This study focuses on the innovative field of quantum synchronization for satellite-based navigation systems including Global Navigation Satellite Systems (GNSSs) and the Non-Terrestrial Network (NTN) component of future 6G networks integrating both communication and navigation services. By combining a four-qubit system with the theoretical approach of the Lindblad master equation, we transcend the inherent limits of standard synchronization techniques. This achievement represents a quantum leap in satellite-based positioning, highlighting the scalability and cost-effectiveness of our technique for smaller satellites. The study demonstrates the possibility of reducing synchronization errors to less than one meter, significantly improving the reliability and precision of satellite-based navigation systems. The results of this study may contribute to the future development of both user-centric localization systems (typically GNSS systems) and network-centric localization systems (typically through the NTN component of 6G networks), leading to better positioning performance, more flexible multi-functional systems with the potential to limit both cost and size of satellites.

3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models (SSMs) that imposed anatomical constraints and produced high quality surfaces were a core technology. Today’s fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach that marries the representation power of deep networks with the benefits of SSMs. DISSMs use an implicit representation to produce compact and descriptive deep surface embeddings that permit statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process (MDP). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than four leading FCN models, including nnU-Net + an adversarial prior: reducing the mean Hausdorff distance (HD) by 7.5-14.3 mm and improving the worst case Dice-Sørensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on an external and highly challenging clinical dataset demonstrate that DISSMs improve the mean DSC and HD by 2.1-5.9% and 9.9-24.5 mm, respectively, and the worst-case DSC by 5.4-7.3%. Supplemental validation on a highly challenging and low-contrast larynx dataset further demonstrate DISSM’s improvements. These improvements are over and above any benefits from representing delineations with high-quality surfaces.

The microservice architecture has dramatically reduced user effort in adopting and maintaining servers by providing a catalog of functions as services that can be used as building blocks to construct applications. This has enabled datacenter operators to look at managing datacenter hosting microservices quite differently from traditional infrastructures. Such a paradigm shift calls for a need to rethink resource management strategies employed in such execution environments. We observe that the visibility enabled by a microservices execution framework can be exploited to achieve high throughput and resource utilization while still meeting Service Level Agreements, especially in multi-tenant execution scenarios.