beta test of the online version of the course compared with the traditional version. Data from student feedback throughout the term was analyzed to identify what aspects of the new version were most/least successful, including the use of technology both for the delivery of educational content and student presentations. Recommendations for future changes/enhancement will be presented. The experiences described will be relevant not only for educators in the LIS field but also for those interested in delivering online content in the areas of museum studies, art history, archeology and any other discipline in which face-to-face classes have traditionally involved field trips and visits to view physical artifacts

Stable Deep MRI Reconstruction using Generative Priors

Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image priors. We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. After training, the regularizer encodes higher-level domain statistics which we demonstrate by synthesizing images without data. Embedding the trained model in a classical variational approach yields high-quality reconstructions irrespective of the sub-sampling pattern. In addition, the model shows stable behavior when confronted with out-of-distribution data in the form of contrast variation. Furthermore, a probabilistic interpretation provides a distribution of reconstructions and hence allows uncertainty quantification. To reconstruct parallel MRI, we propose a fast algorithm to jointly estimate the image and the sensitivity maps. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods, while preserving the flexibility with respect to sub-sampling patterns and allowing for uncertainty quantification