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Understanding the Activity and Stability of Vanadium Oxynitride Thin Films for N 2 Reduction to Ammonia by Combining Theory and Operando Measurements
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Praktiken und Infrastrukturen des Open-Access-Monitorings
No full textDiese Präsentation wurde bei den Open-Access-Tagen 2025 einleitend zu Workshop 24 'Praktiken und Infrastrukturen des Open-Access-Monitorings' gezeigt. Unter dem Begriff Open-Access-Monitoring wird die Erfassung und Analyse von Informationen, Publikationen, Kosten und weiteren Parametern des Open-Access-Publizierens verstanden. Mit Blick auf Zielmarken sowie auf die ökonomische Dimension der Open-Access-Transformation gewinnt der Umgang mit diesen Informationen weiter an Relevanz. Dieser Workshop wird gemeinsam von den DFG-geförderten Projekten OA Datenpraxis, openCost und Transform2Open veranstaltet und richtet sich an Professionals, die sich mit Monitoring beschäftigen. Ziel des Workshops ist es, gemeinsam mit den Teilnehmenden aktuelle Fragestellungen des Open-Access-Monitorings zu diskutieren. Dabei widmet sich der Workshop den folgenden Handlungsfeldern: (1) die Stärkung der Standardisierung im Bereich des Monitorings, (2) die Förderung von Transparenzinitiativen in diesem Bereich, (3) die Weiterentwicklung der Nachnutzungsmöglichkeiten offener Daten – auch im Kontext der Barcelona Declaration
Upper stratosphere to thermosphere effects and H2O transport in the deepBrewer-Dobson branch. In APARC, 2025: The Hunga Eruption Atmospheric Impacts Report [Yunqian Zhu,Graham Mann, Paul A. Newman, William Randel (Eds.)].
No full textSimulation of a simultaneous traceable spectroradiometric calibration of an imaging spectrometer
No full textSpectroradiometric calibration aims to determine the instrumental spectral response function (ISRF) parameters and radiometric coefficients of an instrument’s spectral bands across all spatial pixels. Typically, this is done by making separate spectral and radiometric calibration measurements. We present a method for the simultaneous traceable spectroradiometric calibration of an imaging spectrometer, using the Spectroscopically Tunable Absolute Radiometric, calibration and characterisation, Optical Ground Support Equipment (STAR-cc-OGSE) facility. We performed the forward simulation of calibration data acquisition by convolving input spectra with the sensor model’s response and simulated a slit scattering function (SSF)-based calibration, allowing for both ISRF coefficients and the absolute spectral responsivities to be accurately retrieved from a single series of measurements. We show how the SSF method minimizes uncertainties compared to the traditional spectroradiometric calibration approach
Efficient state preparation for the Schwinger model with a theta term
No full textWe present a comparison of different quantum state preparation algorithms and their overall efficiency for the Schwinger model with a theta term. While adiabatic state preparation is proved to be effective, in practice it leads to large cnot gate counts to prepare the ground state. The quantum approximate optimization algorithm (QAOA) provides excellent results while keeping the cnot counts small by design, at the cost of an expensive classical minimization process. We introduce a “blocked” modification of the Schwinger Hamiltonian to be used in the QAOA that further decreases the length of the algorithms as the size of the problem is increased. The rodeo algorithm (RA) provides a powerful tool to efficiently prepare any eigenstate of the Hamiltonian, as long as its overlap with the initial guess is large enough. We obtain the best results when combining the blocked QAOA ansatz and the RA, as this provides an excellent initial state with a relatively short algorithm without the need to perform any classical steps for large problem sizes
Chorismate-derived production of bulk chemicals and secondary metabolites in engineered Pseudomonas taiwanensis VLB120
No full textAn Investigation of a Multimodal Variational Autoencoder Framework for Physics Data
No full textMany scientific domains, such as physics, provide multimodal data when observing complex phenomena or when doing experiments. Understanding individual contributions of each modality can help to optimise experimental setups and sensors, thereby potentially increasing accuracy on domain-specific tasks that rely on such data. This thesis examines the role of multimodal data in (downstream) prediction tasks, with a focus on the unique and shared contributions of the respective modalities. Disentangled representation learning is a paradigm that aims to extract the independent, underlying factors from data. We employ this approach for multimodal data, proposing an extension to the disentangled multimodal variational autoencoder (DMVAE) by incorporating an additional optimisation objective to enforce minimal redundancy between shared and unique latent representations extracted by the DMVAE. Based on these representations, we train and evaluate several downstream tasks to study their contributions to the task. We compare this method to the traditional DMVAE and VAE across multimodal and single-modal configurations and also compare it directly to regression models. In our experiments, this approach is applied to the Multimodal Universe (MMU) astronomical dataset, which includes both imagery and spectral data. We also evaluate the impact of a physical-model-based differentiable image decoder model for extracting meaningful parameters into the latent space. Addi-tionally, the setup is applied to HyPlant hyperspectral remote sensing data, which consists of airborne measurements of Earth’s surface, to study it as a source of multimodal data to test how much information images and spectra contain about hyperspectral data
