搜索结果：Medizinische Klinik (Munich, Germany : 1983)

Algorithm registers are public-facing databases that display basic information about algorithms employed in public administration. While several such registers exist across Europe and globally, their capacity to deliver meaningful transparency remains contested. In Germany, the landscape is notably fragmented: no federal-level register exists, yet at least five state- and federal-level initiatives publish information about AI systems with varying scopes and objectives. A recent conceptual proposal by Alina Lorenz (2025), outlines technical and governance requirements for a national AI transparency register in Germany. We repurpose this proposal as an audit instrument, extracting structured checklists from the transparency goals and subgoals it formulates. The resulting checklists, translated from German into English, is made publicly available to support practitioners auditing existing registers or designing new ones. We apply this framework to conduct an external audit of the two main existing German transparency initiatives, MaKI and Lernende Systeme, evaluating the extent to which they fulfill the proposed goals. Our audit reveals that several adaptations are likely needed for t

Modern, data-driven medical research requires the processing of sensitive health data on a large scale. However, this data is subject to special protection under the GDPR, which is why processing regularly raises data protection concerns in practice. These concerns are particularly prevalent when sensitive personal data is processed without informed consent. This article analyses options for data processing in the field of medical research without consent and describes the legal framework for anonymisation under the GDPR, the national Austrian implementation of the research exemption, and their interaction. -- Moderne, datengetriebene medizinische Forschung erfordert die Verarbeitung sensibler Gesundheitsdaten in grossem Ausmass. Diese sind im System der DSGVO jedoch besonders geschützt, weswegen einer rechtssicheren Verarbeitung in der Praxis regelmässig datenschutzrechtliche Bedenken entgegenstehen. Diese Bedenken bestehen insbesondere bei Verarbeitung sensibler personenbezogener Daten ohne informierte Einwilligung. Dieser Beitrag analysiert daher Möglichkeiten zur Datenverarbeitung im Bereich der medizinischen Forschung fernab der Einwilligung und beschreibt hierfür das rechtlic

Many documentaries on early house and techno music exist. Here, protagonists from the scenes describe key elements and events that affected the evolution of the music. In the research community, there is consensus that such descriptions have to be examined critically. Yet, there have not been attempts to validate such statements on the basis of audio analyses. In this study, over 9,000 early house and techno tracks from Germany and the United States of America are analyzed using recording studio features, machine learning and inferential statistics. Three observations can be made: 1.) German and US house/techno music are distinct, 2.) US styles are much more alike, and 3.) scarcely evolved over time compared to German house/techno regarding the recording studio features. These findings are in agreement with documented statements and thus provide an audio-based perspective on why techno became a mass phenomenon in Germany but remained a fringe phenomenon in the USA. Observations like these can help the music industry estimate whether new trends will experience a breakthrough or disappear.

The recent breakthroughs in the distribution of quantum information and high-precision time and frequency (T&F) signals over long-haul optical fibre networks have transformative potential for physically secure communications, resilience of Global Navigation Satellite Systems (GNSS) and fundamental physics. However, so far these capabilities remain confined to isolated testbeds, with quantum and T&F signals accessible, for example in Germany, to only a few institutions. We propose the QTF-Backbone: a dedicated national fibre-optic infrastructure in Germany for the networked distribution of quantum and T&F signals using dark fibres and specialized hardware. The QTF-Backbone is planned as a four-phase deployment over ten years to ensure scalable, sustainable access for research institutions and industry. The concept builds on successful demonstrations of high-TRL time and frequency distribution across Europe, including PTB-MPQ links in Germany, REFIMEVE in France, and the Italian LIFT network. The QTF-Backbone will enable transformative R&D, support a nationwide QTF ecosystem, and ensure the transition from innovation to deployment. As a national and European hub, it w

Using administrative data from Germany, this study provides first evidence on the wage effects of collective bargaining compliance laws. These laws require establishments receiving public contracts to pay wages set by a representative collective agreement, even if they are not formally bound by one. Leveraging variation in the timing of law implementation across federal states, and focusing on the public transport sector -- where regulation is uniform and demand is driven solely by state-level needs -- I estimate dynamic treatment effects using event-study designs. The results indicate that within five years of the law's implementation, wage increases were on average 2.9\% to 4.6\% higher in federal states with such a law compared to those without one -- but only in East Germany. These findings highlight the potential for securing collectively agreed wages in times of declining collective bargaining coverage.

The warmer temperatures of global climate change strengthen the water cycle, evaporation and precipitation increase. But the extremes of heavy rain, floods, dry periods and droughts will also increase. How does this fit together? Simple physical considerations show which factors mainly regulate the strength of the water cycle in the Earth system, and how this determines water availability on land. This can be used to interpret the observed changes in the water balance in Germany and explain the increasing dryness in Germany.

