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A probabilistic risk-based decision framework for structural health monitoring

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 150卷 107339-107339页

作者： Hughes, A. J. Barthorpe, R. J. Dervilis, N. Farrar, C. R. Worden, K. Univ Sheffield Dept Mech Engn Dynam Res Grp Sheffield S1 3JD S Yorkshire England Los Alamos Natl Lab Engn Inst MS T-001 Los Alamos NM 87545 USA

Obtaining the ability to make informed decisions regarding the operation and maintenance of structures, provides a major incentive for the implementation of structural health monitoring (SHM) systems. probabilistic risk assessment (PRA) is an established methodology that allows engineers to make risk-informed decisions regarding the design and operation of safety-critical and high-value assets in industries such as nuclear and aerospace. The current paper aims to formulate a risk-based decision framework for structural health monitoring that combines elements of PRA with the existing SHM paradigm. As an apt tool for reasoning and decision-making under uncertainty, probabilistic graphical models serve as the foundation of the framework. The framework involves modelling failure modes of structures as Bayesian network representations of fault trees and then assigning costs or utilities to the failure events. The fault trees allow for information to pass from probabilistic classifiers to influence diagram representations of decision processes whilst also providing nodes within the graphical model that may be queried to obtain marginal probability distributions over local damage states within a structure. Optimal courses of action for structures are selected by determining the strategies that maximise expected utility. The risk-based framework is demonstrated on a realistic truss-like structure and supported by experimental data. Finally, a discussion of the risk-based approach is made and further challenges pertaining to decision-making processes in the context of SHM are identified. (c) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Structural health monitoring probabilistic risk assessment probabilistic graphical models Decision-making

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Temporal Hierarchical Adaptive Texture CRF for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2015年第6期34卷 1227-1241页

作者： Karimaghaloo, Zahra Rivaz, Hassan Arnold, Douglas L. Collins, D. Louis Arbel, Tal McGill Univ Ctr Intelligent Machines Montreal PQ H3A 2A7 Canada Concordia Univ Elect & Comp Engn Dept Montreal PQ H3G 1M8 Canada Concordia Univ PERFORM Ctr Montreal PQ H3G 1M8 Canada NeuroRx Res Montreal PQ H2X 3P9 Canada McGill Univ Montreal Neurol Inst Montreal PQ H3A 2B4 Canada

We propose a conditional random field (CRF) based classifier for segmentation of small enhanced pathologies. Specifically, we develop a temporal hierarchical adaptive texture CRF (THAT-CRF) and apply it to the challenging problem of gad enhancing lesion segmentation in brain MRI of patients with multiple sclerosis. In this context, the presence of many nonlesion enhancements (such as blood vessels) renders the problem more difficult. In addition to voxel-wise features, the framework exploits multiple higher order textures to discriminate the true lesional enhancements from the pool of other enhancements. Since lesional enhancements show more variation over time as compared to the nonlesional ones, we incorporate temporal texture analysis in order to study the textures of enhanced candidates over time. The parameters of the THAT-CRF model are learned based on 2380 scans from a multi-center clinical trial. The effect of different components of the model is extensively evaluated on 120 scans from a separate multi-center clinical trial. The incorporation of the temporal textures results in a general decrease of the false discovery rate. Specifically, THAT-CRF achieves overall sensitivity of 95% along with false discovery rate of 20% and average false positive count of 0.5 lesions per scan. The sensitivity of the temporal method to the trained time interval is further investigated on five different intervals of 69 patients. Moreover, superior performance is achieved by the reviewed labelings of our model compared to the fully manual labeling when applied to the context of separating different treatment arms in a real clinical trial.

关键词： Automatic segmentation magnetic resonance imaging (MRI) multiple sclerosis (MS) probabilistic graphical models

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A Survey on Bayesian Deep Learning

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ACM COMPUTING SURVEYS 2020年第5期53卷 108-108页

作者： Wang, Hao Yeung, Dit-Yan MIT G92832 Vassar St Cambridge MA 02139 USA Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Room 3531Clear Water Bay Hong Kong Peoples R China

A comprehensive artificial intelligence system needs to not only perceive the environment with different "senses" (e.g., seeing and hearing) but also infer the world's conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks, such as visual object recognition and speech recognition, using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. In recent years, Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models.(1) In this general framework, the perception of text or images using deep learning can boost the performance of higher-level inference and, in turn, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, and so on. We also discuss the relationship and differences between Bayesian deep learning and other related topics, such as Bayesian treatment of neural networks.

