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Die Positionen der Parteien zur Bundestagswahl 2021: Ergebnisse des Open Expert Surveys
No full textDiese Research Note berichtet zentrale Ergebnisse des Open Expert Surveys 2021 (OES21). In diesem Expert:innen-Survey, der vor der Bundestagswahl 2021 durchgeführt wurde, haben mehr als 300 Politikwissenschaftler:innen die wichtigsten Parteien entlang zentraler politischer Sachfragen verortet und deren Wichtigkeit für die jeweilige Partei geschätzt. Der OES21 unterscheidet sich von gängigen Expert:innenbefragungen in zweierlei Hinsicht. Zum einen umfasst der Survey zahlreiche themenspezifische Items, die in anderen Befragungen bislang keine Beachtung fanden. Zum anderen ist die Anzahl der Expert:innen sehr hoch, wodurch Zusammenhänge in der Positionierung auf Ebene der einzelnen Expert:innen analysiert werden können
The politics of seeking and avoiding discourse in parliament
No full textAbstract When do politicians debate each other in parliament, and when do they prefer to avoid discourse? While existing research has shown MPs to unilaterally leverage the dialogical nature of legislative debates to their advantage, the circumstances facilitating actual discursive interaction have so far received less attention. We introduce a new framework to study the emergence of discourse in political debates. Applying this framework, we expect ideological differences and government–opposition dynamics to shape politicians' choices about seeking or avoiding discourse. To test these hypotheses, we draw on an original dataset of all 14,595 attempted and successful interventions (Zwischenfragen) – extraordinary, voluntary discursive exchanges between speakers and MPs in the audience – in the German Bundestag (1990–2020), extracted using an annotation pipeline developed specifically for this study. We find that MPs separated by diverging preferences seek discourse with one another more often than their ideologically aligned counterparts. At the same time, these exact attempts do less frequently result in discursive interactions. When considering government–opposition dynamics in this process, we observe very similar patterns: Attempts to initiate discourse are particularly common among opposition MPs facing government speakers, and we find tentative evidence suggesting that government actors are most likely to avoid these invitations to discursive interaction. Our findings have important implications for our understanding of elite behaviour in public environments
Interpretation of high-dimensional regression coefficients by comparison with linearized compressing features
No full textBenchmarking of fluorescence lifetime measurements using time-frequency correlated photons
No full textThe investigation of fluorescence lifetime became an important tool in biology and medical science. So far, established methods of fluorescence lifetime measurements require the illumination of the investigated probes with pulsed or amplitude-modulated light. In this paper, we examine the limitations of an innovative method of fluorescence lifetime using the strong time-frequency correlation of entangled photons generated by a continuous-wave source. For this purpose, we investigate the lifetime of IR-140 to demonstrate the functional principle and its dependencies on different experimental parameters. We also compare this technique with state-of-the-art FLIM and observed an improved figure-of-merit. Finally, we discuss the potential of a quantum advantage
The Injectivity Radius of Souls of Alexandrov Spaces
No full textA sharp lower bound for the injectivity radius in noncompact nonnegatively curved Riemannian manifolds involving their soul goes back to Šarafutdinov. We generalize this bound to the setting of Alexandrov spaces. Our main theorem reads as follows. If the injectivity radius of an Alexandrov space of nonnegative curvature does not coincide with the one of its souls, then it is at least πK⁻¹/², where K is an upper curvature bound. We introduce the soul of Alexandrov spaces in some detail and compare two notions of injectivity radii
Acceptance and Trust: Drivers’ First Contact With Released Automated Vehicles in Naturalistic Traffic
No full textThis study investigates the impact of initial contact of drivers with an SAE Level 3 Automated Driving System (ADS) under real traffic conditions, focusing on the Mercedes-Benz Drive Pilot in the EQS. It examines Acceptance, Trust, Usability, and User Experience. Although previous studies in simulated environments provided insights into human-automation interaction, real-world experiences can differ significantly. The research was conducted on a segment of German interstate with 30 participants lacking familiarity with SAE Level 3 ADS. Pre- and post-driving questionnaires were used to assess changes in acceptance and trust. Supplementary metrics included post-driving ratings for usability and user experience. Findings reveal a significant increase in acceptance and trust following the first contact, confirming results from prior studies. Factors such as Performance Expectancy, Effort Expectancy, Facilitating Condition, and Perceived Safety were rated higher after initial contact with the ADS. However, inadequate communication from the ADS to the human driver was detected, highlighting the need for improved communication to prevent misuse or confusion about the operating mode. However, it’s worth noting that most participants already had a high affinity for technology. Although overall reception was positive and showed an upward trend post first contact, the ADS was also perceived as demanding as manual driving. Future research should focus on a more diverse participant sample and include longer or multiple real-traffic trips to understand behavioral adaptations over time
Robust Methods for Distributed Learning
No full textThis thesis develops robust and efficient aggregation methods for distributed learning and explores their vulnerabilities. Distributed learning paradigms, such as federated and decentralized learning, enable the coordination of models across a collection of agents without the need to exchange raw data. Instead, agents compute model updates locally and share the updated models with a parameter server or their peers, followed by an aggregation step traditionally based on averaging. However, averaging-based schemes are susceptible to outliers, where a single malicious agent can significantly degrade the model's performance. This vulnerability has led to the development of robust aggregation schemes based on variations of the median and trimmed mean, which ensure robustness but at the cost of reduced efficiency.
