Principles of Liberty: A Design-based Research on Liberty as A Priori Constitutive Principle of the Social in the Swiss Nation Story

One of the still unsolved problems in liberal anarchism is a definition of social constituency in positive terms. Partially, this had been solved by the advancements of liberal discourse ethics. These approaches, built on praxeology as a universal framework for social formation, are detached from the need of any previous or external authority or rule for the discursive partners. However, the relationship between action, personal identity, and liberty within the process of a community becoming solely generated from the praxeological a priori remains largely disputed. In order to develop a testable constitutional model, this study revises how the “ontological turn” was introduced into liberal theories and redefines the concept of liberty. Liberty is usually understood as a moral goal or measurement for just actions, as the generative principle of all social existence - individual and interpersonal. For this purpose, the function of performative contradiction within the mechanism of interpersonal “encounter”, as part of the co-generative process of the individual becoming and social formation was explored through the production of a game-based narrative historiography grounded on 19th century life writings. This narrative historiography was developed in the context of Swiss history, with a “design-based” research approach, and resulted in a prototype for networked storytelling through which the transformative learning process could be visible and the negotiation of competing individual visions of the future could be re-enacted

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging