Titlebook: Verschlüsselung und Innere Sicherheit; Die verfassungsrecht Christian Meyn Book 2003 Deutscher Universitäts-Verlag/GWV Fachverlage GmbH, Wi

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Kommunikationsfreiheit, Art. 5I GG,überhaupt.“. Das Kardinalgrundrecht des Art. 5I1 GG schützt die Freiheit der Kommunikation in vielerlei Gestalt. Es läßt sich zeigen, daß Art. 5I1 GG auch die Freiheit schützt, Nachrichten auf elektronischem Weg verschlüsselt zu übertragen..

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2512-6997 sselung seine Daten vor der Kenntnisnahme durch Dritte schützen - auch Kriminelle und Terroristen. Somit laufen die legalen Abhörbefugnisse von Behörden bei verschlüsselter Kommunikation im Internet ins Leere. Wie lässt sich das Bedürfnis nach Sicherheit vor Kriminalität und Terrorismus mit der verl

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dies in this field mainly focus on “network engineering” such as designing new components and objective functions. This work takes a data-centric perspective and investigates multiple critical aspects in “data engineering”, which we believe would complement the current practice. To facilitate a comp

dilute

Christian Meyntonomous navigation and remote sensing. As such, building computer vision and deep learning systems for depth and infrared data are crucial. However, large labeled datasets for these modalities are still lacking. In such cases, transferring knowledge from a neural network trained on a well-labeled l

进取心

Christian Meynsets. However, current 3D semantic segmentation benchmarks contain only a small number of categories – less than 30 for ScanNet and SemanticKITTI, for instance, which are not enough to reflect the diversity of real environments (e.g., semantic image understanding covers hundreds to thousands of clas

Cardiac

Christian Meynfor hypergraph learning extend graph neural networks based on message passing, which is simple yet fundamentally limited in modeling long-range dependencies and expressive power. On the other hand, tensor-based equivariant neural networks enjoy maximal expressiveness, but their application has been

chapel

Christian Meyns suffer from low performance for both 3D shape and 6D pose and size estimation in complex multi-object scenarios with occlusions. We present ShAPO, a method for joint multi-object detection, 3D textured reconstruction, 6D object pose and size estimation. Key to ShAPO is a single-shot pipeline to re

CT-angiography

Christian Meynoriously expensive. One of the most effective solutions is to transfer the knowledge from existing labeled source data to unlabeled target data. However, domain bias typically hinders knowledge transfer and leads to accuracy degradation. In this paper, we propose a Masked Local Structure Prediction