To address this drawback, efficient and robust aggregation schemes for distributed learning are developed in this thesis. We propose robust and efficient diffusion, a distributed learning framework which combines statistical efficiency with robustness. Additionally, a robust distributed Expectation-Maximization algorithm based on Real Elliptically Symmetric distributions is developed, which is highly adaptive to outliers and incorporates a robust data aggregation step, based on robust and efficient diffusion, to provide robustness against malicious agents. Simulations demonstrate the effectiveness of the proposed algorithms over non-robust methods.
The motivation for studying attacks on robust aggregation schemes in decentralized learning is to understand and mitigate vulnerabilities that can significantly degrade the performance of learning algorithms. By developing and analyzing attack strategies, we can identify weaknesses in existing schemes and design more resilient and secure aggregation methods. This research enhances the robustness, reliability, and trustworthiness of decentralized learning, ensuring data integrity and improving overall security against adversarial scenarios.
By leveraging the sensitivity curve, a classical tool from robust statistics, we systematically derive optimal attack patterns against arbitrary robust aggregators, rendering them ineffective in many cases. The new attack pattern, termed sensitivity curve maximization, disrupts existing robust aggregation schemes by injecting optimal perturbations. This highlights the vulnerability of classical aggregation schemes to malicious agents, leading to the degeneration or breakdown of the learning process. The effectiveness of the proposed attack is demonstrated through multiple simulations.
Overall, this dissertation enhances the robustness and efficiency of aggregation methods in distributed learning while identifying and mitigating vulnerabilities through the development of optimal attack strategies
Biphoton generation in quadratic waveguide arrays: A classical optical simulation
No full textQuantum entanglement became essential in understanding the non-locality of quantum mechanics. In optics, this non-locality can be demonstrated on impressively large length scales, as photons travel with the speed of light and interact only weakly with their environment. Spontaneous parametric down-conversion (SPDC) in nonlinear crystals provides an efficient source for entangled photon pairs, so-called biphotons. However, SPDC can also be implemented in nonlinear arrays of evanescently coupled waveguides which allows the generation and the investigation of correlated quantum walks of such biphotons in an integrated device. Here, we analytically and experimentally demonstrate that the biphoton degrees of freedom are entailed in an additional dimension, therefore the SPDC and the subsequent quantum random walk in one-dimensional arrays can be simulated through classical optical beam propagation in a two-dimensional photonic lattice. Thereby, the output intensity images directly represent the biphoton correlations and exhibit a clear violation of a Bell-like inequality
Charge identification of fragments with the emulsion spectrometer of the FOOT experiment
No full textThe FOOT (FragmentatiOn Of Target) experiment is an international project designed to carry out the fragmentation cross-sectional measurements relevant for charged particle therapy (CPT), a technique based on the use of charged particle beams for the treatment of deep-seated tumors. The FOOT detector consists of an electronic setup for the identification of Z ≥ 3 fragments and an emulsion spectrometer for Z ≤ 3 fragments. The first data taking was performed in 2019 at the GSI facility (Darmstadt, Germany). In this study, the charge identification of fragments induced by exposing an emulsion detector, embedding a C₂H₄ target, to an oxygen ion beam of 200 MeV/n is discussed. The charge identification is based on the controlled fading of nuclear emulsions in order to extend their dynamic range in the ionization response