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PublicationVariational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments( 2024)
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PublicationDelineating morbidity patterns in preterm infants at near-term age using a data-driven approach( 2024)Background: Long-term survival after premature birth is significantly determined by development of morbidities, primarily affecting the cardio-respiratory or central nervous system. Existing studies are limited to pairwise morbidity associations, thereby lacking a holistic understanding of morbidity co-occurrence and respective risk profiles. Methods: Our study, for the first time, aimed at delineating and characterizing morbidity profiles at near-term age and investigated the most prevalent morbidities in preterm infants: bronchopulmonary dysplasia (BPD), pulmonary hypertension (PH), mild cardiac defects, perinatal brain pathology and retinopathy of prematurity (ROP). For analysis, we employed two independent, prospective cohorts, comprising a total of 530 very preterm infants: AIRR ("Attention to Infants at Respiratory Risks") and NEuroSIS ("Neonatal European Study of Inhaled Steroids"). Using a data-driven strategy, we successfully characterized morbidity profiles of preterm infants in a stepwise approach and (1) quantified pairwise morbidity correlations, (2) assessed the discriminatory power of BPD (complemented by imaging-based structural and functional lung phenotyping) in relation to these morbidities, (3) investigated collective co-occurrence patterns, and (4) identified infant subgroups who share similar morbidity profiles using machine learning techniques. Results: First, we showed that, in line with pathophysiologic understanding, BPD and ROP have the highest pairwise correlation, followed by BPD and PH as well as BPD and mild cardiac defects. Second, we revealed that BPD exhibits only limited capacity in discriminating morbidity occurrence, despite its prevalence and clinical indication as a driver of comorbidities. Further, we demonstrated that structural and functional lung phenotyping did not exhibit higher association with morbidity severity than BPD. Lastly, we identified patient clusters that share similar morbidity patterns using machine learning in AIRR (n=6 clusters) and NEuroSIS (n=8 clusters). Conclusions: By capturing correlations as well as more complex morbidity relations, we provided a comprehensive characterization of morbidity profiles at discharge, linked to shared disease pathophysiology. Future studies could benefit from identifying risk profiles to thereby develop personalized monitoring strategies.
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PublicationTowards Small Anomaly Detection( 2024)In this position paper, we describe the design of a camera-based FOD (Foreign Object Debris) detection system intended for use in the parking position at the airport. FOD detection, especially the detection of small objects, requires a great deal of human attention. The transfer of ML (machine learning) from the laboratory to the field calls for adjustments, especially in testing the model. Automated detection requires not only high detection performance and low false alarm rate, but also good generalization to unknown objects. There is not much data available for this use case, so in addition to ML methods, the creation of training and test data is also considered.
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PatentVerfahren und Vorrichtungen für automatische, kooperative Manöver( 2023-03-23)Offenbart ist ein Verfahren, umfassend die Schritte:- Erhalten einer gemeinsamen Umgebungsinformation wenigstens zweier Maschinen;- Generieren einer Mehrzahl von Kooperationsmanövern, welche jeweils ein Manöver für jede der zwei Maschinen umfassen, auf Basis der Umgebungsinformation;- Bewerten jedes Kooperationsmanövers auf Basis eines vorgegebenen Gütekriteriums;- Auswählen eines Kooperationsmanövers, welches einen vorbestimmten Wert des Gütekriteriums, insbesondere einen Bestwert, aufweist;- Bereitstellen des ausgewählten Kooperationsmanövers für die zwei Maschinen.
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PublicationButterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection( 2023)This work aims to explore and identify tiny and seemingly unrelated perturbations of images in object detection that will lead to performance degradation. While tininess can naturally be defined using Lp norms, we characterize the degree of "unrelatedness" of an object by the pixel distance between the occurred perturbation and the object. Triggering errors in prediction while satisfying two objectives can be formulated as a multi-objective optimization problem where we utilize genetic algorithms to guide the search. The result successfully demonstrates that (invisible) perturbations on the right part of the image can drastically change the outcome of object detection on the left. An extensive evaluation reaffirms our conjecture that transformer-based object detection networks are more susceptible to butterfly effects in comparison to single-stage object detection networks such as YOLOv5.
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PublicationDevOps in Robotics: Challenges and Practices( 2023)DevOps, which refers to a set of practices for streamlining the development and operations of software companies, is becoming increasingly popular as businesses strive to adopt a loosely coupled architecture that supports frequent software delivery. As a result, DevOps is also gaining traction in other domains and involved architectures, including robotics, though research in this area is still lacking. To address this gap, this paper investigates how to adapt key DevOps principles from the domain of software engineering to the domain of robotics. In order to demonstrate the feasibility of this in practice, an industrial robotics case study is conducted. The results indicate that the adoption of these principles is also beneficial for robotic software architectures, though general DevOps approaches may require some adaptation to match the existing infrastructure.
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PublicationStatistical Guarantees for Safe 2D Object Detection Post-processing( 2023)Safe and reliable object detection is essential for safetycritical applications of machine learning, such as autonomous driving. However, standard object detection methods cannot guarantee their performance during operation. In this work, we leverage conformal prediction in order to provide statistical guarantees for back-box object detection models. Extending prior work, we present a postprocessing methodology that can cover the entire object detection problem (localization, classification, false negatives, detection in videos, etc.), while offering sound safety guarantees on its error rates. We apply our method on state-of-the-art 2D object detection models and measure its efficacy in practice. Moreover, we investigate what happens as the acceptable error rates are pushed towards high safety levels. Overall, the presented methodology offers a practical approach towards safety-aware object detection, and we hope it can pave the way for further research in this area.
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PublicationEfficient MILP Decomposition in Quantum Computing for ReLU Network Robustness( 2023)
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PublicationBenchmarking the Variational Quantum Eigensolver using different quantum hardware( 2023)
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PublicationEffects of defects in automated fiber placement laminates and its correlation to automated optical inspection results( 2023)Automated Fiber Placement (AFP) is a widely used production process for the manufacturing of large scale CFRP parts. However, the occurrence of manufacturing defects such as gaps or overlaps is still a common problem in today’s AFP production environments. This study investigates the effect of different defect configurations on the mechanical performance (i.e., tensile strength, flexural strength, and shear strength) of AFP laminates. The results are then linked to the data generated “inline” by a ply inspection system. We use the Pearson correlation in order to relate the measured defect volume to the strength of samples containing different types of defects. A clear knockdown in tensile strength was found for specimens with gaps or overlaps that caused a high amount of fiber undulations in the laminate. The sensor data analysis showed a similar trend. Specimens with a high defect volume had significantly lower values for the tensile strength. A correlation coefficient of -0.98 between these two values was calculated. The obtained results are a promising step towards automated quality inspection for the AFP process.
