University of Passau

We consider adaptive increasingly rare Markov chain Monte Carlo (MCMC) algorithms, which are adaptive MCMC methods, where the adaptation concerning the “past” happens less and less frequently over time. Under a contraction assumption with respect to a Wasserstein-like function we deduce upper bounds of the convergence rate of Monte Carlo sums taking a renormalisation factor into account that is “almost” the one that appears in a law of the iterated logarithm. We demonstrate the applicability of our results by considering different settings, among which are those of simultaneous geometric and uniform ergodicity. All proofs are carried out on an augmented state space, including the classical non-augmented setting as a special case. In contrast to other adaptive MCMC limit theory, some technical assumptions, like diminishing adaptation, are not needed.

With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in the scholarship, identify areas of debate and unresolved challenges, and outline open questions and future directions for research and educational practice that emerged from structured small-group activities designed to bridge disciplinary barriers.

The Arbeitsbezogenes Verhaltens- und Erlebensmuster (AVEM) is a preventive diagnostic tool designed to assess typical work-related patterns of perceiving and coping with occupational stress. It aims to identify individuals at risk of burnout and other occupational health problems at an early stage. The present study examined cross-country differences in pattern distribution among teacher education students in Central Europe and tested the replicability of the work-related patterns in an international sample. The sample consisted of 3,005 first-year students enrolled in full-time teacher education programs at higher education institutions in Austria, Czechia, Germany, Poland, and Slovakia. The findings revealed that 36.97% of Austrian, 39.16% of German, 59.63% of Polish, 62.18% of Slovak, and 62.85% of Czech students were classified into risk patterns, indicating heightened vulnerability to occupational health problems. Latent profile analysis confirmed the original four-pattern solution, underscoring the diagnostic utility of the AVEM instrument. These findings suggest that AVEM can be implemented as early as the first year of teacher education to facilitate the early identification of vulnerable individuals. The concerning trends revealed in this study highlight the need for policy action, particularly the integration of psychological interventions into teacher education to support occupational health in prospective teachers.

Abstract Image denoising by means of total variation (TV) regularization is still a standard procedure. For very large images, especially three-dimensional voxel datasets, however, this can be computationally infeasible. We show how this TV regularization can be approximately performed even in arbitrary dimensions by applying appropriate shrinkage to selected and properly weighted Haar wavelet coefficients, all of which depends even on the dimensionality of the data. Our approach acts entirely on the wavelet coefficients which represent the compressed image, and is therefore suited for the application on large three-dimensional images represented in the Haar wavelet basis, e.g., volumes from computed tomography.

Abstract This article presents a transcribed, glossed, and translated folktale in Thebo Tibetan. The narrative is presented in four-line interlinear format with accompanying commentary and time-aligned transcription. This is the first published text in Thebo Tibetan and offers valuable insight into its structure and oral narrative tradition. This contribution enhances the documentation of Thebo and expands the comparative folklore record for Tibeto-Burman languages.

Organizational knowledge is essential for sustained competitive advantage, yet it naturally depreciates over time. Traditional rule-based technologies help counter this erosion by serving as stable repositories of knowledge. In contrast, machine learning (ML) systems—an increasingly prevalent and relied-upon technology—introduce new risks. Because their predictive models depend on historical training data, ML systems are vulnerable to model drift: a gradual misalignment with evolving operational realities that creates recurring needs for human-led repair. We develop a multilevel process model showing how and when repeated cycles of ML use and repair can unintentionally accelerate organizational knowledge depreciation. In doing so, we highlight the distinct vulnerabilities of ML systems, challenge the conventional view of technologies as stable repositories of knowledge, and emphasize the importance of deliberate human engagement alongside automation to sustain organizational knowledge over time.

Abstract Immanuel Kant's The Dispute between the Faculties (1798) contains a footnote referencing four utopian states — Atlantis, Utopia, Oceana, and Severambia. This passage has largely been overlooked in Kantian scholarship. This paper revisits this neglected passage to explore Kant's engagement with utopian literature and its implications for his philosophy of history and political thought. The footnote, though brief, sheds light on Kant's views regarding the feasibility of ideal states and their connection to revolutionary change. It reveals Kant's nuanced perspective on the role of utopian visions in shaping political ideals and the duties of individuals and governments in realizing such ideals. While Kant acknowledges these utopias as “sweet dreams,” he also questions their practicality and warns against revolutionary approaches to achieving them. This paper examines how Kant's references to these utopian constructs reflect his broader philosophical concerns about progress and reform, emphasizing that progress must occur through gradual, lawful reform rather than through revolutionary upheaval. By analyzing Kant's treatment of these literary fictions, the paper aims to illuminate his complex relationship with utopian thought and its place within his broader theoretical framework.

The adoption of novel technologies such as Unmanned Aerial Vehicles (UAVs) in Search and Rescue (SAR) operations remains limited. As a result, their full potential is not yet realized. Although UAVs have been deployed on an ad hoc basis, typically under manual control by dedicated operators, assisted and fully autonomous configurations remain largely unexplored. In this study, three SAR frameworks are systematically evaluated within a unified benchmarking framework: conventional ground missions, UAV-assisted missions, and fully autonomous UAV operations. As the key performance indicator, the target localization time was quantified and used as the means of comparison amongst frameworks. The conventional and assisted frameworks were experimentally tested through physical hardware in a controlled outdoor setting, wherein simulated callouts occurred via rescue teams. The autonomous swarm framework was simulated in the form of a multi-agent Reinforcement Learning (RL) method via the use of the Proximal Policy Optimization (PPO) algorithm. This enabled the optimization of the decentralized cooperative actions that could occur for efficient exploration of a partially observed three-dimensional environment. Our results demonstrated that the autonomous swarm significantly outperformed the conventional and assisted approaches in terms of speed and coverage. Finally, a detailed depiction of the framework’s integration into an operational system is provided.