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arXiv 2018年

作者： Bohn, Bastian Griebel, Michael Oettershagen, Jens Institute for Numerical Simulation University of Bonn Endenicher Allee 19b Bonn53115 Fraunhofer Center for Machine Learning Fraunhofer Institute for Algorithms and Scien-tific Computing Scai Schloss Birlinghoven Sankt Augustin53754 Germany

For low-dimensional data sets with a large amount of data points, standard kernel methods are usually not feasible for regression anymore. Besides simple linear models or involved heuristic deep learning models, grid-based discretizations of larger (kernel) model classes lead to algo-rithms, which naturally scale linearly in the amount of data points. For moderate-dimensional or high-dimensional regression tasks, these grid-based discretizations suffer from the curse of dimensionality. Here, sparse grid methods have proven to circumvent this problem to a large extent. In this context, space-and dimension-Adaptive sparse grids, which can detect and exploit a given low effective dimensionality of nominally high-dimensional data, are particularly successful. They nevertheless rely on an axis-Aligned structure of the solution and exhibit issues for data with predominantly skewed and rotated coordinates. In this paper we propose a preprocessing approach for these adaptive sparse grid algorithms that determines an optimized, problem-dependent coordinate system and, thus, reduces the effective dimensionality of a given data set in the ANOVA sense. We provide numerical examples on synthetic data as well as real-world data to show how an adaptive sparse grid least squares algorithm benefits from our preprocessing *** Codes 68T05 Copyright © 2018, The Authors. All rights reserved.

关键词： Analysis of variance (ANOVA)

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App-based augmented reality to assess cognitive impairment in early Alzheimer’s disease

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Alzheimer's & Dementia 2023年第S14期19卷

作者： Anna-Katharine Brem Marijn Muurling Casper de Boer Jelena Curcic Alankar Atreya Neva Coello Pauline Conde Holger Fröhlich Margarita Grammatikopoulou Chris Hinds Ioulietta Lazarou Manuel Lentzen Robbert Harms Maximilian Bügler Vaibhav A Narayan Rouba Kozak Spiros Nikolopoulos Srinivasan Vairavan Pieter Jelle Visser Gayle Wittenberg Dag Aarsland RADAR-AD Consortium King’s College London London United Kingdom University Hospital of Old Age Psychiatry University of Bern Bern Switzerland Correspondece Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Noord-Holland Netherlands Novartis Institutes for BioMedical Research Basel Switzerland University of Oxford Oxford United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Centre for Research & Technology Hellas Thessaloniki Greece Altoida Inc. Washington DC USA Davos Alzheimers Collaborative Wayne PA USA Novartis Cambridge MA USA Janssen Research and Development LLC Titusville NJ USA Alzheimer Center Amsterdam Department of Neurology Amsterdam University Medical Center Amsterdam Netherlands Maastricht University Maastricht Netherlands Stavanger University Hospital Stavanger Norway

Background Augmented reality apps merge real world with virtual experiences and can be used to remotely assess complex instrumental activities of daily living (iADL) that are affected early in Alzheimer’s disease (AD). Our aim was to compare standard clinical measures with an augmented reality app to assess iADL that are related to memory and spatial navigation in early AD and its feasibility in the home-setting. Method We administered an augmented reality app (Altoida Inc., Washington DC, USA) in an on-going cross-sectional study (RADAR-AD: Remote Assessment of Disease and Relapse – Alzheimer’s Disease) in three groups: 1) amyloid negative healthy controls (HC, N = 49); and amyloid positive 2) preclinical AD (PreAD, N = 17); and 3) prodromal AD (ProAD, N = 29) (Table 1). Altoida’s research algorithm DNS-MCI (Digital Neuro Signature) produces the outcome of a machine learning model trained to identify cognitively normal individuals from those with cognitive impairment). DNS-MCI reflects performance in app-based tasks assessing memory and visuo-spatial function (placing and finding virtual objects, fire drill simulation) further including attention and motor performance (reaction time, finger tapping, navigational trajectory). At baseline, app-based tasks were performed in the clinic together with a standard neuropsychological assessment and iADL questionnaires (Figure 1). Participants were furthermore given the option of using Altoida in the home environment. Result The DNS-MCI score could significantly distinguish HC and PreAD participants from the ProAD group and was correlated with all neuropsychological tests and iADL questionnaires (Figures 1 and 2). Participants used the app on average 3-4 times at home (Table 1). Baseline in-clinic assessments were strongly correlated with at-home assessments (r = 0.53, p <.001). Conclusion App-based augmented reality tasks are applicable in the home setting and successful in capturing cognitive impairment in early AD. Future

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Ten new insights in climate science 2024

