Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models

Deep neural networks (DNNs) have proven to be successful in various computer vision applications such that models even infer in safety-critical situations. Therefore, vision models have to behave in a robust way to disturbances such as noise or blur. While seminal benchmarks exist to evaluate model robustness to diverse corruptions, blur is often approximated in an overly simplistic way to model defocus, while ignoring the different blur kernel shapes that result from optical systems. To study model robustness against realistic optical blur effects, this paper proposes two datasets of blur corruptions, which we denote OpticsBench and LensCorruptions. OpticsBench examines primary aberrations such as coma, defocus, and astigmatism, i.e. aberrations that can be represented by varying a single parameter of Zernike polynomials. To go beyond the principled but synthetic setting of primary aberrations, LensCorruptions samples linear combinations in the vector space spanned by Zernike polynomials, corresponding to 100 real lenses. Evaluations for image classification and object detection on ImageNet and MSCOCO show that for a variety of different pre-trained models, the performance on OpticsBench and LensCorruptions varies significantly, indicating the need to consider realistic image corruptions to evaluate a model's robustness against blur

Social Capital and National Innovativeness – The Influence of Trust and Tolerance on the National Capacity to Innovate

While national innovativeness is of pivotal relevance for economic development, so far, relatively little attention has been paid to the social drivers of innovativeness. Thus, the role of social norms and values as drivers of innovativeness is somewhat blurry. Tackling this gap, the article at hand reflects the concepts of social capital, trust, and tolerance, before a model of social capital and innovativeness is developed and tested empirically, followed by the presentation and discussion of the results and a brief conclusion

Augmented Reality for Lighting Adjustment in a Virtual Studio

An interactive virtual studio is used for live broadcasts with live interaction in front of the recording system. Advances in Augmented Reality (AR) using video see-through Head-Mounted Displays (HMDs) also make it possible to improve the production process itself. Lighting can be improved and adjusted using AR while seeing live effects. An Augmented Reality Lighting Adjustment System (ARLAS) for a virtual studio has been developed and evaluated. The traditional process for controlling lighting in a virtual studio is complicated and time-consuming. It often requires several people to control many different components at different locations. Using AR, this process is simplified by integrating relevant programs and video streams into a single application on an HMD, allowing direct control at lighting locations. Changes to settings are immediately visible on the real fixtures. Real lights can be replicated virtually to maintain consistent lighting conditions in virtual scenes, ensuring that virtual objects are lit as they would be in a real studio. The prototype demonstrated the benefits of AR for complex virtual studio setups and was evaluated by 18 participants

1020 - Prüfungsordnung (studiengangsspezifische Bestimmungen) für den Bachelorstudiengang Medientechnik an der Hochschule Düsseldorf vom 27.08.2025

Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Prüfungsordnung als Satzung erlassen. Diese Ordnung gilt nur in Verbindung mit der Rahmenprüfungsordnung des Fachbereichs Medien (RahmenPO) an der Hochschule Düsseldorf vom 27.08.2025 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 1019)

MassSpecGym: A benchmark for the discovery and identification of molecules

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at this https URL [https://github.com/pluskal-lab/MassSpecGym]

1007 - Erste Satzung zur Änderung der Ordnung über die Eintragung von studentischen Vereinigungen (Hochschulgruppen) in die Matrikel der Hochschule Düsseldorf vom 05.06.2025

Aufgrund der §§ 2 Abs. 4, 22 Abs. 1 S. 1 Nr. 3 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Satzung erlassen