nPI Resummation in 3D SU(N) Higgs Theory

We test the utility of the nPI formalism for solving nonperturbative dynamics of gauge theories by applying it to study the phase diagram of SU(N) Higgs theory in 3 Euclidean spacetime dimensions. Solutions reveal standard signatures of a first order phase transition with a critical endpoint leading to a crossover regime, in qualitative agreement with lattice studies. The location of the critical endpoint, x sim 0.14 for SU(2) with a fundamental Higgs, is in rough but not tight quantitative agreement with the lattice. We end by commenting on the overall effectiveness and limitations of an nPI effective action based study. In particular, we have been unable to find an nPI gauge-fixing procedure which can simultaneously display the right phase structure and correctly handle the large-VEV Higgs region. We explain why doing so appears to be a serious challenge.Comment: 24 pages plus appendices, 8 figure

UV Cascade in Classical Yang-Mills via Kinetic Theory

We show that classical Yang-Mills theory with statistically homogeneous and isotropic initial conditions has a kinetic description and approaches a scaling solution at late times. We find the scaling solution by explicitly solving the Boltzmann equations, including all dominant processes (elastic and number-changing). Above a scale $p_{max} \propto t^{1/7}$ the occupancy falls exponentially in $p$. For asymptotically late times and sufficiently small momenta the occupancy scales as $f(p)\propto 1/p$, but this behavior sets in only at very late time scales. We find quantitative agreement of our results with lattice simulations, for times and momenta within the range of validity of kinetic theory.Comment: 18 pages, 4 figure

Allograft Structural Interbody Spacers Compared to PEEK Cages in Cervical Fusion: Benchtop and Clinical Evidence

Cervical degenerative disc disease (CDDD) can lead to radiculopathy and myelopathy, resulting in pain, lack of function, and immobility. Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment modality for advanced CDDD. ACDF involves removal of the affected disc(s) followed by replacement with a bone or synthetic graft. Historically, autograft has been considered the gold standard for interbody fusion. However, it is often associated with limitations, including donor site morbidity and limited quality and supply, prompting surgeons to seek alternatives. Two of the most common alternatives are structural bone allografts and polyetheretherketone (PEEK) synthetic cages. Both, advantageously, have similar mechanical properties to autologous bone, with comparable elastic modulus values. However, a lack of osseointegration of PEEK cages has been reported both pre-clinically and clinically. Reported fusion rates assessed radiographically are higher with the use of structural bone allografts compared to PEEK cages, while having a lower incidence of pseudarthrosis. This book chapter will discuss in detail the pre-clinical and clinical performance of structural allografts in comparison to conventional PEEK cages

Study of multiband disordered systems using the typical medium dynamical cluster approximation

We generalize the typical medium dynamical cluster approximation to multiband disordered systems. Using our extended formalism, we perform a systematic study of the non-local correlation effects induced by disorder on the density of states and the mobility edge of the three-dimensional two-band Anderson model. We include inter-band and intra-band hopping and an intra-band disorder potential. Our results are consistent with the ones obtained by the transfer matrix and the kernel polynomial methods. We apply the method to K$_x$Fe$_{2-y}$Se$_2$ with Fe vacancies. Despite the strong vacancy disorder and anisotropy, we find the material is not an Anderson insulator. Our results demonstrate the application of the typical medium dynamical cluster approximation method to study Anderson localization in real materials.Comment: 10 pages, 8 figure

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.Comment: Accepted to MICCAI 201

Significance of trends toward earlier snowmelt runoff, Columbia and Missouri Basin headwaters, western United States

We assess changes in runoff timing over the last 55 years at 21 gages unaffected by human influences, in the headwaters of the Columbia-Missouri Rivers. Linear regression models and tests for significance that control for ‘‘false discoveries’’ of many tests, combined with a conceptual runoff response model, were used to examine the detailed structure of spring runoff timing. We conclude that only about one third of the gages exhibit significant trends with time but over half of the gages tested show significant relationships with discharge. Therefore, runoff timing is more significantly correlated with annual discharge than with time. This result differs from previous studies of runoff in the western USA that equate linear time trends to a response to global warming. Our results imply that predicting future snowmelt runoff in the northern Rockies will require linking climate mechanisms controlling precipitation, rather than projecting response to simple linear increases in temperature

Adversarial Deformation Regularization for Training Image Registration Neural Networks

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.Comment: Accepted to MICCAI 201