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SSRN 2025年

作者： Anand, Astha Shen, Jian Cremers, Armin Bernd Jacobs, Marc Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 1 North Rhine-Westphalia Sankt Augustin53757 Germany RPU Rhenish Plant Power Envirotech Co. Ltd. Bonn-Aachen International Center for Information Technology Friedrich-Hirzebruch-Allee 5 North Rhine-Westphalia Bonn53115 Germany

As sustainable agricultural practices gain importance, the need for intelligent pest control decision-making has grown. This paper introduces SEEDS: Similarity-based Expert Embedding Decision System, a Retrieval-Augmented Generation (RAG) based agricultural question-answering (QA) system. It is built upon a domain-specific knowledge graph (KG), representing Cedar Apple Rust disease, its host and causative agents, plant defense molecules against apple rust infection, and various pesticides. Utilizing the OpenAI embedding model, the system generates embeddings for user queries and KG data, employing similarity metrics to rank KG entries, facilitating accurate and relevant pest control recommendations. SEEDS is a promising niche AI tool in plant protection, setting the stage for scalable, extensible QA frameworks in precision agriculture. The results signify not only a step forward in agricultural expert systems but also highlight the potential for expanding this approach to other crops and pests, marking a substantial advancement in the use of AI for agricultural pest control. © 2025, The Authors. All rights reserved.

关键词： Fruits

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Generative models for the transfer of knowledge in seismic interpretation with deep learning

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Leading Edge 2021年第7期40卷 534-542页

作者： Durall, Ricard Tschannen, Valentin Ettrich, Norman Keuper, Janis Fraunhofer Institute for Industrial Mathematics Kaiserslautern Germany Interdisciplinary Center for Scientific Computing University of Heidelberg Heidelberg Germany Fraunhofer Research Center for Machine Learning Germany Institute for Machine Learning and Analytics Offenburg University Offenburg Germany

Interpreting seismic data requires the characterization of a number of key elements such as the position of faults and main reflections, presence of structural bodies, and clustering of areas exhibiting a similar amplitude versus angle response. Manual interpretation of geophysical data is often a difficult and time-consuming task, complicated by lack of resolution and presence of noise. In recent years, approaches based on convolutional neural networks have shown remarkable results in automating certain interpretative tasks. However, these state-of-the-art systems usually need to be trained in a supervised manner, and they suffer from a generalization problem. Hence, it is highly challenging to train a model that can yield accurate results on new real data obtained with different acquisition, processing, and geology than the data used for training. In this work, we introduce a novel method that combines generative neural networks with a segmentation task in order to decrease the gap between annotated training data and uninterpreted target data. We validate our approach on two applications: the detection of diffraction events and the picking of faults. We show that when transitioning from synthetic training data to real validation data, our workflow yields superior results compared to its counterpart without the generative network. © 2021 Society of Exploration Geophysicists. All rights reserved.

关键词： Convolutional neural networks

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Predicting dementia in people with Parkinson’s disease

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npj Parkinson's Disease 2025年第1期11卷 1-10页

作者： Aborageh, Mohamed Hähnel, Tom Martins Conde, Patricia Klucken, Jochen Fröhlich, Holger Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Augustin Sankt 53757 Germany Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Centre Hospitalier de Luxembourg (CHL) Luxembourg Luxembourg Bonn-Aachen International Center for Information Technology (B-IT) Rheinische Friedrich-Wilhelms-Universität Bonn Bonn 53115 Germany

Parkinson’s disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson’s dementia (PDD) post-diagnosis, further validated by data from the Parkinson’s Progression Markers Initiative (PPMI) cohort. Using Shapley Additive Explanation (SHAP) and Bayesian Network structure learning, we analyzed interactions among genetic predisposition, comorbidities, lifestyle, and environmental factors. We concluded that genetic predisposition is the dominant factor, with significant influence from comorbidities. Additionally, we employed Mendelian randomization (MR) to establish potential causal links between hypertension, type 2 diabetes, and PDD, suggesting that managing blood pressure and glucose levels in Parkinson’s patients may serve as a preventive strategy. This study identifies risk factors for PDD and proposes avenues for prevention. © The Author(s) 2025.

