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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., Comment: 26 pages, 7 figures, 2 tables, Supporting Information

In this paper, we study how generative AI and specifically large language models (LLMs) impact learning in coding classes. We show across three studies that LLM usage can have positive and negative effects on learning outcomes. Using observational data from university-level programming courses, we establish such effects in the field. We replicate these findings in subsequent experimental studies, which closely resemble typical learning scenarios, to show causality. We find evidence for two contrasting mechanisms that determine the overall effect of LLM usage on learning. Students who use LLMs as personal tutors by conversing about the topic and asking for explanations benefit from usage. However, learning is impaired for students who excessively rely on LLMs to solve practice exercises for them and thus do not invest sufficient own mental effort. Those who never used LLMs before are particularly prone to such adverse behavior. Students without prior domain knowledge gain more from having access to LLMs. Finally, we show that the self-perceived benefits of using LLMs for learning exceed the actual benefits, potentially resulting in an overestimation of one's own abilities. Overall, our findings show promising potential of LLMs as learning support, however also that students have to be very cautious of possible pitfalls.

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.

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., Comment: 12 pages, 9 figures, submitted and accepted for SPIE Optical Systems Design, 2018, Frankfurt, Germany. arXiv admin note: text overlap with arXiv:1801.02197

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., Comment: 5 pages, 12 figures, submitted and accepted for IS&T Electronic Imaging, Autonomous Vehicles and Machines 2018