We present a historical outline of the research and developments of Virtual Reality at the Fraunhofer Institute for Computer Graphics (IGD) in Darmstadt, Germany, from 1990 through 2000.

The correct detection of dense article layout and the recognition of characters in historical newspaper pages remains a challenging requirement for Natural Language Processing (NLP) and machine learning applications on historical newspapers in the field of digital history. Digital newspaper portals for historic Germany typically provide Optical Character Recognition (OCR) text, albeit of varying quality. Unfortunately, layout information is often missing, limiting this rich source's scope. Our dataset is designed to enable the training of layout and OCR modells for historic German-language newspapers. The Chronicling Germany dataset contains 693 annotated historical newspaper pages from the time period between 1852 and 1924. The paper presents a processing pipeline and establishes baseline results on in- and out-of-domain test data using this pipeline. Both our dataset and the corresponding baseline code are freely available online. This work creates a starting point for future research in the field of digital history and historic German language newspaper processing. Furthermore, it provides the opportunity to study a low-resource task in computer vision

Even though many of the experiments leading to the standard model of particle physics were done at large accelerator laboratories in the US and at CERN, many exciting developments happened in smaller national facilities all over the world. In this report we highlight the history of accelerator facilities in Germany.

This study investigates the implementation of semi-on-demand (SoD) hybrid-route services using Shared Autonomous Vehicles (SAVs) on existing transit lines. SoD services combine the cost efficiency of fixed-route buses with the flexibility of on-demand services. SAVs first serve all scheduled fixed-route stops, then drop off and pick up passengers in the pre-determined flexible-route portion, and return to the fixed route. This study addresses four key questions: optimal fleet and vehicle sizes for peak-hour fixed-route services with SAVs and during transition (from drivers to autonomous vehicles), optimal off-peak SoD service planning, and suitable use cases. The methodology combines analytical modeling for service planning with agent-based simulation for operational analysis. We examine ten bus routes in Munich, Germany, considering full SAV and transition scenarios with varying proportions of drivers. Our findings demonstrate that the lower operating costs of SAVs improve service quality through increased frequency and smaller vehicles, even in transition scenarios. The reduced headway lowers waiting time and also favors more flexible-route operation in SoD services. The optimal

Collecting and analyzing meaningful data in mobile networks is the key to assessing network performance. Crowdsourced Network Measurements (CNMs) provide insights beyond the network layer and offer performance and other measurements at the application and user-level towards Quality of Experience (QoE). In this paper, the mobile Internet experience for Germany is evaluated with the help of crowdsourcing from the perspective of an end user. We statistically analyze a dataset with throughput measurements on the end device from Tutela Ltd., which covers more than 2.5 million throughput tests across Germany from January to July 2019. We give insights into this emerging methodology and highlight the benefits of this method. The paper contains statistics and conclusions for several large cities as well as regions in Germany compared to general statements for Germany, since individual measurements and averages often only imprecisely reflect the situation. The goal is to give a holistic view of the performance of the current mobile network in Germany. Reading this paper, it becomes evident that reliable statements about the quality of the mobile network for Germany depend on a large number

We study ionic electrodiffusion modeled by the Nernst--Planck equations describing the evolution of $N$ ionic species in a three-dimensional incompressible fluid flowing through a porous medium. We address the long-time dynamics of the resulting system in the three-dimensional whole space $\mathbb{R}^3$. We prove that the $k$-th spatial derivatives of each ionic concentration decays to zero in $L^2$ with a sharp rate of order $t^{-\frac{3}{4}-\frac{k}{2}}$. Moreover, we investigate the behavior of the relative entropy associated with the model and show that it blows up in time with a sharp growth rate of order $\log t$.

We assess the impact of COVID-19 response measures implemented in Germany and Switzerland on cumulative COVID-19-related hospitalization and death rates. Our analysis exploits the fact that the epidemic was more advanced in some regions than in others when certain lockdown measures came into force, based on measuring health outcomes relative to the region-specific start of the epidemic and comparing outcomes across regions with earlier and later start dates. When estimating the effect of the relative timing of measures, we control for regional characteristics and initial epidemic trends by linear regression (Germany and Switzerland), doubly robust estimation (Germany), or synthetic controls (Switzerland). We find for both countries that a relatively later exposure to the measures entails higher cumulative hospitalization and death rates on region-specific days after the outbreak of the epidemic, suggesting that an earlier imposition of measures is more effective than a later one. For Germany, we also evaluate curfews (as introduced in a subset of states) based on cross-regional variation. We do not find any effects of curfews on top of the federally imposed contact restriction that

