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20th European Conference on Composite Materials: Composites Meet Sustainability, ECCM 2022

作者： Bénézech, Jean Seelinger, Linus Bastian, Peter Dodwell, Tim Butler, Richard Scheichl, Robert Material and Structure Research Centre University of Bath Bath United Kingdom Institute for Applied Mathematics Heidelberg University Heidelberg Germany Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany Institute of Data Science and Ai University of Exeter Exeter United Kingdom Alan Turing Institute The British Library London United Kingdom

ISBN: (纸本)9782970161400

Due to the interaction of structural meso-scale and geometric macro-scale features, composite aero-structures are inherently multi-scale in nature. In addition, complex manufacturing processes lead to the appearance of defects at multiple scales. These defects bring extra dimensions to the multi-scale problem which discourage the use of a scale separation hypothesis. Despite the growing knowledge of the types of composite characteristic defects, for large scale aerostructures, their location and exact geometry remain unknown or are only observable with difficulty. Hence, a large-scale uncertainty quantification (UQ) problem must be solved. The primary challenge is the cost of the associated numerical simulation. Therefore, to tackle this problem, an efficient multiscale generalized finite element (FE) method has been developed within the open-source software package DUNE (https://***/). Thanks to its modularity, our method has been integrated into an Offline/Online framework allowing a very efficient exploration of defects in of the material parameters. ©2022 Bénézech et al.

关键词： Defects

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Higher-Order Organization in the Human Brain From Matrix-Based Rényi’s Entropy

Higher-Order Organization in the Human Brain From Matrix-Bas...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Qiang Li Shujian Yu Kristoffer H Madsen Vince D Calhoun Armin Iraji Image Processing Laboratory University of Valencia Spain Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) USA Department of Computer Science Vrije Universiteit Amsterdam The Netherlands Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Danish Research Centre for Magnetic Resonance Copenhagen University Hospital Denmark

Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to explore higher-order interactions that go beyond paired brain areas in order to better understand information processing in the human brain. To address this problem, we applied multivariate mutual information, specifically, Total Correlation and Dual Total Correlation to reveal higher-order information in the brain. In this paper, we estimate these metrics using matrix-based Rényi ’ s entropy, which offers a direct and easily interpretable approach that is not limited by direct assumptions about probability distribution functions of multivariate time series. We applied these metrics to resting-state fMRI data in order to examine higher-order interactions in the brain. Our results showed that the higher-order information interactions captured increase gradually as the interaction order increases. Furthermore, we observed a gradual increase in the correlation between the Total Correlation and Dual Total Correlation as the interaction order increased. In addition, the significance of Dual Total Correlation values compared to Total Correlation values also indicate that the human brain exhibits synergy dominance during the resting state.

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Calibmsm: An R Package for Calibration Plots of the Transition Probabilities in a Multistate Model

SSRN

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SSRN 2024年

作者： Pate, Alexander Sperrin, Matthew Riley, Richard D. Van Calster, Ben Martin, Glen P. Centre for Health Informatics University of Manchester United Kingdom Institute of Applied Health Research University of Birmingham United Kingdom Department of Development and Regeneration Division of Imaging Informatics and Data Science Faculty of Biology Medicine and Health University of Manchester M139PR United Kingdom