关键词： Deep learning Bayesian networks probabilistic graphical models generative models

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Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package

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KNOWLEDGE-BASED SYSTEMS 2023年第1期278卷

作者： Leonelli, Manuele Ramanathan, Ramsiya Wilkerson, Rachel L. IE Univ Sch Sci & Technol Madrid Spain Univ Bologna Dipartimento Sci Stat Bologna Italy Tesserwell LLC Waco TX USA

Bayesian networks are a class of models that are widely used for the diagnosis, prediction, and risk assessment of complex operational systems. Multiple approaches, as well as implemented software, now guide their construction via learning from data or expert elicitation. However, current software only includes minimal functionalities to explore the assumptions, quality of fit, and sensitivity to learned parameters of a constructed Bayesian network. Here, we illustrate the usage of the bnmonitor R package: the first comprehensive software for model-checking of a Bayesian network. An applied data analysis using bnmonitor is carried out over a medical dataset to illustrate the use of its wide array of functions. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Bayesian networks Model-checking probabilistic graphical models R package Sensitivity analysis

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Modeling Global and Local Interactions for Online Conversation Recommendation

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2022年第3期40卷 49-49页

作者： Zeng, Xingshan Li, Jing Wang, Lingzhi Wong, Kam-Fai Chinese Univ Hong Kong Dept Syst Engn & Engn Management Shatin Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Kowloon Hong Kong Peoples R China

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions, represented by topic and discourse word clusters to reflect users' content and pragmatic preferences, and (2) local interactions, encoding replying relations and chronological order of conversation turns to characterize users' prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users' topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

关键词： Conversation recommendation graph neural networks probabilistic graphical models

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Open problems in causal structure learning: A case study of COVID-19 in the UK

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期234卷

作者： Constantinou, Anthony Kitson, Neville K. Liu, Yang Chobtham, Kiattikun Amirkhizi, Arian Hashemzadeh Nanavati, Praharsh A. Mbuvha, Rendani Petrungaro, Bruno Queen Mary Univ London QMUL Sch Elect Engn & Comp Sci EECS Bayesian AI Res Lab Machine Intelligence & Decis Syst MInDS Res Grp London E1 4NS England Sharif Univ Technol Tehran Iran Univ Toronto Toronto ON Canada Indian Inst Sci Educ & Res Bhopal Madhya Pradesh India

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and effect relationships. The causal representation provided by these algorithms enables transparency and explain ability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models, data sets, and source code publicly available online.

关键词： Bayesian network structure learning Causal discovery Causal machine learning Causal models Knowledge-based systems probabilistic graphical models

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Dynamic Bayesian networks with application in environmental modeling and management: A review

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ENVIRONMENTAL MODELLING & SOFTWARE 2023年 170卷

作者： Chang, Jingjing Bai, Yongxin Xue, Jie Gong, Lu Zeng, Fanjiang Sun, Huaiwei Hu, Yang Huang, Hao Ma, Yantao Xinjiang Univ Coll Ecol & Environm Urumqi 830046 Peoples R China Beijing Informat Sci & Technol Univ Sch Appl Sci Beijing 100192 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Key Lab Ecol Safety & Sustainable Dev Arid Lands Urumqi 830011 Xinjiang Peoples R China Cele Natl Stn Observat & Res Desert Grassland Ecos Cele 848300 Xinjiang Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Peoples R China

Dynamic Bayesian networks (DBNs) as an extension of traditional Bayesian networks have recently been paid great concern to environmental modeling to capture dynamic processes and support feedback loops. However, the applications of DBNs in environmental modeling are still scarce and challenging. There are no reviews found in the literature to explore the potential and application of DBNs in the environmental science fields so far. This review is to illustrate how DBNs are applied in the environmental modeling and management. The overview of DBNs is performed from January 1990 to December 2021 in the Web of Science search related to Environmental Sciences. Only 5.69% of the total publications have been used in this item. The application fields, model types, model aims, model learning, model spatial and temporal scale, model validation, and software are discussed. The pros and cons analysis highlights the advantages and disadvantages of DBNs in the environmental modeling. The current DBNs research focuses mainly on environment applications through expert knowledge for learning and validation of models due to less available support of the popular commercial software packages and data availability limitation. The powerful potential of DBNs is yet unexploited. Some challenges including timeline discretization, modeling accuracy in medium-and long-term scale, modeling of spatial dependencies and in-teractions, and development of algorithms and software have to be considered in future research.

