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DemoUpStorage - Geophysical Baseline Characterization of the in-situ CO2 Mineral Storage Site in Helguvik, Iceland
Item type: Dataset
Junker, Jonas; Obermann, Anne; Zappone, Alba Simona (2025)
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control
Item type: Journal Article
Lahr, Amon; Näf, Joshua; Wabersich, Kim P.; et al. (2024)
Incorporating learning-based models, such as artificial neural networks or Gaussian processes, into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, enabling state-of-the-art implementations of learning-based models for MPC is complicated by the challenge of interfacing machine learning frameworks with real-time optimal control software. This work aims at filling this gap by incorporating external sensitivities in sequential quadratic programming solvers for nonlinear optimal control. To this end, we provide L4acados, a general framework for incorporating Python-based dynamics models in the real-time optimal control software acados. By computing external sensitivities via a user-defined Python module, L4acados enables the implementation of MPC controllers with learning-based residual models in acados, while supporting parallelization of sensitivity computations when preparing the quadratic subproblems. We demonstrate significant speed-ups and superior scaling properties of L4acados compared to available software using a neural-network-based control example. Last, we provide an efficient and modular real-time implementation of Gaussian process-based MPC using L4acados, which is applied to two hardware examples: autonomous miniature racing, as well as motion control of a full-scale autonomous vehicle for an ISO lane change maneuver.
Western Palaearctic Chelostoma bees of the subgenus Chelostoma (Megachilidae, Osmiini): biology, taxonomy and key to species
Item type: Journal Article
Müller, Andreas; Pisanty, Gideon; Dorchin, Achik (2025)
Osmiine bees of the subgenus Chelostoma (Chelostoma) are restricted to the Palaearctic. Including the species newly described in the present publication, the subgenus comprises 21 species, 19 of which occur in the Western Palaearctic. Except for two pollen generalists, all species of C. (Chelostoma), whose pollen host preferences have been studied so far, are strict pollen specialists on actinomorphic flowers of a single plant genus, such as Ornithogalum (Asparagaceae) or Ranunculus (Ranunculaceae), of a single subfamily, such as Dipsacoideae (Caprifoliaceae), or of a single family, such as Asteraceae or Brassicaceae. In this study, Lamiaceae are identified as a new host plant taxon of C. (Chelostoma) used by probably three closely related species that are equipped with very long mouthparts and an elongated head, likely representing adaptations for the exploitation of the long-tubed and zygomorphic flowers of this plant family. The females of C. (Chelostoma) nest in insect burrows in dead wood or in hollow plant stems, the colonization of which is facilitated by their very slender and elongated body. They use mud to construct brood cell partitions and nest plugs, which is often reinforced by embedding tiny pebbles and sand grains in its outer surface. The taxonomic revision of C. (Chelostoma) revealed the existence of eight new species: C. dolichocephalum sp. nov. from southeastern Turkey and northern Iraq, C. kurdistanicum sp. nov. from eastern Turkey, northern Iraq and Iran, C. levantense sp. nov. from the Levant, C.meronense sp. nov. from northern Israel, C. miripalpum sp. nov. from southern central Turkey and northern Iraq, C. negevense sp. nov. from southern Israel, C. ornithogali sp. nov. from Turkey, and C. scabiosae sp. nov. from southeastern Turkey and northwestern Iran. Chelostoma dolosum (Benoist, 1935) syn. nov. is newly synonymized with C. mocsaryi Schletterer, 1899.
Ultrafast Terahertz Field Control of the Emergent Magnetic and Electronic Interactions at Oxide Interfaces
Item type: Journal Article
Derrico, Abigail M.; Basini, Martina; Unikandanunni, Vivek; et al. (2025)
Ultrafast electric‐field control of emergent electronic and magnetic states at oxide interfaces offers exciting prospects for the development of the next generation of energy‐efficient devices. Here, it is demonstrated that the electronic structure and emergent ferromagnetic interfacial state in epitaxial LaNiO
3
/CaMnO
3
superlattices can be effectively controlled using intense, single‐cycle THz electric‐field pulses. A suite of advanced X‐ray spectroscopic techniques is employed to measure a detailed magneto‐optical profile and the thickness of the ferromagnetic interfacial layer. Then, a combination of time‐resolved and temperature‐dependent optical measurements is used to disentangle several correlated electronic and magnetic processes driven by ultrafast, high‐field THz pulses. Sub‐picosecond non‐equilibrium Joule heating of the electronic system is observed, ultrafast demagnetization of the ferromagnetic interfacial layer, and slower dynamics indicative of a change in the magnetic state of the superlattice due to the transfer of spin‐angular momentum to the lattice. These findings suggest a promising avenue for the efficient control of 2D ferromagnetic states at oxide interfaces using ultrafast electric‐field pulses.
Optimal kernel regression bounds under energy-bounded noise
Item type: Conference Paper
Amon Lahr; Johannes Köhler; Anna Scampicchio; et al. (2025)
Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical contexts. In this paper, we derive a tight, non-asymptotic uncertainty bound for kernel-based estimation, which can also handle correlated noise sequences. Its computation relies on a mild norm-boundedness assumption on the unknown function and the noise, returning the worst-case function realization within the hypothesis class at an arbitrary query input location. The value of this function is shown to be given in terms of the posterior mean and covariance of a Gaussian process for an optimal choice of the measurement noise covariance. By rigorously analyzing the proposed approach and comparing it with other results in the literature, we show its effectiveness in returning tight and easy-to-compute bounds for kernel-based estimates.