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Parameter estimation of structural dynamics with neural operators enabled surrogate modeling

Mechanical Systems and Signal Processing 2025年 237卷

作者： Mingyuan Zhou Haoze Song Wenjing Ye Wei Wang Zhilu Lai Internet of Things Thrust Information Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong China Data Science and Analytics Thrust Information Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong China

Parameter estimation in structural dynamics generally involves inferring the values of physical, geometric, or even customized parameters based on first principles or expert knowledge, which is challenging for complex structural systems. In this work, we present a unified deep learning-based framework for parameterization , forward modeling , and inverse modeling of structural dynamics. The parameterization is flexible and can be user-defined, including physical and/or non-physical (customized) parameters. In the forward modeling, we train a neural operator for response prediction — forming a surrogate model, which leverages the defined system parameters and excitation forces as inputs to the model. The inverse modeling focuses on estimating system parameters. In particular, the learned forward surrogate model (which is differentiable) is utilized for preliminary parameter estimation via gradient-based optimization; to further boost the parameter estimation, we introduce a neural refinement method to mitigate ill-posed problems, which often occur in the former. The framework’s effectiveness is verified numerically and experimentally, in both interpolation and extrapolation cases, indicating its capability to capture intrinsic dynamics of structural systems from both forward and inverse perspectives. Moreover, the framework’s flexibility is expected to support a wide range of applications, including surrogate modeling, structural identification, damage detection, and inverse design of structural systems.

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Weakly supervised learning for textbook question answering

TechRxiv

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TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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Assessing theoretical uncertainties for cosmological constraints from weak lensing surveys

arXiv

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arXiv 2022年

作者： Tan, Ting Zürcher, Dominik Fluri, Janis Refregier, Alexandre Tarsitano, Federica Kacprzak, Tomasz Sorbonne Université CNRS IN2P3 Laboratoire de Physique Nucléaire et de Hautes Energies LPNHE 4 Place Jussieu ParisF-75252 France Institute for Particle Physics and Astrophysics Department of Physics ETH Zürich Wolfgang Pauli Strasse 27 Zürich8093 Switzerland Data Analytics Lab Department of Computer Science ETH Zurich Universitätstrasse 6 Zürich8006 Switzerland

Weak gravitational lensing is a powerful probe which is used to constrain the standard cosmological model and its extensions. With the enhanced statistical precision of current and upcoming surveys, high accuracy predictions for weak lensing statistics are needed to limit the impact of theoretical uncertainties on cosmological parameter constraints. For this purpose, we present a comparison of the theoretical predictions for the nonlinear matter and weak lensing power spectra, based on the widely used fitting functions (mead and rev-halofit), emulators (EuclidEmulator, EuclidEmulator2, BaccoEmulator and CosmicEmulator) and N-body simulations (Pkdgrav3). We consider the forecasted constraints on the ΛCDM and wCDM models from weak lensing for stage III and stage IV surveys. We study the relative bias on the constraints and their dependence on the assumed prescriptions. Assuming a ΛCDM cosmology, we find that the relative agreement on the S8 parameter is between 0.2 − 0.3σ for a stage III-like survey between the above predictors. For a stage IV-like survey the agreement becomes 1.4 − 3.0σ. In the wCDM scenario, we find broader S8 constraints, and agreements of 0.18 − 0.26σ and 0.7 − 1.7σ for stage III and stage IV surveys, respectively. The accuracies of the above predictors therefore appear adequate for stage III surveys, while the fitting functions would need improvements for future stage IV weak lensing surveys. Furthermore, we find that, of the fitting functions, mead provides the best agreement with the emulators. We discuss the implication of these findings for the preparation of the future weak lensing surveys. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Correction: AI content detection in the emerging information ecosystem: new obligations for media and tech companies

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Ethics and Information Technology 2024年第4期26卷 1-2页

作者： Knott, Alistair Pedreschi, Dino Jitsuzumi, Toshiya Leavy, Susan Eyers, David Chakraborti, Tapabrata Trotman, Andrew Sundareswaran, Sundar Baeza-Yates, Ricardo Biecek, Przemyslaw Weller, Adrian Teal, Paul D. Basu, Subhadip Haklidir, Mehmet Morini, Virginia Russell, Stuart Bengio, Yoshua Social Media Governance Project Global Partnership on AI Montreal Canada School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand University of Pisa Pisa Italy Chuo University Tokyo Japan Insight SFI Research Centre for Data Analytics School of Information and Communication University College Dublin Dublin Ireland School of Computing University of Otago Dunedin New Zealand Alan Turing Institute London United Kingdom University College London London United Kingdom Institute for Experiential AI Northeastern University Silicon Valley USA Warsaw University of Technology Warsaw Poland University of Cambridge Cambridge United Kingdom Computer Science and Engineering Department Jadavpur University Kolkata India Artificial Intelligence Institute Tubitak Bilgem Gebze Türkiye Center for Human-Compatible AI UC Berkeley Berkeley USA Mila - Quebec AI Institute Montreal Canada University of Montreal Montreal Canada

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A novel lift adjustment methodology for improving association rule interpretation

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Decision analytics Journal 2025年 15卷

作者： Sowan, Bilal Zhang, Li Matar, Nasim Zraqou, J. Omar, Firas Alnatsheh, Athari Department of Business Intelligence and Data Analytics University of Petra Amman Jordan Department of Computer Science Royal Holloway University of London Surrey TW20 0EX United Kingdom Department of Software Engineering Al-Zaytoonah University of Jordan Amman Jordan Department of Computer Science/Virtual and Augmented Reality University of Petra Amman Jordan Department of Business Intelligence Al-Zaytoonah University of Jordan Amman Jordan Department of Information Security and Cybersecurity Philadelphia University Amman Jordan

