arXiv subjects

Matthias Lehmann

Publications and source records attributed to Matthias Lehmann.

5 recordsLinked to original sources

arXiv2024

Anisotropic Photo-Physical Properties of Plexcitons in Strongly Coupled Metal-Organic Thin Films

Exciton plasmon polaritons have gained increasing interests over recent years due to their versatile properties emerging by the underlying light-matter coupling and making them potential candidates for new photonic applications. We have advanced this concept by studying thin films of laterally aligned J-type aggregates of self-assembled tetra-bay phenoxy-dendronized perylene bisimide (PBI) molecules, arranged in a helical manner of three strains on a silver surface. As a result of the interaction between the uniformly aligned dipole moments and the surface plasmons of a thin silver layer underneath, the excitonic state at 1.94 eV evolves into dispersions in absorption and emission, both characterized by a distinct anisotropy. The coupling constant defined by the scalar product of the transition dipole moment $\vec{\mu}$ and the surface plasmon wavevector $\vec{k}_x$ shows a pronounced two-fold rotational symmetry with values between almost 0 to 28 meV. Complementary TD-DFT calculations of the angular dependent absorption and photoluminescence provide insights in the coherent energy exchange between the excitonic and plasmonic sub-systems. Additionally, power dependent PL studies yield first evidence that the diffusion length of the coupled exciton-plasmon polaritons exceeds that of the mere Frenkel state in neat PBI by at least one order of magnitude. Our results not only demonstrate the possibility to control the photo-physical properties of strongly coupled states by their spatially anisotropic light-matter interaction but also reveal innovative strategies to influence opto-electronic device operation by the directional transport of hybrid state energy.

physics.optics↗

arXiv2024

AI Meets the Classroom: When Do Large Language Models Harm Learning?

The effect of large language models (LLMs) in education is debated: Previous research shows that LLMs can help as well as hurt learning. In two pre-registered and incentivized laboratory experiments, we find no effect of LLMs on overall learning outcomes. In exploratory analyses and a field study, we provide evidence that the effect of LLMs on learning outcomes depends on usage behavior. Students who substitute some of their learning activities with LLMs (e.g., by generating solutions to exercises) increase the volume of topics they can learn about but decrease their understanding of each topic. Students who complement their learning activities with LLMs (e.g., by asking for explanations) do not increase topic volume but do increase their understanding. We also observe that LLMs widen the gap between students with low and high prior knowledge. While LLMs show great potential to improve learning, their use must be tailored to the educational context and students' needs.

cs.CY↗

arXiv2024

The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations

In recent years, various powerful policy gradient algorithms have been proposed in deep reinforcement learning. While all these algorithms build on the Policy Gradient Theorem, the specific design choices differ significantly across algorithms. We provide a holistic overview of on-policy policy gradient algorithms to facilitate the understanding of both their theoretical foundations and their practical implementations. In this overview, we include a detailed proof of the continuous version of the Policy Gradient Theorem, convergence results and a comprehensive discussion of practical algorithms. We compare the most prominent algorithms on continuous control environments and provide insights on the benefits of regularization. All code is available at https://github.com/Matt00n/PolicyGradientsJax.

cs.LG↗

arXiv2018

Resolution and accuracy of non-linear regression of PSF with artificial neural networks

In a previous work we have demonstrated a novel numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allows for completely new ways to validate optical systems, which is especially interesting for mass production optics like in the automotive industry, but also for ophtalmology. The numerical basis for this model is a non-linear regression of the PSF with an artificial neural network (ANN). In this work we examine two important aspects of this model: the spatial resolution and the accuracy of the model. Measurement and simulation of a PSF can have a much higher resolution then the typical pixel size used in current camera sensors, especially those for the automotive industry. We discuss the influence this has on on the topology of the ANN and the final application where the modeled PSF is actually used. Another important influence on the accuracy of the trained ANN is the error metric which is used during training. The PSF is a distinctly non-linear function, which varies strongly over field and defocus, but nonetheless exhibits strong symmetries and spatial relations. Therefore we examine different distance and similarity measures and discuss its influence on the modeling performance of the ANN.

eess.IV↗

arXiv2018

Realistic Image Degradation with Measured PSF

Training autonomous vehicles requires lots of driving sequences in all situations\cite{zhao2016}. Typically a simulation environment (software-in-the-loop, SiL) accompanies real-world test drives to systematically vary environmental parameters. A missing piece in the optical model of those SiL simulations is the sharpness, given in linear system theory by the point-spread function (PSF) of the optical system. We present a novel numerical model for the PSF of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The numerical basis for this model is a non-linear regression of the PSF with an artificial neural network (ANN). The novelty lies in the portability and the parameterization of this model, which allows to apply this model in basically any conceivable optical simulation scenario, e.g. inserting a measured lens into a computer game to train autonomous vehicles. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field and azimuth. By convolving existing images and videos with this PSF model we apply the measured lens as a transfer function, therefore generating an image as if it were seen with the measured lens itself. Applications of this method are in any optical scenario, but we focus on the context of autonomous driving, where quality of the detection algorithms depends directly on the optical quality of the used camera system. With the parameterization of the optical model we present a method to validate the functional and safety limits of camera-based ADAS based on the real, measured lens actually used in the product.

eess.IV↗