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arXiv 2019年

作者： von Rueden, Laura Mayer, Sebastian Beckh, Katharina Georgiev, Bogdan Giesselbach, Sven Heese, Raoul Kirsch, Birgit Pfrommer, Julius Pick, Annika Ramamurthy, Rajkumar Walczak, Michal Garcke, Jochen Bauckhage, Christian Schuecker, Jannis The Fraunhofer IAIS Institute for Intelligent Analysis and Information Systems Sankt Augustin53757 Germany The Fraunhofer SCAI Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany The Fraunhofer ITWM Institute for Industrial Mathematics Kaiserslautern67663 Germany The Fraunhofer IOSB Institute for Optronics System Technologies and Image Exploitation Karlsruhe76131 Germany

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning. © 2019, CC BY.

关键词： Surveys

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A Graph-Theoretic Approach to the Train Marshalling Problem

A Graph-Theoretic Approach to the Train Marshalling Problem

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Federated Conference on Computer Science and Information Systems

作者： Jens Dorpinghaus Rainer Schrader Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Institut fur Informatik Universitat zu Koln

ISBN: (纸本)9781538623718

Rearranging cars of an incoming train in a hump yard is a widely discussed topic. We focus on the train marshalling problem where the incoming cars of a train are distributed to a certain number of sorting tracks. When pulled out again to build the outgoing train, cars sharing the same destination should appear consecutively. The goal is to minimize the number of sorting tracks. We suggest a graph-theoretic approach for this NP-complete problem. The idea is to partition an associated directed graph into what we call pseudochains of minimum length. We describe a greedy-type heuristic to solve the partitioning problem which, on random instances, performs better than the known heuristics for the train marshalling problem.

关键词： TRAIN Cars on-the-job training particle tracks Track tracks Tracks Heuristics REARRANGING grade Nondeterministic polynomial-time hard PULL-OUT

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Evaluating digital device technology in Alzheimer’s disease via artificial intelligence

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Alzheimer's & Dementia 2021年第S6期17卷

作者： Meemansa Sood Robbert Harms Maximilian Bügler Ioannis Tarnanas Neva Coello Holger Fröhlich Bonn-Aachen International Center for IT (b-it) Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Altoida Inc. Houston TX USA Altoida AG Hirschengraben Lucerne Switzerland Global Brain Health Institute Trinity College Dublin Ireland Hellenic Initiative Against Alzheimer's Disease Johns Hopkins Precision Medicine Center Baltimore MD USA Novartis Basel Switzerland

Background Digital Measures (DMs) derived from mobile devices and smartphone applications have received a strongly increasing attention during the last years, because they could allow for an accurate, quantitative monitoring of disease symptoms, even outside clinics. In addition, DMs may help to diagnose Alzheimer’s Disease (AD) in a pre-symptomatic stage and thus increase the success chances of therapeutic interventions. However, before any use in clinical routine, DMs have to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. In this regard the IMI project RADAR-AD ( *** ) has the ambition to evaluate a broad panel of digital technologies with respect to their potential for early disease diagnosis while focusing on functional activities of daily living. Method An example of a panel of digital technology RADAR-AD uses is a smartphone based virtual reality game resulting into an assessment of cognitive impairment. In our work we analyzed connections between digital readouts and cognitive features like MMSE (Mini Mental State Examination) via one of our recently developed Artificial Intelligence (AI) approaches called Variational Autoencoder Modular Bayesian Networks (VAMBN). Going one step further we also tested the possibility to accurately predict MMSE scores from DMs and vice versa via machine learning. Based on this finding we then simulated DMs within the ADNI cohort and re-ran VAMBN. Result Application of VAMBN on the data from virtual reality game resulted into a network comprising DMs, MMSE sub-item scores and demographic features (Figure 1). It thus allowed to disentangle and quantify the relationship between DMs and established clinical scores. The simulation of DM’s and application of VAMBN in the ADNI cohort allowed us to further predict connections of DMs with FAQ (Functional Activity Questionnaire) and even molecular mechanisms. Conclusion Our results indicate t

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Distributional property testing in a quantum world

arXiv

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arXiv 2019年

作者： Gilyén, András Liy, Tongyang QuSoft CWI University of Amsterdam Netherlands Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams Program

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. In particular, we give fast quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. The distributions can be either classical or quantum, however our quantum algorithms require coherent quantum access to a process preparing the samples. Our results build on the recent technique of quantum singular value transformation, combined with more standard tricks such as divide-and-conquer. The presented approach is a natural fit for distributional property testing both in the classical and the quantum case, demonstrating the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling;for classical distributions our algorithms significantly improve the precision dependence of some earlier results. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

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Pseudoentropic derivation of the regularized lattice Boltzmann method

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Physical Review E 2019年第2期100卷 023302-023302页