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One Earth 2025年

作者： Schaeffer, Roberto Schipper, E. Lisa F. Ospina, Daniel Mirazo, Paula Alencar, Ane Anvari, Mehrnaz Artaxo, Paulo Biresselioglu, Mehmet Efe Blome, Tanja Boeckmann, Melanie Brink, Ebba Broadgate, Wendy Bustamante, Mercedes Cai, Wenju Canadell, Josep G. Cardinale, Roberto Chidichimo, Maria Paz Ditlevsen, Peter Eicker, Ursula Feron, Sarah Fikru, Mahelet G. Fuss, Sabine Gaye, Amadou T. Gustafsson, Örjan Harring, Niklas He, Cheng Hebden, Sophie Heilemann, Adrian Hirota, Marina Janardhanan, Nandakumar Juhola, Sirkku Jung, Tae Yong Kejun, Jiang Kilkiș, Şiir Kumarasinghe, Nilushi Lapola, David Lee, June-Yi Levis, Carolina Lusambili, Adelaide Maasakkers, Joannes D. MacIntosh, Claire Mahmood, Jemilah Mankin, Justin S. Marchegiani, Pía Martin, Maria Mukherji, Aditi Muñoz-Erickson, Tischa A. Niazi, Zeenat Nyangon, Joseph Pandipati, Santosh Perera, Amarasinghage T.D. Persad, Geeta Persson, Åsa Redman, Aaron Riipinen, Ilona Rockström, Johan Roffe, Sarah Roy, Joyashree Sakschewski, Boris Samset, Bjørn H. Schlosser, Peter Sharifi, Ayyoob Shih, Wan-Yu Sioen, Giles B. Sokona, Youba Stammer, Detlef Suk, Sunhee Thiam, Djiby Thompson, Vikki Tullos, Erin van Westen, René M. Vargas Falla, Ana Maria Vecellio, Daniel J. Worden, John Wu, Henry C. Xu, Chi Yang, Yang Zachariah, Mariam Zhang, Zhen Ziervogel, Gina Federal University of Rio de Janeiro Rio de Janeiro Brazil University of Bonn Bonn Germany Future Earth Secretariat Stockholm Sweden Arizona State University Tempe AZ United States Amazon Environmental Research Institute (IPAM) Brasilia Brazil Fraunhofer Institute for Algorithms and Scientific Computing SCAI St. Augustin Germany Potsdam Institute for Climate Impact Research (PIK) Potsdam Germany University of São Paulo São Paulo Brazil İzmir University of Economics İzmir Turkey Climate Service Center Germany (GERICS) Helmholtz-Zentrum Hereon Hamburg Germany University of Bremen Bremen Germany Lund University Lund Sweden University of Brasilia Brasilia Brazil Ocean University of China Qingdao China Xiamen University Xiamen China Commonwealth Scientific and Industrial Research Organisation (CSIRO) Environment Canberra ACT Australia University College London London United Kingdom National Scientific and Technical Research Council (CONICET) Buenos Aires Argentina National University of San Martin Buenos Aires Argentina University of Copenhagen Copenhagen Denmark Concordia University Montreal QC Canada University of Groningen Groningen Netherlands Missouri University of Science and Technology Rolla MO United States Mercator Research Institute on Global Commons and Climate Change (MCC) Berlin Germany Humboldt-University of Berlin Berlin Germany Cheikh Anta Diop University Dakar Senegal Stockholm University Stockholm Sweden University of Gothenburg Gothenburg Sweden Institute of Epidemiology Helmholtz Zentrum München—German Research Center for Environmental Health (GmbH) Neuherberg Germany European Space Agency (ESA) - European Centre for Space Applications and Telecommunications (ECSAT) Oxford United Kingdom Federal University of Santa Catarina Florianopolis Brazil Institute for Global Environmental Strategies (IGES) Hayama Japan University of Helsinki Helsinki Finland Yonsei University Graduate Seoul South Korea Chinese Academy of Macroeconomic

The years 2023 and 2024 were characterized by unprecedented warming across the globe, underscoring the urgency of climate action. Robust science advice for decision makers on subjects as complex as climate change requires deep cross- and interdisciplinary understanding. However, navigating the ever-expanding and diverse peer-reviewed literature on climate change is enormously challenging for individual researchers. We elicited expert input through an online questionnaire (188 respondents from 45 countries) and prioritized 10 key advances in climate-change research with high policy relevance. The insights span a wide range of areas, from changes in methane and aerosol emissions to the factors shaping citizens’ acceptance of climate policies. This synthesis and communications effort forms the basis for a science-policy report distributed to party delegations ahead of the 29th session of the Conference of the Parties (COP29) to inform their positions and arguments on critical issues, including heat-adaptation planning, comprehensive mitigation strategies, and strengthened governance in energy-transition minerals value chains. © 2025 The Authors

关键词： adaptation climate policy climate science governance just transition mitigation resilience science policy