Background and objective: Multistate models, which allow the prediction of complex multistate survival processes such as multimorbidity, or recovery, relapse and death following treatment for cancer, are being used for clinical prediction. It is paramount to evaluate the calibration (as well as other metrics) of a risk prediction model before implementation of the model. While there are a number of software applications available for developing multistate models, currently no software exists to aid in assessing the calibration of a multistate model, and as a result evaluation of model performance is uncommon. calibmsm has been developed to fill this ***: Assessing the calibration of predicted transition probabilities between any two states is made possible through three approaches. The first two utilise calibration techniques for binary and multinomial logistic regression models in combination with inverse probability of censoring weights, whereas the third utilises psuedo-values. All methods are implemented in conjunction with landmarking to allow calibration assessment of predictions made at any time beyond the start of follow up. This study focuses on calibration curves, but the methodological framework also allows estimation of calibration slopes and ***: This article provides a comprehensive example on how to use calibmsm to assess the calibration of a multistate model developed to predict recovery, adverse events, relapse and survival in patients with blood cancer after a transplantation. The calibration plots indicate that predictions of relapse made at the time of transplant are poorly calibrated, however predictions of death are well calibrated. The calibration of all predictions made at 100 days post transplant appear to be poor, although a larger validation sample is required to make stronger ***: calibmsm is an R package which allows users to assess the calibration of predicted transition probabilities from a mu

关键词： Calibration

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An investigation of ensemble learning techniques for obesity risk prediction using lifestyle data

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Decision Analytics Journal 2025年 14卷

作者： Ganie, Shahid Mohammad Reddy, Bobba Bharath K, Hemachandran Rege, Manjeet AI Research Centre Woxsen University Telangana Hyderabad 502345 India School of Technology Woxsen University Telangana Hyderabad 502345 India Department of Software Engineering and Data Science Director of the Center for Applied Artificial Intelligence University of St. Thomas United States

Obesity disease is a significant health issue and has affected millions of people worldwide. Identifying underlying reasons for the onset of obesity risk in its early stage has become challenging for medical practitioners. The growing volume of lifestyle data related to obesity has made it imperative to employ effective machine-learning algorithms that can gather insights from the underlying data trends and identify critical patient conditions. In this study, an ensemble learning approach including boosting, bagging, and voting techniques was used for obesity risk prediction based on lifestyle dataset. Specifically, XGBoost, Gradient Boosting, and CatBoost models are used for boosting, Bagged Decision Tree, Random Forest, and Extra Tree models are used for bagging, and Logistic Regression, Decision Tree, and Support Vector Machine models are used for voting. Different preprocessing steps were employed to improve the quality assessment of the data. Hyperparameter tuning and feature selection and ranking are also used to achieve better prediction results. The considered models are extensively evaluated and compared using various metrics. Among all the models, XGBoost performed better with an accuracy of 98.10%, precision and recall of 97.50%, f1-score of 96.50%, and AUC-ROC of 100%, respectively. Additionally, weight, height, and age features were identified and ranked as the most significant predictors using the recursive feature elimination method for obesity risk prediction. Our proposed model can be used in the healthcare industry to support healthcare providers in better predicting and detecting multiple stages of obesity diseases. © 2024 The Author(s)

关键词： Bagging Boosting Ensemble learning Machine learning Obesity risk prediction Voting

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Optimal Clustering with Dependent Costs in Bayesian Networks

arXiv

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arXiv 2023年

作者： Wu, Paul Pao-Yen Ruggeri, Fabrizio Mengersen, Kerrie Centre for Data Science School of Mathemmatical Sciences Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia Institute of Applied Mathematics and Information Technology Italian National Research Councily Via Alfonso Corti 12 MilanoI-20133 Italy