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PEMT: a patent enrichment tool for drug discovery (vol 39, btac716, 2023)

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BIOINFORMATICS 2023年第12期39卷 btac716-btac716页

作者： Gadiya, Yojana Zaliani, Andrea Gribbon, Philip Hofmann-Apitius, Martin Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) Hamburg 22525 Germany Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD) Frankfurt 60590 Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin 53754 Germany Bonn-Aachen International Center for Information Technology (B-IT) University of Bonn Bonn 53113 Germany

Motivation Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public *** Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent *** and implementation PEMT is an open-source Python tool and its source code and PyPi package are available at https://***/fraunhofer-ITMP/PEMT and https://***/project/PEMT/, *** information Supplementary data are available at Bioinformatics online.

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On the effects of biased quantum random numbers on the initialization of artificial neural networks

arXiv

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arXiv 2021年

作者： Heese, Raoul Wolter, Moritz Mücke, Sascha Franken, Lukas Piatkowski, Nico Fraunhofer Center for Machine Learning and Fraunhofer Institute for Industrial Mathematics ITWM Germany Fraunhofer Center for Machine Learning and Fraunhofer Institute for Algorithms and Scientific Computing SCAI Germany Artificial Intelligence Group TU Dortmund University Germany Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Germany

Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum (NISQ) devices. A common property of quantum computers is that they can exhibit instances of true randomness as opposed to pseudo-randomness obtained from classical systems. Investigating the effects of such true quantum randomness in the context of machine learning is appealing, and recent results vaguely suggest that benefits can indeed be achieved from the use of quantum random numbers. To shed some more light on this topic, we empirically study the effects of hardware-biased quantum random numbers on the initialization of artificial neural network weights in numerical experiments. We find no statistically significant difference in comparison with unbiased quantum random numbers as well as biased and unbiased random numbers from a classical pseudo-random number generator. The quantum random numbers for our experiments are obtained from real quantum hardware. Copyright © 2021, The Authors. All rights reserved.

关键词： Random number generation

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Computational Life Sciences 1

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丛书名： Studies in Big Data

1000年

作者： Jens Dörpinghaus Sebastian Schaaf Vera Weil Alexander Apke

ISBN: (数字)9783031084119

ISBN: (纸本)9783031084102;9783031084133

This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-solving and data analysis often depend on biological expertise combined with technical skills in order to generate, manage and efficiently analyse big data. These technical skills can easily be enhanced by good theoretical foundations, developed from well-chosen practical examples and inspiring new strategies. This is the innovative approach of Computational Life Sciences-Data Engineering and Data Mining for Life Sciences: We present basic concepts, advanced topics and emerging technologies, introduce algorithm design and programming principles, address data mining and knowledge discovery as well as applications arising from real projects. Chapters are largely independent and often flanked by illustrative examples and practical advise.

关键词： Data Engineering Bioinformatics Big Data Computational Intelligence

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Decision Support by Interpretable machine learning in Acoustic Emission Based Cutting Tool Wear Prediction

Decision Support by Interpretable Machine Learning in Acoust...

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： A. Schmetz C. Vahl Z. Zhen D. Reibert S. Mayer D. Zontar J. Garcke C. Brecher Fraunhofer-Institute for Production Technology IPT Aachen Germany Fraunhofer-Center for Machine Learning and Fraunhofer-Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Germany

ISBN: (纸本)9781665437721

Predictive maintenance is a prominent and active field for applications of machine learning in industry in recent years. The health and wear of equipment directly influences the productivity and quality of the production process. Especially in ultra-precision manufacturing, tool wear has a major impact on the achievable quality while the wear itself cannot be measured directly in-process. In this paper we present a machine learning-based classification of the tool wear in-process using acoustic emission sensors. To increase the interpretability of the process – to open the black box model – we apply a feature importance analysis and use the obtained feature importances to provide augmented data representations to the users. These representations increase the transparency of the model's decision process and assist the users in validating the model's decisions and gain new insight into the phenomenon of tool wear itself.