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A knowledge-based surrogate modeling approach for cup drawing with limited data

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IOP Conference Series: Materials Science and Engineering 2019年第1期651卷

作者： L Morand D Helm R Iza-Teran J Garcke Fraunhofer Institute for Mechanics of Materials IWM Wöhlerstraße 11 Freiburg 79108 Germany Fraunhofer Center for Machine Learning and Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin 53754 Germany Institute for Numerical Simulations University of Bonn Endenicher Allee 19b Bonn 53115 Germany

To predict the quality of a process outcome with given process parameters in real-time, surrogate models are often adopted. A surrogate model is a statistical model that interpolates between data points obtained either by process measurements or deterministic models of the process. However, in manufacturing processes the amount of useful data is often limited, and therefore setting up a sufficiently accurate surrogate model is challenging. The present contribution shows how to handle limited data in a surrogate modeling approach using the example of a cup drawing process. The purpose of the surrogate model is to classify the quality of the drawn cup and to predict its final geometry. These classification and regression tasks are solved via machine learning methods. The training data is sampled on a relatively wide range varying three parameters of a finite element simulation, namely sheet metal thickness, blank holder force, and friction. The geometrical features of the cup are extracted using domain knowledge. Besides this knowledge-based approach, an outlook is given for a data-driven surrogate modeling approach.

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From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine

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EPMA Journal 2014年第1期5卷 1-1页

作者： Erfan Younesi Martin Hofmann-Apitius Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven Sankt Augustin Germany Rheinische Friedrich-Wilhelms-Universität Bonn Bonn-Aachen International Center for IT Bonn Germany

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Improving Distantly Supervised Extraction of Drug-Drug and Protein-Protein Interactions 12

Improving Distantly Supervised Extraction of Drug-Drug and P...

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Conference of The European Chapter of The Association for Computational Linguistics

作者： Tamara Bobic Roman Klinger Philippe Thomas Martin Hofmann-Apitius Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 53754 Sankt Augustin Germany Bonn-Aachen Center for Information Technology Dahlmannstrasse 2 53113 Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 53754 Sankt Augustin Germany Computer Science Institut Humboldt-Universitaet Unter den Linden 6 10099 Berlin Germany

ISBN: (纸本)9781622764907

Relation extraction is frequently and successfully addressed by machine learning methods. The downside of this approach is the need for annotated training data, typically generated in tedious manual, cost intensive work. Distantly supervised approaches make use of weakly annotated data, like automatically annotated corpora. Recent work in the biomedical domain has applied distant supervision for protein-protein interaction (PPI) with reasonable results making use of the IntAct database. Such data is typically noisy and heuristics to filter the data are commonly applied. We propose a constraint to increase the quality of data used for training based on the assumption that no self-interaction of real-world objects are described in sentences. In addition, we make use of the University of Kansas Proteomics Service (KUPS) database. These two steps show an increase of 7 percentage points (pp) for the PPI corpus AIMed. We demonstrate the broad applicability of our approach by using the same workflow for the analysis of drug-drug interactions, utilizing relationships available from the drug database DrugBank. We achieve 37.31 % in F_1 measure without manually annotated training data on an independent test set.

关键词： Protein-Protein Interaction Training data pulse position indicator Drug Interactions Heuristics Manual handling Database

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Compensation of pressure peaks in PWK-type hydraulic pumps

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Key Engineering Materials 2011年 490卷 33-44页

作者： Osiecki, Leszek Patrosz, Piotr Zawistowski, Tomasz Landvogt, Bettina Piechna, Janusz Zyliñski, Bartek Gdansk University of Technology ul. Narutowicza 11/12 80-233 Gdansk Poland Space Research Center Polish Academy of Sciences ul. Bartycka 18A 00-716 Warszawa Poland Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 53754 Sankt Augustin Germany Warsaw University of Technology Pl. Politechniki 1 00-661 Warszawa Poland Bandak Engineering Postboks 1505 Ålesund Norway

Hydraulic axial pumps equipped with cam-driven commutation unit (PWK pumps) proved their high efficiency up to 55 MPa and ability to work self-sucking, even at high speed. Displacement of PWK pump may easily be changed by moving its control cam. Full discharge from hydrostatic forces makes such control possible by direct action of a low-energy actuator like proportional electromagnet or stepping motor. That eliminates heavy and costly hydraulic servomechanism, necessary in other variable displacement machines. Such a control mechanism was positively evaluated in prototype tests in Department of Hydraulics and Pneumatics of Gdañsk University of Technology. The new commutation unit generates however harmful pressure peaks. The paper presents the compensation method for pressure peaks by use of the elastic compensation chamber. It shows also results of multidisciplinary FEA and CFD analysis necessary to properly design the compensation chamber. © 2012 Trans Tech Publications, Switzerland.

关键词： Stepping motors

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