TY - JOUR A1 - Oeppert, Luise A1 - Hartz, Tobias A1 - Wehner, Kathrin A1 - Schrader, Thomas A1 - Meier, Jens T1 - FHIR-Datenmodell zur Übermittlung von Tumordaten zwischen Krebsregistern und IQTIG JF - GMS Medizinische Informatik, Biometrie und Epidemiologie KW - Computer applications to medicine KW - Medical informatics KW - Internal medicine KW - Infectious and parasitic diseases Y1 - 2021 UR - https://doaj.org/article/dc0151fdba764800ac9c652e3ad4eb40 U6 - https://doi.org/10.3205/mibe000231 VL - 17 IS - Iss 4 PB - German Medical Science GMS Publishing House ER - TY - CHAP A1 - Orlowski, Maximilian A1 - Knauff, Emilia A1 - Marquardt, Florian T1 - Towards Personal Assistants for Energy Processes Based on Locally Deployed LLMs T2 - Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) , 2025, Porto, Portugal N2 - This paper presents a coaching assistant for network operator processes based on a Retrieval-Augmented Gen- eration (RAG) system leveraging open-source Large Language Models (LLMs) as well as Embedding Models. The system addresses challenges in employee onboarding and training, particularly in the context of increased customer contact due to more complex and extensive processes. Our approach incorporates domain-specific knowledge bases to generate precise, context-aware recommendations while mitigating LLM hallucination. We introduce our systems architecture to run all components on-premise in an our own datacenter, ensuring data security and process knowledge control. We also describe requirements for underlying knowledge doc- uments and their impact on assistant answer quality. Our system aims to improve onboarding accuracy and speed while reducing senior employee workload. The results of our study show that realizing a coaching assistant for German network operators is reasonable, when addressing performance, correctness, integration and locality. However current results regarding accu- racy do not yet meet the requirements for productive use. Y1 - 2025 U6 - https://doi.org/10.5220/0013175600003890 SP - 695 EP - 706 PB - SCITEPRESS ER - TY - CHAP A1 - Pepe, Alberto A1 - Buchholz, Sven A1 - Lasenby, Joan T1 - Clifford Group Equivariant Neural Network Layers for Protein Structure Prediction T2 - Northern Lights Deep Learning Conference, VOL 233, 09-11 Januar 2024, Tromso, Norwegen N2 - We employ Clifford Group Equivariant Neural Network (CGENN) layers to predict protein coordinates in a Protein Structure Prediction (PSP) pipeline. PSP is the estimation of the 3D structure of a protein, generally through deep learning architectures. Information about the geometry of the protein chain has been proven to be crucial for accurate predictions of 3D structures. However, this information is usually flattened as machine learning features that are not representative of the geometric nature of the problem. Leveraging recent advances in geometric deep learning, we redesign the 3D projector part of a PSP architecture with the addition of CGENN layers . CGENNs can achieve better generalization and robustness when dealing with data that show rotational or translational invariance such as protein coordinates, which are independent of the chosen reference frame. CGENNs inputs, outputs, weights and biases are objects in the Geometric Algebra of 3D Euclidean space, i.e. G3,0,0, and hence are interpretable from a geometrical perspective. We test 6 approaches to PSP and show that CGENN layers increase the accuracy in term of GDT scores by up to 2.1\%, with fewer trainable parameters compared to linear layers and give a clear geometric interpretation of their outputs. Y1 - 2024 UR - https://proceedings.mlr.press/v233/pepe24a/pepe24a.pdf SP - 1 EP - 7 PB - PMLR ER - TY - CHAP A1 - Pepe, Alberto A1 - Lasenby, Joan A1 - Buchholz, Sven T1 - CGAPoseNet+GCAN: A Geometric Clifford Algebra Network for Geometry-aware Camera Pose Regression T2 - Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, US, 2024 N2 - We introduce CGAPoseNet+ GCAN, which enhances CGAPoseNet, an architecture for camera pose regression, with a Geometric Clifford Algebra Network (GCAN). With the addition of the GCAN we obtain a geometry-aware pipeline for camera pose regression from RGB images only. CGAPoseNet employs Clifford Geometric Algebra to unify quaternions and translation vectors into a single mathematical object, the motor, which can be used to uniquely describe camera poses. CGAPoseNet solves the issue of balancing rotation and translation components in the loss function, and can obtain comparable results to other approaches without the need of expensive tuning of the loss function or additional information about the scene, such as 3D point clouds, which might not always be available. CGAPoseNet, however, like several approaches in the literature, only learns to predict motor coefficients, and it is unaware of the mathematical space in which predictions sit in and of their geometrical meaning. By leveraging recent advances in Geometric Deep Learning, we modify CGAPoseNet with a GCAN: proposals of possible motor coefficients associated with a camera frame are obtained from the InceptionV3 backbone, and the GCAN downsamples them to a single motor through a sequence of layers that work in G_ 4, 0. The network is hence geometry-aware, has multivector-valued inputs, weights and biases and preserves the grade of the objects that it receives in input. CGAPoseNet+ GCAN has almost 4 million fewer trainable parameters, it reduces the average rotation error by 41% and the average translation error by 8.8% compared to CGAPoseNet. Similarly, it reduces rotation and translation errors by 32.6% and 19.9%, respectively, compared to the best performing PoseNet strategy. CGAPoseNet+ GCAN reaches the state-of-the-art results on 13 commonly employed datasets. To the best of our knowledge, it is the first experiment in GCANs applied to the problem of camera pose regression. Y1 - 2024 U6 - https://doi.org/10.1109/WACV57701.2024.00646 SP - 6593 EP - 6603 PB - IEEE ER - TY - INPR A1 - Pepe, Alberto A1 - Lasenby, Joan A1 - Buchholz, Sven T1 - STAResNet: A Network in Spacetime Algebra to solve Maxwell's PDEs T2 - arXiv N2 - We introduce STAResNet, a ResNet architecture in Spacetime Algebra (STA) to solve Maxwell's partial differential equations (PDEs). Recently, networks in Geometric Algebra (GA) have been demonstrated to be an asset for truly geometric machine learning. In [1], GA networks have been employed for the first time to solve partial differential equations (PDEs), demonstrating an increased accuracy over real-valued networks. In this work we solve Maxwell's PDEs both in GA and STA employing the same ResNet architecture and dataset, to discuss the impact that the choice of the right algebra has on the accuracy of GA networks. Our study on STAResNet shows how the correct geometric embedding in Clifford Networks gives a mean square error (MSE), between ground truth and estimated fields, up to 2.6 times lower than than obtained with a standard Clifford ResNet with 6 times fewer trainable parameters. STAREsNet demonstrates consistently lower MSE and higher correlation regardless of scenario. The scenarios tested are: sampling period of the dataset; presence of obstacles with either seen or unseen configurations; the number of channels in the ResNet architecture; the number of rollout steps; whether the field is in 2D or 3D space. This demonstrates how choosing the right algebra in Clifford networks is a crucial factor for more compact, accurate, descriptive and better generalising pipelines. Y1 - 2024 UR - https://arxiv.org/pdf/2408.13619 SP - 1 EP - 17 PB - arXiv ER - TY - CHAP A1 - Pepe, Alberto A1 - Lasenby, Joan A1 - Buchholz, Sven T1 - GA-ReLU: an activation function for Geometric Algebra Networks applied to 2D Navier-Stokes PDEs T2 - ICLR 2024 Workshop on AI4DifferentialEquations In Science, Wien, Österreich N2 - Many differential equations describing physical phenomena are intrinsically geometric in nature. It has been demonstrated how this geometric structure of data can be captured effectively through networks sitting in Geometric Algebra (GA) that work with multivectors, making them suitable candidates to solve differential equations. GA networks however, are still mostly uncharted territory. In this paper we focus on non-linearities, since applying them to multivectors is not a trivial task: they are generally applied in a point-wise fashion over each real-valued component of a multivector. This approach discards interactions between different elements of the multivector input and compromises the geometric nature of GA networks. To bridge this gap, we propose GA-ReLU, a GA approach to the rectified linear unit (ReLU), and show how it can improve the solution of Navier-Stokes PDEs. Y1 - 2024 UR - https://openreview.net/pdf?id=T54Hn1e98P SP - 1 EP - 6 ER - TY - JOUR A1 - Pfeiffer, Thomas A1 - Dilip, Khadse Himanshu A1 - Suryawanshi, Raunak A1 - Kishnamurthy, Gnana Swaroop A1 - Téllez, Batcheva Domínguez A1 - Creutzburg, Reiner A1 - Hartmann, Michael T1 - Airflow visualization and air purifier positioning optimization in potentially COVID-19 contaminated classrooms JF - Electronic Imaging KW - Covid-19 KW - aeroso KW - airflow visualization Y1 - 2021 U6 - https://doi.org/10.2352/ISSN.2470-1173.2021.3.MOBMU-039 SN - 2470-1173 SP - 39-1 EP - 39-15 PB - Society for Imaging Science and Technology CY - Springfield, VA ER - TY - JOUR A1 - Pilgermann, Michael A1 - Bocklisch, Thomas A1 - Creutzburg, Reiner T1 - Conception and implementation of a course for professional training and education in the field of IoT and smart home security JF - Electronic Imaging KW - Internet of Things KW - IoT KW - Smart Home KW - Smart Home Security KW - Cybersecurity KW - Connected Home Y1 - 2020 U6 - https://doi.org/10.2352/ISSN.2470-1173.2020.3.MOBMU-277 SN - 2470-1173 SP - 277-1 EP - 277-8 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - Pilgermann, Michael A1 - Werth, Sören A1 - Creutzburg, Reiner T1 - Towards sector specific security operation JF - Electronic Imaging KW - cybersecurity KW - information security KW - network security KW - IT security KW - Security Information Center KW - SOC KW - Security Information and Event Management KW - SIEM KW - security monitoring KW - health care KW - critical infrastructures KW - hospitals KW - HL7 KW - MLLP Y1 - 2020 U6 - https://doi.org/10.2352/ISSN.2470-1173.2020.3.MOBMU-254 SN - 2470-1173 SP - 254-1 EP - 254-10 PB - Society for Imaging Science and Technology CY - Springfield, VA ER - TY - JOUR A1 - Prinz, Alexander A1 - Orlowski, Katja A1 - Beck, Eberhard A1 - Witte, Kerstin T1 - Serious Games for Seniors With Dementia: A Pilot Study JF - Journal of Applied Gerontology N2 - Dementia, marked by cognitive decline, significantly impacts daily life. With global prevalence rising, traditional treatments manage symptoms but have side effects and offer no cure. Non-pharmacological interventions, like serious games, are gaining importance. This study assesses the feasibility and benefits of serious games for people with mild to moderate dementia over a 10-week intervention. Sixty-one patients were recruited, with 35 completing the study. The intervention included six games focusing on physical and cognitive training. Outcome measures were motor function, cognitive assessments, quality of life, and depression. Results showed significant improvements in dynamic balance (p = .013) but no significant changes in other measures. The findings suggest that serious games are feasible and can improve motor functions like balance. However, short intervention periods may limit their impact on cognitive function and quality of life. Longer interventions and personalized game designs are recommended for greater benefits. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:522-opus4-33440 VL - 2025 SP - 1 EP - 11 PB - SAGE ER -