关键词： Dynamic bayesian networks Environmental modeling probabilistic graphical models Literature review Spatio-temporal relationships

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A Pan-Cancer Modular Regulatory Network Analysis to Identify Common and Cancer-Specific Network Components

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CANCER INFORMATICS 2014年第Sup5期13卷 69-84页

作者： Knaack, Sara Siahpirani, Alireza Roy, Sushmita Univ Wisconsin Wisconsin Inst Discovery Madison WI USA Univ Wisconsin Dept Comp Sci Madison WI USA Univ Wisconsin Dept Biostat & Med Informat Madison WI USA

Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module-and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle

关键词： The Cancer Genome Atlas (TCGA) Regulatory modules Transcriptional regulatory networks Pan-cancer analysis Stability selection Modular regulatory network inference probabilistic graphical models

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Open vs closed-ended questions in attitudinal surveys-Comparing, combining, and interpreting using natural language processing

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期137卷 103589-103589页

作者： Baburajan, Vishnu de Abreu e Silva, Joao Pereira, Francisco Camara Inst Super Tecn CERIS MIT Portugal Program P-1049001 Lisbon Portugal Tech Univ Denmark DTU Management Engn DK-2800 Lyngby Denmark Inst Super Tecn CERIS P-1049001 Lisbon Portugal

To improve the traveling experience, researchers have been analyzing the role of attitudes in travel behavior modeling. Although most researchers use closed-ended surveys, the appropriate method to measure attitudes is debatable. Topic Modeling could significantly reduce the time to extract information from open-ended responses and eliminate subjective bias, thereby alleviating analyst concerns. Our research uses Topic Modeling to extract information from open-ended questions and compare its performance with closed-ended responses. Furthermore, some respondents might prefer answering questions using their preferred questionnaire type. So, we propose a modeling framework that allows respondents to use their preferred questionnaire type to answer the survey and enable analysts to use the modeling frameworks of their choice to predict behavior. We demonstrate this using a dataset collected from the USA that measures the intention to use Autonomous Vehicles for commute trips. Respondents were presented with alternative questionnaire versions (open-and closed-ended). Since our objective was also to compare the performance of alternative questionnaire versions, the survey was designed to eliminate influences resulting from statements, behavioral framework, and the choice experiment. Results indicate the suitability of using Topic Modeling to extract information from openended responses;however, the models estimated using the closed-ended questions perform better compared to them. Besides, the proposed model performs better compared to the models used currently. Furthermore, our proposed framework will allow respondents to choose the questionnaire type to answer, which could be particularly beneficial to them when using voice-based surveys.

关键词： Topic modeling Likert scales Open-ended responses Travel behavior research probabilistic graphical models Autonomous vehicles

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FuDGE: a method to estimate a functional differential graph in a high-dimensional setting

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 3535-3616页

作者： Boxin Zhao Y. Samuel Wang Mladen Kolar Booth School of Business The University of Chicago Chicago IL Department of Statistics and Data Science Cornell University Ithaca NY

We consider the problem of estimating the difference between two undirected functional graphical models with shared structures. In many applications, data are naturally regarded as a vector of random functions rather than as a vector of scalars. For example, electroencephalography (EEG) data are treated more appropriately as functions of time. In such a problem, not only can the number of functions measured per sample be large, but each function is itself an infinite-dimensional object, making estimation of model parameters challenging. This is further complicated by the fact that curves are usually observed only at discrete time points. We first define a functional differential graph that captures the differences between two functional graphical models and formally characterize when the functional differential graph is well defined. We then propose a method, FuDGE, that directly estimates the functional differential graph without first estimating each individual graph. This is particularly beneficial in settings where the individual graphs are dense but the differential graph is sparse. We show that FuDGE consistently estimates the functional differential graph even in a high-dimensional setting for both fully observed and discretely observed function paths. We illustrate the finite sample properties of our method through simulation studies. We also propose a competing method, the Joint Functional graphical Lasso, which generalizes the Joint graphical Lasso to the functional setting. Finally, we apply our method to EEG data to uncover differences in functional brain connectivity between a group of individuals with alcohol use disorder and a control group.

关键词： differential graph estimation functional data analysis multivariate functional data probabilistic graphical models structure learning

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