Ground state energies of quantum dots in high magnetic fields: A new approach

We present a new method for calculating ground state properties of quantum dots in high magnetic fields. It takes into account the equilibrium positions of electrons in a Wigner cluster to minimize the interaction energy in the high field limit. Assuming perfect spin alignment the many-body trial function is a single Slater determinant of overlapping oscillator functions from the lowest Landau level centered at and near the classical equilibrium positions. We obtain an analytic expression for the ground state energy and present numerical results for up to N=40.Comment: 4 pages, including 2 figures, contribution to the Proceedings of EP2DS-14, submitted to Physica

Age-related increase of kynurenic acid in human cerebrospinal fluid-IgG and beta(2)-microglobulin changes

Kynurenic acid (KYNA) is an endogenous metabolite in the kynurenine pathway of tryptophan degradation and is an antagonist at the glycine site of the N-methyl-D-aspartate as well as at the alpha 7 nicotinic cholinergic receptors. In the brain tissue KYNA is synthesised from L-kynurenine by kynurenine aminotransferases (KAT) I and II. A host of immune mediators influence tryptophan degradation. In the present study, the levels of KYNA in cerebrospinal fluid (CSF) and serum in a group of human subjects aged between 25 and 74 years were determined by using a high performance liquid chromatography method. In CSF and serum KAT I and II activities were investigated by radioenzymatic assay, and the levels of β2-microglobulin, a marker for cellular immune activation, were determined by ELISA. The correlations between neurochemical and biological parameters were evaluated. Two subject groups with significantly different ages, i.e. 50 years, p < 0.001, showed statistically significantly different CSF KYNA levels, i.e. 2.84 ± 0.16 fmol/μl vs. 4.09 ± 0.14 fmol/μl, p < 0.001, respectively; but this difference was not seen in serum samples. Interestingly, KYNA is synthesised in CSF principally by KAT I and not KAT II, however no relationship was found between enzyme activity and ageing. A positive relationship between CSF KYNA levels and age of subjects indicates a 95% probability of elevated CSF KYNA with ageing (R = 0.6639, p = 0.0001). KYNA levels significantly correlated with IgG and β2-microglobulin levels (R = 0.5244, p = 0.0049; R = 0.4253, p = 0.043, respectively). No correlation was found between other biological parameters in CSF or serum. In summary, a positive relationship between the CSF KYNA level and ageing was found, and the data would suggest age-dependent increase of kynurenine metabolism in the CNS. An enhancement of CSF IgG and β2-microglobulin levels would suggest an activation of the immune system during ageing. Increased KYNA metabolism may be involved in the hypofunction of the glutamatergic and/or nicotinic cholinergic neurotransmission in the ageing CNS

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

Calculation of mechanical and thermal influences during coiling of hot strip

Coiled steel strip is the final product from flat hot rolling processes. With increasing demand for higher quality of hot rolled strips, especially the evolution of strip flatness during and after coiling becomes a crucial aspect. The main impacts on the flatness properties of hot rolled strips result from residual stresses and “eigen-strains” induced by the last hot rolling passes, by strip cooling at the run-out table, and finally, by the mechanical and thermal conditions during and after the coiling process itself. In this paper, a mathematical model is presented, which takes into account the mechanical and thermal effects on hot rolled strip during and after the coiling process. To improve the prediction quality of the underlying process, a customized self-developed 3D finite-element model has been developed and programmed in C++, leading to a software prototype, which is highly superior to commercial FEM-packages with respect to calculation time and storage capacities. The model is based on a dynamic implicit total Lagrangian formulation. All relevant devices directly interacting with the strip, such as pinch rolls, coiler rolls and mandrel are incorporated in the calculation model. Well known and established methods in the solid-shell theory, like the EAS- and ANS-method, were applied to prevent the occurrence of locking phenomena resulting from low order interpolation functions. Selected benchmark tests were performed to evaluate the accuracy of these novel solid-shell elements in comparison to the results attained by the FEM- package ABAQUS©. The results obtained so far agree very satisfactorily. A further important topic is the contact and friction algorithm, where Coulomb’s friction law is applied. The accurate and reliable determination of the contact between strip and interacting devices as well as the aspect of self-contact was treated by applying a sophisticated two dimensional contact search algorithm, leading to a significantly reduced calculation time. The highly non-linear time-dependent system of equations is integrated by utilizing the (implicit) Newmark time-integration scheme. The developed calculation model serves as an effective tool to predict the interesting stress-distributions and local plastic deformations inside the strip, which induce residual stresses and strip unflatness (latent or even manifest waviness). Furthermore, this tool p ovides the basis for further improvements and investigations on hot rolling production lines

PialNN: A fast deep learning framework for cortical pial surface reconstruction

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by 30% compared to state-of-the-art deep learning approaches. The codes are publicly available at https://github.com/m-qiang/PialNN

Contrastive learning for view classification of echocardiograms

Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep learning networks that are trained to predict disease or detect image features. However, such models are extremely data-hungry and training requires labelling of many thousands of images by experienced clinicians. Here we propose the use of contrastive learning to mitigate the labelling bottleneck. We train view classification models for imbalanced cardiac ultrasound datasets and show improved performance for views/classes for which minimal labelled data is available. Compared to a naïve baseline model, we achieve an improvement in F1 score of up to 26% in those views while maintaining state-of-the-art performance for the views with sufficiently many labelled training observations

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks

In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the well-known GrabCut method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naive approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy