15 Fakultätsübergreifend / Sonstige Einrichtung
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/16
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Item Open Access Umgang mit Forschungssoftware an der Universität Stuttgart(2020) Flemisch, Bernd; Hermann, Sibylle; Holm, Christian; Mehl, Miriam; Reina, Guido; Uekermann, Benjamin; Boehringer, David; Ertl, Thomas; Grad, Jean-Noël; Iglezakis, Dorothea; Jaust, Alexander; Koch, Timo; Seeland, Anett; Weeber, Rudolf; Weik, Florian; Weishaupt, KilianWir empfehlen die Einrichtung einer Organisationseinheit Forschungssoftware-Entwicklung an der Universität Stuttgart und eines daran angegliederten Stellenpools von Research Software Engineers (RSEs). Dazu schlagen wir Maßnahmen zur Schaffung und Finanzierung entsprechender neuer RSE-Stellen, zur Integration bestehender Stellen sowie zur Gewinnung und Förderung geeigneter Personen vor. RSEs sind Personen, die sich um Konzeption, Organisation, Implementierung, Testen, Dokumentation und Wartung von Forschungssoftware kümmern. Die institutionelle Förderung von Forschungssoftware-Entwicklung ist notwendig, da die Bedeutung von Software für die Forschung und Anforderungen an die entsprechende Software, u.a. durch die DFG, stetig zunimmt.Item Open Access Body dysmorphic disorder and self-esteem : a meta-analysis(2021) Kuck, Nora; Cafitz, Lara; Bürkner, Paul-Christian; Hoppen, Laura; Wilhelm, Sabine; Buhlmann, UlrikeBody dysmorphic disorder (BDD) is associated with low self-esteem. The aim of this meta-analysis was to examine the strength of the cross-sectional relationship between BDD symptom severity and global self-esteem in individuals with BDD, mentally healthy controls, community or student samples, and cosmetic surgery patients. Moreover, the role of depressive symptom severity in this relationship and other moderating factors were investigated. A keyword-based literature search was performed to identify studies in which BDD symptoms and global self-esteem were assessed. Random effects meta-analysis of Fisher’s z-transformed correlations and partial correlations controlling for the influence of depressive symptom severity was conducted. In addition to meta-analysis of the observed effects, we corrected the individual correlations for variance restrictions to address varying ranges of BDD symptom severity across samples. Twenty-five studies with a total of 6278 participants were included. A moderately negative relationship between BDD symptom severity and global self-esteem was found (r = -.42, CI = [-.48, -.35] for uncorrected correlations, r = -.45, CI = [-.51, -.39] for artifact-corrected correlations). A meta-analysis of partial correlations revealed that depressive symptom severity could partly account for the aforementioned relationship (pr = -.20, CI = [-.25, -.15] for uncorrected partial correlations, pr = -.23, CI = [-.28, -.17] for artifact-corrected partial correlations). The sample type (e.g., individuals with BDD, mentally healthy controls, or community samples) and diagnosis of BDD appeared to moderate the relationship only before artifact correction of effect sizes, whereas all moderators were non-significant in the meta-analysis of artifact-corrected correlations. The findings demonstrate that low self-esteem is an important hallmark of BDD beyond the influence of depressive symptoms. It appears that negative evaluation in BDD is not limited to appearance but also extends to other domains of the self. Altogether, our findings emphasize the importance of addressing self-esteem and corresponding core beliefs in prevention and treatment of BDD.Item Open Access Simulating stochastic processes with variational quantum circuits(2022) Fink, DanielSimulating future outcomes based on past observations is a key task in predictive modeling and has found application in many areas ranging from neuroscience to the modeling of financial markets. The classical provably optimal models for stationary stochastic processes are so-called ϵ-machines, which have the structure of a unifilar hidden Markov model and offer a minimal set of internal states. However, these models are not optimal in the quantum setting, i.e., when the models have access to quantum devices. The methods proposed so far for quantum predictive models rely either on the knowledge of an ϵ-machine, or on learning a classical representation thereof, which is memory inefficient since it requires exponentially many resources in the Markov order. Meanwhile, variational quantum algorithms (VQAs) are a promising approach for using near-term quantum devices to tackle problems arising from many different areas in science and technology. Within this work, we propose a VQA for learning quantum predictive models directly from data on a quantum computer. The learning algorithm is inspired by recent developments in the area of implicit generative modeling, where a kernel-based two-sample-test, called maximum mean discrepancy (MMD), is used as a cost function. A major challenge of learning predictive models is to ensure that arbitrarily many time steps can be simulated accurately. For this purpose, we propose a quantum post-processing step that yields a regularization term for the cost function and penalizes models with a large set of internal states. As a proof of concept, we apply the algorithm to a stationary stochastic process and show that the regularization leads to a small set of internal states and a constantly good simulation performance over multiple future time steps, measured in the Kullback-Leibler divergence and the total variation distance.