This article holds that governmental investments in quantum technologies speak to the imaginable futures of digital sovereignty and digital infrastructures, two major areas of change driven by related technologies like AI and Big Data, among other things, in international law today. Under intense development today for future interpolation into digital systems that they may alter, quantum technologies occupy a sort of liminal position, rooted in existing assemblages of computational technologies while pointing to new horizons for them. The possibilities they raise (...) are neither certain nor determinate, but active investments in them (legal, political and material investments) offer perspective on digital technology-driven influences on an international legal imagination. In contributing to visions of the future that are guiding ambitions for digital sovereignty and digital infrastructures, quantum technologies condition digital technology-driven changes to international law and legal imagination in the present. Privileging observation and description, I adapt and utilize a diffractive method with the aim to discern what emerges out of the interference among the several related things assembled for this article, including material technologies and legal institutions. In conclusion, I observe ambivalent changes to an international legal imagination, changes which promise transformation but appear nonetheless to reproduce current distributions of power and resources. (shrink)

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model’s potential to assist (...) a clinician in deciding whether to treat a given patient in the hospital or at home. We examined the error rates of the models when predicting that a given fraction of patients will survive. We examined survival fractions between 0.1 and 0.6. Over this range, each model’s predictive error rate was within 1% of the error rate of every other model. When predicting that approximately 30°K of the patients will survive, all the models have an error rate of less than 1.5%. The models are distinguished more by the number of variables and parameters that they contain than by their error rates; these differences suggest which models may be the most amenable to future implementation as paper-based guidelines. (shrink)