Association rules can offer a human-interpretable insight extracted from data. The lift measures used for evaluating association rules in classical Association Rule Mining (ARM) contexts are mainly based on traditional and well-known ones but suffer from interpretation inadequacy when dealing with skewed distributions or low support. This study introduces a new lift adjustment approach with four methods to overcome traditional lift measures and identify the best rules in association rule mining. More concretely, our main objective is to improve the interpretability of association rules to make them more practically relevant for decision-making. We propose an approach incorporating four novel lift adjustment methods (smoothed, weighted, log, and threshold-adjusted lift) to achieve this. We introduce a flexible, dynamic approach combined with four new lift adjustment methods: smoothed, weighted, logarithm, and threshold-adjusted lift. Each technique addresses specific limitations of the traditional lift measure and better captures the reliable representation of item associations by exaggerating stronger relationships or smoothing weaker ones. The proposed methods applied context-aware rule evaluation and adjustment based on measures of relative significance (e.g., Jaccard similarity). The experimental results involving real-world data and synthetic datasets reveal new methods’ effectiveness and robustness in understanding the strengths of association rules and provide a comprehensive view that considers item importance. We evaluate the performance stability of our proposed methods using statistical analysis, including ANOVA, chi-squared, t-tests, and effect size metrics. © 2025 The Author(s)

关键词： Association rule mining Context-aware analysis data interpretability Lift adjustment Rule evaluation Statistical analysis

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T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precision Immunotherapy

arXiv

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arXiv 2023年

作者： Chen, Ziqi Min, Martin Renqiang Guo, Hongyu Cheng, Chao Clancy, Trevor Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Machine Learning Department NEC Labs PrincetonNJ08540 United States Digital Technologies Research Centre National Research Council Canada ON Canada Department of Medicine Baylor College of Medicine HoustonTX77030 United States NEC Oncolmmunity AS Oslo Cancer Cluster Innovation Park Ullernchausséen 64 Oslo0379 Norway Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery. © 2023, CC BY.

关键词： Reinforcement learning

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A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

arXiv

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arXiv 2022年

作者： Qin, Rongjun Liu, Tao Geospatial Data Analytics Lab The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 2070 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Labs 2015 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University Pomerene Hall 1760 Neil Ave ColumbusOH43210 United States College of Forest Resources and Environmental Science Michigan Technological University 1400 Townsend Drive HoughtonMI49931 United States Ecosystem Science Center Michigan Technological University 1400 Townsend Drive HoughtonMI49931 United States

As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data;(2) domain gaps across different geographical regions;and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor. Copyright © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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Findings of the sentiment analysis of Dravidian languages in code-mixed text

arXiv

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arXiv 2021年

作者： Chakravarthi, Bharathi Raja Priyadharshini, Ruba Thavareesan, Sajeetha Chinnappa, Dhivya Thenmozhi, Durairaj Sherly, Elizabeth McCrae, John P. Hande, Adeep Ponnusamy, Rahul Banerjee, Shubhanker Vasantharajan, Charangan Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway ULTRA Arts and Science College Tamil Nadu Madurai India Eastern University Sri Lanka Thomson Reuters United States Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu India Indian Institute of Information Technology and Management Kerala India Indian Institute of Information Technology Tiruchirappalli ADAPT Centre Data Science Institute National University Of Ireland Galway Department of Computer Science and Engineering University of Moratuwa Sri Lanka

We present the results of the Dravidian-CodeMix shared task1 held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the continuation of last year’s Dravidian-CodeMix shared task2 held at FIRE 2020. This year’s tasks included code-mixing at the intra-token and inter-token levels. Additionally, apart from Tamil and Malayalam, Kannada was also introduced. We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English. The top system for Tamil-English, Malayalam-English and Kannada-English scored weighted average F1-score of 0.711, 0.804, and 0.630, respectively. In summary, the quality and quantity of the submission show that there is great interest in Dravidian languages in code-mixed setting and state of the art in this domain still needs more improvement. © 2021, CC BY.

关键词： Sentiment analysis

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Epileptic Seizure Detection for Imbalanced datasets Using an Integrated Machine Learning Approach

Epileptic Seizure Detection for Imbalanced Datasets Using an...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mohammad Masum Hossain Shahriar Hisham M. Haddad Analytics and Data Science Institute Kennesaw State University Kennesaw USA Department of Information Technology Kennesaw State University Marietta USA Department of Computer Science Kennesaw State University Marietta USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Epileptic Seizure (Epilepsy) is a neurological disorder that occurs due to abnormal brain activities. Epilepsy affects patients' health and lead to life-threatening situations. Early prediction of epilepsy is highly effective to avoid seizures. Machine Learning algorithms have been used to classify epilepsy from Electroencephalograms (EEG) data. These algorithms exhibited reduced performance when classes are imbalanced. This work presents an integrated machine learning approach for epilepsy detection, which can effectively learn from imbalanced data. This approach utilizes Principal Component Analysis (PCA) at the first stage to extract both high- and low- variant Principal Components (PCs), which are empirically customized for imbalanced data classification. Conventionally, PCA is used for dimension reduction of a dataset leveraging PCs with high variances. In this paper, we propose a model to show that PCs associated with low variances can capture the implicit pattern of minor class of a dataset. The selected PCs are then fed into different machine learning classifiers to predict seizures. We performed experiments on the Epileptic Seizure Recognition dataset to evaluate our model. The experimental results show the robustness and effectiveness of the proposed model.

关键词： Feature extraction Epilepsy Electroencephalography Support vector machines Principal component analysis Radio frequency Brain modeling

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