Clustering of nodes in Bayesian Networks (BNs) and related graphical models such as Dynamic BNs (DBNs) has been demonstrated to enhance computational efficiency and improve model learning. Typically, it involves the partitioning of the underlying Directed Acyclic Graph (DAG) into cliques, or optimising for some cost or criteria. Computational cost is important since BN and DBN inference, such as estimating marginal distributions given evidence or updating model parameters, is NP-hard. The challenge is exacerbated by cost dependency, where inference outcomes and hence clustering cost depends on both nodes within a cluster and the mapping of clusters that are connected by at least one arc. We propose an algorithm called Dependent Cluster MAPping (DCMAP) which can, given an arbitrarily defined, positive cost function, iteratively and rapidly find near-optimal, then optimal cluster mappings. DCMAP is shown analytically to be optimal in terms of finding all of the least cost cluster mapping solutions and with no more iterations than an equally informed algorithm. DCMAP is demonstrated on a complex systems seagrass DBN, which has 25 nodes per time-slice, and captures biological, ecological and environmental dynamics and their interactions to predict the impact of dredging stressors on resilience and their cumulative effects over time. The algorithm is employed to find clusters to optimise the computational efficiency of inferring marginal distributions given evidence. For the 25 (one time-slice) and 50-node (two time-slices) DBN, the search space size was 9.91 × 109 and 1.51 × 1021 possible cluster mappings, respectively, but the first optimal solution was found at iteration number 856 (95% CI 852,866), and 1569 (1566,1581) with a cost that was 4% and 0.2% of the naive heuristic cost, respectively. Through optimal clustering, DCMAP opens up opportunities for further research beyond improving computational efficiency, such as using clustering to minimise entropy in BN lear

关键词： Bayesian networks

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AskBeacon – Performing genomic data exchange and analytics with natural language

arXiv

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arXiv 2024年

作者： Wickramarachchi, Anuradha Tonni, Shakila Majumdar, Sonali Karimi, Sarvnaz Kõks, Sulev Hosking, Brendan Rambla, Jordi Twine, Natalie A. Jain, Yatish Bauer, Denis C. Australian e-Health Research Centre Commonwealth Scientific and Industrial Research Organisation Adelaide Australia Data61 Commonwealth Scientific and Industrial Research Organisation Sydney Australia Centre for Molecular Medicine and Innovative Therapeutics Murdoch University PerthWA6150 Australia Perron Institute for Neurological and Translational Science PerthWA6009 Australia The Barcelona Institute of Science and Technology Barcelona Spain Department of Medicine and Life Sciences Universitat Pompeu Fabra PRBB Barcelona Spain Australian e-Health Research Centre Commonwealth Scientific and Industrial Research Organisation Sydney Australia Macquarie University Applied BioSciences Faculty of Science and Engineering Macquarie Park Australia University of Sydney School - School of Medical Sciences Department of Biomedical Informatics and Digital Health Sydney Australia

Enabling clinicians and researchers to directly interact with global genomic data resources by removing technological barriers is vital for medical genomics. AskBeacon enables Large Language Models to be applied to securely shared cohorts via the GA4GH Beacon protocol. By simply "asking" Beacon, actionable insights can be gained, analyzed and made publication-ready. © 2024, CC BY-NC-SA.

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Calibration plots for multistate risk predictions models: an overview and simulation comparing novel approaches

arXiv

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arXiv 2023年

作者： Pate, Alexander Sperrin, Matthew Riley, Richard D. Peek, Niels Van Staa, Tjeerd Sergeant, Jamie C. Mamas, Mamas A. Lip, Gregory Y.H. O'Flaherty, Martin Barrowman, Michael Buchan, Iain Martin, Glen P. Centre for Health Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom NIHR Manchester Biomedical Research Centre University of Manchester Manchester United Kingdom Institute of Applied Health Research University of Birmingham Birmingham United Kingdom Centre for Epidemiology Versus Arthritis Centre for Musculoskeletal Research Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Centre for Biostatistics Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Keele Cardiovascular Research Group Keele University Stoke-on-Trent United Kingdom Liverpool Centre for Cardiovascular Science University of Liverpool Liverpool John Moores University Liverpool Heart & Chest Hospital Liverpool United Kingdom Department of Clinical Medicine Aalborg University Aalborg Denmark NIHR Applied Research Collaboration NW Coast University of Liverpool Liverpool United Kingdom Institute of Population Health Faculty of Health and Life Sciences University of Liverpool Liverpool United Kingdom Manchester United Kingdom

Introduction There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of non-informative and informative censoring through a simulation. Methods We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. Results The pseudo-value, BLR-IPCW and MLR-IPCW approaches give unbiased estimates of the calibration curves under non-informative censoring. These methods remained unbiased in the presence of informative censoring, unless the mechanism was strongly informative, with bias concentrated in the areas of predicted transition probabilities of low density. Conclusions We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot, which provides additional information over either of the other methods. © 2023, CC BY.