关键词： Productivity Industries Engineering management machine learning Acoustic emission Industrial engineering Sensors

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Canonical convolutional neural networks

arXiv

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arXiv 2022年

作者： Veeramacheneni, Lokesh Wolter, Moritz Klein, Reinhard Garcke, Jochen Department of Computer Science Hochschule Bonn-Rhein-Sieg Fraunhofer Center for Machine Learning SCAI Germany High Performance Computing and Analytics Lab University of Bonn Fraunhofer Center for Machine Learning SCAI Germany Department of Computer Science University of Bonn Germany Fraunhofer Center for Machine Learning SCAI Institute for Numerical Simulation University of Bonn Germany

We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network weights in the decomposed form, where scale weights are optimized separately for each mode. Additionally, similarly to weight normalization, we include a global scaling parameter. We study the initialization of the canonical form by running the power method and by drawing randomly from Gaussian or uniform distributions. Our results indicate that we can replace the power method with cheaper initializations drawn from standard distributions. The canonical re-parametrization leads to competitive normalization performance on the MNIST, CIFAR10, and SVHN data sets. Moreover, the formulation simplifies network compression. Once training has converged, the canonical form allows convenient model-compression by truncating the parameter sums. © 2022, CC BY-NC-SA.

关键词： Convolutional neural networks

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Wavelet-Packets for Deepfake Image Analysis and Detection

arXiv

引用

arXiv 2021年

作者： Wolter, Moritz Blanke, Felix Heese, Raoul Garcke, Jochen High Performance Computing & Analytics Lab Universität Bonn Germany Fraunhofer SCAI University of Bonn Germany Fraunhofer Center for Machine Learning Fraunhofer ITWM Germany Institute for Numerical Simulation University of Bonn Germany Fraunhofer Center for Machine Learning Fraunhofer SCAI Germany

As neural networks become able to generate realistic artificial images, they have the potential to improve movies, music, video games and make the internet an even more creative and inspiring place. Yet, the latest technology potentially enables new digital ways to lie. In response, the need for a diverse and reliable method toolbox arises to identify artificial images and other content. Previous work primarily relies on pixel-space convolutional neural networks or the Fourier transform. Synthesized fake image analysis and detection methods based on a multi-scale wavelet representation, localized in both space and frequency, have been absent thus far. The wavelet transform conserves spatial information to a degree, allowing us to present a new analysis. Comparing the wavelet coefficients of real and fake images significant differences are identified, where this representation also allows further interpretations. Additionally, this paper proposes to learn a model for the detection of synthetic images based on the wavelet-packet representation of natural and generative adversarial neural network (GAN)-generated images. Our forensic classifiers exhibit competitive or improved performance at small network sizes, as we demonstrate on the Flickr Faces High Quality (FFHQ), Large-scale Celeb Faces Attributes (CelebA) and Large-scale Scene UNderstanding (LSUN) source identification problems. Furthermore, we study the binary Face Forensics++ (ff++) fake-detection problem. © 2021, CC BY-NC-SA.

关键词： Fake detection

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ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs 13

ProtSTonKGs: A Sophisticated Transformer Trained on Protein ...

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13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022

作者： Balabin, Helena Hoyt, Charles Tapley Gyori, Benjamin M. Bachman, John Kodamullil, Alpha Tom Hofmann-Apitius, Martin Domingo-Fernández, Daniel Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin53757 Germany Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Fraunhofer Center for Machine Learning Germany Enveda Biosciences BoulderCO80301 United States

While most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowl- edge graphs, their union can better exploit the advantages of such ap- proaches, ultimately improving representations of biology. Using mul- timodal transformers for such purposes can improve performance on context dependent classification tasks, as demonstrated by our previ- ous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce Prot- STonKGs, a transformer aimed at learning all-encompassing representa- tions of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark Prot- STonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of infor- mation, paving the foundation for future approaches that use multiple modalities for biomedical applications. Copyright © 2022 for this paper by its authors.

关键词： Natural language processing systems

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