The recently introduced EML (Exp-Minus-Log) function acts as continuous analogue of NAND gates, providing a compositional building block capable of representing elementary functions. In this work, we study the expressive power of tree-structured compositions of EML functions. We show that such trees enjoy a universal approximation property for functions in $W^{k, \infty}$ for $k \in \mathbb N$, drawing on classical neural network approximation arguments while exploiting the ability to explicitly construct EML trees that mimic polynomial representations. We further propose a learning algorithm for EML-type trees equipped with fitting parameters, and demonstrate its feasibility in practical optimization problems. Our results establish EML trees as a theoretically grounded framework for function approximation.

Hamiltonian systems lie at the heart of modeling the physical world. Their defining scalar, the Hamiltonian, encodes both energy conservation and symplectic geometry in its phase-space trajectories. Recent deep learning approaches model Hamiltonian systems by embedding their properties either in the architecture or in the loss function. However, they typically ignore that: i) a Hamiltonian carries units of energy and/or ii) that every integrable Hamiltonian admits a canonical transformation to action-angle coordinates in which the dynamics reduce to a simple rotation on an invariant torus. We propose BuSyNet, a deep learning architecture that combines these two constraints via a dimensionally-consistent, symplectic transformation. A symplectic layer maps input trajectories to lower-dimensional latent action-angle variables, which are then combined with system parameters to discover a symbolic Hamiltonian expression in units of energy. Evaluated on the harmonic oscillator and the Kepler two-body problem (in 2D and 3D), BuSyNet recovers concise, closed-form Hamiltonians that outperform state-of-the-art neural architectures in long-term prediction accuracy and stability, while maintai

The use of green hydrogen can support the decarbonization of sectors which are difficult to electrify, such as industry or heavy transport. Yet, the wider power sector effects of providing green hydrogen are not well understood so far. We use an open-source electricity sector model to investigate potential power sector interactions of three alternative supply chains for green hydrogen in Germany in the year 2030. We distinguish between model settings in which Germany is modeled as an electric island versus embedded in an interconnected system with its neighboring countries, as well as settings with and without technology-specific capacity bounds on wind energy. The findings suggest that large-scale hydrogen storage can provide valuable flexibility to the power system in settings with high renewable energy shares. These benefits are more pronounced in the absence of flexibility from geographical balancing. We further find that the effects of green hydrogen production on the optimal generation portfolio strongly depend on the model assumptions regarding capacity expansion potentials. We also identify a potential distributional effect of green hydrogen production at the expense of oth

The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February to mid September 2020. Using a hierarchical Bayesian framework, we found that the temporal trend of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in August. However decline and increase of the temporal trend seems to be sharper in Spain and smoother in Germany. The spatial heterogeneity of the relative risk of COVID-19 in Spain is also more pronounced than Italy and Germany.

The NUCLEUS experiment aims to detect coherent elastic neutrino-nucleus scattering of reactor antineutrinos on CaWO$_4$ targets in the fully coherent regime, using gram-scale cryogenic calorimeters. The experimental apparatus will be installed at the Chooz nuclear power plant in France, in the vicinity of two 4.25 GW$_{\text{th}}$ reactor cores. This work presents results from the commissioning of an essential version of the experiment at the shallow Underground Laboratory of the Technical University of Munich. For the first time, two cryogenic target detectors were tested alongside active and passive shielding systems. Over a period of two months all detector subsystems were operated with stable performance. Background measurements were conducted, providing important benchmarks for the modeling of background sources at the reactor site. Finally, we present ongoing efforts to upgrade the detector systems in preparation for a technical run at Chooz in 2026, and highlight the remaining challenges to achieving neutrino detection.

In 1990, Germany began the reunification of two separate research systems. In this study, we explore the factors predicting the East-West integration of academic fields by examining the evolution of Germany's co-authorship network between 1974 and 2014. We find that the unification of the German research network accelerated rapidly during the 1990s, but then stagnated at an intermediate level of integration. We then study the integration of the 20 largest academic fields (by number of publications prior to 1990), finding an inverted U-shaped relationship between each field's East or West "dominance" (a measure of the East-West concentration of a field's scholarly output prior to 1990) and the fields' subsequent level of integration. We checked for the robustness of these results by running Monte Carlo simulations and a differences-in-differences analysis. Both methods confirmed that fields that were dominated by either West or East Germany prior to reunification integrated less than those whose output was balanced. Finally, we explored the origins of this inverted U-shaped relationship by considering the tendency of scholars from a given field to collaborate with scholars from simi