关键词： Calibration

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Results of the Ontology Alignment Evaluation Initiative 2023 18

Results of the Ontology Alignment Evaluation Initiative 2023

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18th International Workshop on Ontology Matching, OM 2023

作者： Pour, Mina Abd Nikooie Algergawy, Alsayed Buche, Patrice Castro, Leyla J. Chen, Jiaoyan Coulet, Adrien Cufi, Julien Dong, Hang Fallatah, Omaima Faria, Daniel Fundulaki, Irini Hertling, Sven He, Yuan Horrocks, Ian Huschka, Martin Ibanescu, Liliana Jain, Sarika Jiménez-Ruiz, Ernesto Karam, Naouel Lambrix, Patrick Li, Huanyu Li, Ying Monnin, Pierre Nasr, Engy Paulheim, Heiko Pesquita, Catia Saveta, Tzanina Shvaiko, Pavel Sousa, Guilherme Trojahn, Cassia Vatascinova, Jana Wu, Mingfang Yaman, Beyza Zamazal, Ondrej Zhou, Lu Linköping University Swedish e-Science Research Centre Linköping Sweden Friedrich Schiller University Jena Germany University of Passau Germany UMR IATE INRAE University of Montpellier France ZB MED Information Centre for Life Sciences Germany Department of Computer Science The University of Manchester United Kingdom Inria Paris France Centre de Recherche des Cordeliers Inserm Université Paris Cité Sorbonne Université France Department of Computer Science University of Oxford United Kingdom Department of Data Science Umm Al-Qura University Saudi Arabia INESC-ID IST University of Lisbon Portugal Institute of Computer Science-FORTH Heraklion Greece Data and Web Science Group University of Mannheim Germany Fraunhofer Institute for High-Speed Dynamics Ernst-Mach-Institut EMI Germany Université Paris-Saclay INRAE AgroParisTech UMR MIA Paris-Saclay France National Institute of Technology Kurukshetra India City University of London United Kingdom SIRIUS University of Oslo Norway Institute for Applied Informatics University of Leipzig Germany University Côte d’Azur Inria CNRS I3S France Albert Ludwig University of Freiburg Germany LASIGE Faculdade de Ciências Universidade de Lisboa Portugal Trentino Digitale SpA Trento Italy Institut de Recherche en Informatique de Toulouse France Prague University of Economics and Business Czech Republic Australian Research Data Commons Australia ADAPT Centre Trinity College Dublin Ireland Flatfee Corp. United States

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2023 campaign offered 15 tracks and was attended by 16 participants. This paper is an overall presentation of that campaign. © 2023 Copyright for this paper by its authors.

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Dualband and Tripleband Metamaterial Absorber in Wr1 Band and Lower Terahertz Frequencies

SSRN

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SSRN 2023年

作者： Gharat, Sarvesh Kulkarni, Prutha Prabhu, Shriganesh Garde, Chandrashekhar Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Electronics and Telecommunication Vishwakarma Institute of Information Technology Pune India Depatment of Engineering and Applied Sciences Vishwakarma Institute of Information Technology Pune India Department of Condensed Matter Physics and Materials Science Tata Institute of Fundamental Research Mumbai India

The frequency Band of 0.5 THz to 1.1 THz (WR1.5 and WR1) is one of the promising bands when it comes to 6G. In this paper, we propose a novel metamaterial absorber suitable to be used at lower WR1 frequencies in TE mode. Alternatively, the design can be used as a strong absorber at lower Terahertz frequencies. In addition, when used in TM mode the absorber works as a perfect absorber at a higher WR1 band. © 2023, The Authors. All rights reserved.

关键词： Metamaterials

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