作者： Andreas Krämer Dominik Wilde Knut Küllmer Dirk Reith Holger Foysi Institute of Technology Resource and Energy-efficient Engineering (TREE) Bonn-Rhein-Sieg University of Applied Sciences Grantham-Allee 20 53757 Sankt Augustin Germany National Heart Lung and Blood Institute National Institutes of Health Bethesda Maryland 20892 United States Department of Mechanical Engineering University of Siegen Paul-Bonatz-Straße 9-11 57076 Siegen-Weidenau Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 53754 Sankt Augustin Germany

The lattice Boltzmann method (LBM) facilitates efficient simulations of fluid turbulence based on advection and collision of local particle distribution functions. To ensure stable simulations on underresolved grids, the collision operator must prevent drastic deviations from local equilibrium. This can be achieved by various methods, such as the multirelaxation time, entropic, quasiequilibrium, regularized, and cumulant schemes. Complementing a part of a unified theoretical framework of these schemes, the present work presents a derivation of the regularized lattice Boltzmann method (RLBM), which follows a recently introduced entropic multirelaxation time LBM by Karlin, Bösch, and Chikatamarla (KBC). It is shown that both methods can be derived by locally maximizing a quadratic Taylor expansion of the entropy function. While KBC expands around the local equilibrium distribution, the RLBM is recovered by expanding entropy around a global equilibrium. Numerical tests were performed to elucidate the role of pseudoentropy maximization in these models. Simulations of a two-dimensional shear layer show that the RLBM successfully reproduces the largest eddies even on a 16×16 grid, while the conventional LBM becomes unstable for grid resolutions of 128×128 and lower. The RLBM suppresses spurious vortices more effectively than KBC. In contrast, simulations of the three-dimensional Taylor-Green and Kida vortices show that KBC performs better in resolving small scale vortices, outperforming the RLBM by a factor of 1.8 in terms of the effective Reynolds number.

关键词： Flow instability Shear flows Turbulence

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Assessment of RMTs for Discriminating Stages of Alzheimer’s Disease

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Alzheimer's & Dementia 2023年第S22期19卷

作者： Manuel Lentzen Srinivasan Vairavan Jelena Curcic Alankar Atreya Chris Hinds Marijn Muurling Pauline Conde Margarita Grammatikopoulou Ioulietta Lazarou Spiros Nikolopoulos Neva Coello Casper de Boer Dag Aarsland Anna-Katharine Brem Holger Fröhlich RADAR-AD Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Bonn-Aachen International Center for IT (b-it) Bonn Germany Janssen Research and Development LLC Titusville NJ USA Novartis Institutes for BioMedical Research Basel Switzerland University of Oxford Oxford United Kingdom VU University Medical Center Amsterdam Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Netherlands King’s College London London United Kingdom Centre for Research & Technology Hellas Thessaloniki Greece Amsterdam Neuroscience Neurodegeneration Amsterdam Noord-Holland Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Noord-Holland Netherlands

Background RADAR-AD is a European project in the context of the Innovative Medicine Initiative (IMI) focusing on the earlier identification of patients at risk for developing Alzheimer’s Disease (AD) via a panel of remote monitoring technologies (RMTs), including smartphone apps and wearable devices. Method We examined the ability of 6 RMTs (Altoida, Axivity, Banking app, Fitbit, Physilog, and Mezurio) to distinguish between healthy controls (HC) and disease stages of preclinical (PreAD), prodromal (ProAD), and mild to moderate Alzheimer’s disease (MildAD) based on 175 patients (interim analysis). We trained three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) in a pairwise setting (HC vs. PreAD, HC vs. ProAD, HC vs. MildAD, PreAD vs. ProAD, and ProAD vs. MildAD). Since the interim dataset is still limited, we performed repeated, stratified nested cross-validation to get a robust performance estimate. Each classifier was trained with the features of the different devices and a set of baseline variables. The latter include a patient’s gender, age, years of education, and body mass index (BMI) when physical conditions might play a role (Axivitiy, Fitbit, Physilog). In addition, we checked whether specific patterns of the study groups allowed discrimination of the different study groups based on the baseline variables alone. Therefore, we trained one Logistic Regression model with these variables and compared the performance of the other three models with this baseline. The models trained with the baseline and questionnaire-based data served as the reference value in our benchmark that represents how well the discrimination of the different groups works with clinical tests. Result Our preliminary data show that RMTs can identify patients already in a prodromal disease stage (AUC ∼69%, Figure 1). Furthermore, the pairwise combination of data from a banking app and an app monitoring functional cognitive abilities via an augmented reality g

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Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches

arXiv

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arXiv 2019年

作者： Chia, Nai-Hui Li, Tongyang Lin, Han-Hsuan Wang, Chunhao Department of Computer Science University of Texas Austin United States Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams program

Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. Recently, quantum algorithms with superpolynomial speedups for solving SDPs have been proposed assuming access to its constraint matrices in quantum superposition. Mutually inspired by both classical and quantum SDP solvers, in this paper we present a sublinear classical algorithm for solving low-rank SDPs which is asymptotically as good as existing quantum algorithms. Specifically, given an SDP with m constraint matrices, each of dimension n and rank poly(log n), our algorithm gives a succinct description and any entry of the solution matrix in time O(m · poly(log n, 1/Ε)) given access to a sample-based low-overhead data structure of the constraint matrices, where Ε is the precision of the solution. In addition, we apply our algorithm to a quantum state learning task as an application. Technically, our approach aligns with both the SDP solvers based on the matrix multiplicative weight (MMW) framework and the recent studies of quantum-inspired machine learning algorithms. The cost of solving SDPs by MMW mainly comes from the exponentiation of Hermitian matrices, and we propose two new technical ingredients (compared to previous sample-based algorithms) for this task that may be of independent interest: • Weighted sampling: assuming sampling access to each individual constraint matrix A1,..., Aτ, we propose a procedure that gives a good approximation of A = A1 + ··· + Aτ. • Symmetric approximation: we propose a sampling procedure that gives low-rank spectral decomposition of a Hermitian matrix A. This improves upon previous sampling procedures that only give low-rank singular value decompositions, losing the signs of eigenvalues. Copyright © 2019, The Authors. All rights reserved.

关键词： Singular value decomposition

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Motor-cognitive dual tasking in the clinical setting: a sensitive measure of functional 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 Gaetano Scebba Jelena Curcic Marijn Muurling Casper de Boer Neva Coello Alankar Atreya Pauline Conde Holger Fröhlich Margarita Grammatikopoulou Chris Hinds Ioulietta Lazarou Manuel Lentzen Vaibhav A Narayan Rouba Kozak Spiros Nikolopoulos Srinivasan Vairavan Pieter Jelle Visser Gayle Wittenberg Dag Aarsland RADAR-AD Consortium University Hospital of Old Age Psychiatry University of Bern Bern Switzerland King’s College London London United Kingdom Correspondece Novartis Institutes for BioMedical Research Basel Switzerland 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 University of Oxford Oxford United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Centre for Research & Technology Hellas Thessaloniki Greece 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 Gait is a complex everyday activity that depends upon supraspinal activity and a host of cognitive functions such as attention and executive functions. As cognition declines in neurodegenerative diseases, the interaction and competition for neuronal resources during motor-cognitive dual-tasking (e.g., walking while talking) might be a sensitive measure of subtle functional impairments in early Alzheimer’s disease (AD). Here, we aim to identify gait deficits due to neuronal competition across the AD spectrum. Method This investigation is part of the ongoing Remote Assessment of Disease and Relapse – Alzheimer’s Disease (RADAR-AD) study. We attached three inertial measurement units (accelerometer and gyroscope) to both feet and one hip to assess dual task effects (DTE) assessing gait performance with/without concurrent serial subtraction-by-1 task in four groups: 1) amyloid negative healthy controls (HC, N = 59); and 2) amyloid positive preclinical AD (PreAD, N = 30); 3) prodromal AD (ProAD, N = 51); and 4) mild-to-moderate AD dementia (MildAD, N = 44) (Table 1). We furthermore investigated associations of DTE with observer-reported cognition. Result Group comparisons showed that dual-tasking induced lower cadence and increased stance, which were significantly different between HC and ProAD. Several DTE measures of variability differed significantly between PreAD and MildAD, with variability in the path length separating best between PreAD and ProAD (Table 2, Figure 1). DTE measures were associated with observer-rated divided attention only in the MildAD group. Conclusion Neuronal competition as assessed with motor-cognitive dual-tasking, specifically the DTE variability, might reflect functional deficits already in early AD, and could be a valuable additional measure to detect early impairments not captured by cognitive or motor tests alone. Future studies should implement an adaptive cognitive load to improve sensitivity/specificity in early AD stages and

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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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DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

DARE: A Reflective Platform Designed to Enable Agile Data-Dr...

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IEEE International Conference on e-Science and Grid computing

作者： Iraklis Klampanos Athanasios Davvetas André Gemünd Malcolm Atkinson Antonios Koukourikos Rosa Filgueira Amrey Krause Alessandro Spinuso Angelos Charalambidis Federica Magnoni Emanuele Casarotti Christian Pagé Mike Lindner Andreas Ikonomopoulos Vangelis Karkaletsis Institute of Informatics and Telecommunications NCSR "Demokritos" Greece Fraunhofer Institute for Algorithms and Scientific Computing School of Informatics The University of Edinburgh UK NCSR "Demokritos" EPCC The University of Edinburgh UK Koninklijk Nederlands Meteorologisch Instituut (KNMI) The Netherlands Istituto Nazionale Geofisica e Vulcanologia (INGV) Italy Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique Karlsruher Institut f??r Technologie (KIT) Germany Institute of Nuclear & Radiological Sciences and Technology Energy & Safety NCSR "Demokritos" Greece

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.

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