Energy and decay width of the pi-K atom

The energy and decay width of the pi-K atom are evaluated in the framework of the quasipotential-constraint theory approach. The main electromagnetic and isospin symmetry breaking corrections to the lowest-order formulas for the energy shift from the Coulomb binding energy and for the decay width are calculated. They are estimated to be of the order of a few per cent. We display formulas to extract the strong interaction S-wave pi-K scattering lengths from future experimental data concerning the pi-K atom.Comment: 37 pages, 5 figures, uses Axodra

What Might Have Been Lost

This article examines the role of “independent” folk music (indie-folk) in personal identity formation. It builds upon Paul Ricoeur’s theory of narrative identity, which argues (i) that it is through the mechanism of narrative that people build a more or less coherent life-story, and (ii) emphasizes the role of art (most notably literary fiction and poetry) as a mediator in the comprehension and regulation of transitory life experiences. This article aims to apply these insights to studying the role of indie-folk, a narrative art form adhering to the traditional understanding of folk music as a genre rooted in oral tradition, in the construction of personal identity. Studying the daily use of indie-folk songs by audience members through in-depth interviewing, it shows that (i) the reception of indie-folk music results in ritualistic listening behavior aimed at coping with the experience of accelerating social time; (ii) that respondents use indie-folk narratives as resources for reading the self, and (iii) that indie-folk songs provide healing images that are effective in coping with the experience of narrated time as discordant. In arguing for the central role of narrative in identity formation, this article aims to contribute to existing research on music as a “technology of the self” (DeNora). It specifically emphasizes how narrative particles are tools and building blocks in identity construction, a process characterized by the oscillation between narrative coherence and disruption

PHOTONAI—A Python API for rapid machine learning model development

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com