A Goldstone Theorem in Thermal Relativistic Quantum Field Theory

We prove a Goldstone Theorem in thermal relativistic quantum field theory, which relates spontaneous symmetry breaking to the rate of space-like decay of the two-point function. The critical rate of fall-off coincides with that of the massless free scalar field theory. Related results and open problems are briefly discussed

Position paper: Runtime Model for Role-based Software Systems

In the increasingly dynamic realities of today's software systems, it is no longer feasible to always expect human developers to react to changing environments and changing conditions immediately. Instead, software systems need to be self-aware and autonomously adapt their behavior according to their experiences gathered from their environment. Current research provides role-based modeling as a promising approach to handle the adaptivity and self-awareness within a software system. There are established role-based systems e.g., for application development, persistence, and so on. However, these are isolated approaches using the role-based model on their specific layer and mapping to existing non-role-based layers. We present a global runtime model covering the whole stack of a software system to maintain a global view of the current system state and model the interdependencies between the layers. This facilitates building holistic role-based software systems using the role concept on every single layer to exploit its full potential, particularly adaptivity and self-awareness

Multivariate error modeling and uncertainty quantification using importance (re-)weighting for Monte Carlo simulations in particle transport

Fast and accurate predictions of uncertainties in the computed dose are crucial for the determination of robust treatment plans in radiation therapy. This requires the solution of particle transport problems with uncertain parameters or initial conditions. Monte Carlo methods are often used to solve transport problems especially for applications which require high accuracy. In these cases, common non-intrusive solution strategies that involve repeated simulations of the problem at different points in the parameter space quickly become infeasible due to their long run-times. Intrusive methods however limit the usability in combination with proprietary simulation engines. In our previous paper [51], we demonstrated the application of a new non-intrusive uncertainty quantification approach for Monte Carlo simulations in proton dose calculations with normally distributed errors on realistic patient data. In this paper, we introduce a generalized formulation and focus on a more in-depth theoretical analysis of this method concerning bias, error and convergence of the estimates. The multivariate input model of the proposed approach further supports almost arbitrary error correlation models. We demonstrate how this framework can be used to model and efficiently quantify complex auto-correlated and time-dependent errors.Comment: 26 pages, 10 figures, [v2]: corrected title of figure

Fluorescent nuclear track detectors as a tool for ion-beam therapy research

Originally designed for optical storage, fluorescent nuclear track detectors (FNTD) based on single aluminum oxide crystals contain aggregate color centers that show permanent radiochromic transformation when bombarded with ionizing radiation. Transformed centers produce high-yield fluorescence at 750 nm when stimulated at 620 nm. This enables non-destructive readout using confocal laser scanning microscopes (CLSMs). Since the intensity signal depends on the local energy deposition, 3D particle trajectories through the crystal can be assessed. Together with the excellent sensitivity of FNTDs, this enables derivation of information on track location, direction, energy loss, etc. over the entire particle and energy range found in ion beam therapy. Effects such as projectile fragmentation and secondary electron trajectories can be studied in detail with diffraction-limited resolution. Due to their biocompatibility, autoclavability and since post-irradiation chemical processing is not needed, FNTDs can show significant superiority to existing technologies such as plastic nuclear track detectors (e.g. CR-39). The Heavy Ion Therapy Research Group at the German Cancer Research Center studies FNTD technology for application on three main fields: (a) Fundamental dosimetry quantities (w-value, I-value) in ion beams: FNTDs allow for determination of particle fluence and range with very high accuracy. (b) In vivo track-based assessment of dose to organs at risk during therapy: FNTDs represent one of a few systems that enable biological dose estimation which is the essential predictor for clinical outcome in ion beam therapy. In addition, FNTDs are small, resilient, wireless and biocompatible and can, therefore, be used within phantoms, animal models or even patients. (c) Radiobiology: Our group was the first to use FNTDs as substrate for cell ("Cell-Fit-HD"). This enables to correlate microscopic physical parameters and subcellular/cell response both in fixed and living cell and study cellular processes fundamental to ion beam radiotherapy that are hitherto little understood. The talk will present the basic principle of FNTD technology, our group’s technical implementation as well as the latest methodological developments and application results

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems.Comment: 6 pages, 5 figures, accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japa

A Novel Combining Receiver for a Dual-Diversity Wireless Relay Network

We present a simple combining receiver for a dual-diversity wireless relay network. The main concern of the paper is to face the trade-off between performance and complexity. The receiver focuses on signal-to-noise ratio (SNR) monitoring and selects dynamically between selection combining (SC) and equal gain combining (EGC) depending on the SNR ratio of the two received branches. It is shown that SC suffers no SNR degradation compared to a single branch communications system if the two receive branches are unbalanced, wheres EGC suffers a loss of 3 dB. Error performance with respect to branch unbalance is considered as well and limiting values for a high degree of branch unbalance are derived