Relevant bibliographies by topics / Dynamic Representation Learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dynamic Representation Learning'

Author: Grafiati
Published: 22 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Dynamic Representation Learning"

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Lee, Jungmin, and Wongyoung Lee. "Aspects of A Study on the Multi Presentational Metaphor Education Using Online Telestration." Korean Society of Culture and Convergence 44, no. 9 (2022): 163–73. http://dx.doi.org/10.33645/cnc.2022.9.44.9.163.

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This study is an attempt to propose a multiple representational metaphor education model that combines linguistic representation and visual representation using online telestration. The advent of the media and online era has incorporated not only the understanding of linguistic representation l but also the understanding of visual representation into an important phase of cognitive behavior and requires the implementation of online learning. In such an era's needs, it can be said that teaching-learning makes metaphors be used as a tool for thinking and cognition in an online environment, learn
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Han, Chengkai, Jingyuan Wang, Yongyao Wang, et al. "Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11763–71. https://doi.org/10.1609/aaai.v39i11.33280.

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Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and overlook the dynamic nature of traffic states and trajectories, which is crucial for downstream tasks. To address this gap, we propose TRACK, a novel framework to bridge traffic state and trajectory data for dynamic road network and trajectory representation learning. TRACK leverages graph attentio
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Biswal, Siddharth, Cao Xiao, Lucas M. Glass, Elizabeth Milkovits, and Jimeng Sun. "Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 557–64. http://dx.doi.org/10.1609/aaai.v34i01.5394.

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Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? How to use doctor representation to solve important health analytic problems? In this work, we study the problem on clinical trial recruitment, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those docto
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Wang, Xingqi, Mengrui Zhang, Bin Chen, Dan Wei, and Yanli Shao. "Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis." Electronics 12, no. 13 (2023): 2986. http://dx.doi.org/10.3390/electronics12132986.

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Multimodal sentiment analysis (MSA) has attracted more and more attention in recent years. This paper focuses on the representation learning of multimodal data to reach higher prediction results. We propose a model to assist in learning modality representations with multitask learning and contrastive learning. In addition, our approach obtains dynamic weights by considering the homoscedastic uncertainty of each task in multitask learning. Specially, we design two groups of subtasks, which predict the sentiment polarity of unimodal and bimodal representations, to assist in learning representati
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Goyal, Palash, Sujit Rokka Chhetri, and Arquimedes Canedo. "dyngraph2vec: Capturing network dynamics using dynamic graph representation learning." Knowledge-Based Systems 187 (January 2020): 104816. http://dx.doi.org/10.1016/j.knosys.2019.06.024.

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Han, Liangzhe, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu, and Tongyu Zhu. "Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4293–301. http://dx.doi.org/10.1609/aaai.v37i4.25548.

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Many deep spatio-temporal learning methods have been proposed for crowd flow modeling in recent years. However, most of them focus on designing a spatial and temporal convolution mechanism to aggregate information from nearby nodes and historical observations for a pre-defined prediction task. Different from the existing research, this paper aims to provide a generic and dynamic representation learning method for crowd flow modeling. The main idea of our method is to maintain a continuous-time representation for each node, and update the representations of all nodes continuously according to t
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Jiao, Pengfei, Hongjiang Chen, Huijun Tang, et al. "Contrastive representation learning on dynamic networks." Neural Networks 174 (June 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.

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Radulescu, Angela, Yeon Soon Shin, and Yael Niv. "Human Representation Learning." Annual Review of Neuroscience 44, no. 1 (2021): 253–73. http://dx.doi.org/10.1146/annurev-neuro-092920-120559.

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The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory are themselves inferred from experience with each task. We review this evidence and place it in the
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Liu, Dianbo, Alex Lamb, Xu Ji, et al. "Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8825–33. http://dx.doi.org/10.1609/aaai.v37i7.26061.

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Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It has been theoretically and empirically shown that discretization of representations leads to improved generalization, including in reinforcement learning where discretization can be used to bottleneck multi-agent communication to promote agent specialization and robustness. The discretization tightness of most VQ-based methods is defined by the number of discrete codes in the representation vector and the codebook size, which are fixed as hyperparameters. In
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Deng, Yongjian, Hao Chen, and Youfu Li. "A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (2024): 1492–500. http://dx.doi.org/10.1609/aaai.v38i2.27914.

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Recent advances in event-based research prioritize sparsity and temporal precision. Approaches learning sparse point-based representations through graph CNNs (GCN) become more popular. Yet, these graph techniques hold lower performance than their frame-based counterpart due to two issues: (i) Biased graph structures that don't properly incorporate varied attributes (such as semantics, and spatial and temporal signals) for each vertex, resulting in inaccurate graph representations. (ii) A shortage of robust pretrained models. Here we solve the first problem by proposing a new event-based GCN (E
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Dissertations / Theses on the topic "Dynamic Representation Learning"

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Stefanidis, Achilleas. "Dynamic Graph Representation Learning on Enterprise Live Video Streaming Events." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278817.

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Enterprises use live video streaming as a mean of communication. Streaming high-quality video to thousands of devices in a corporate network is not an easy task; the bandwidth requirements often exceed the network capacity. For that matter, Peer-To-Peer (P2P) networks have been proven beneficial, as peers can exchange content efficiently by utilizing the topology of the corporate network. However, such networks are dynamic and their topology might not always be known. In this project we propose ABD, a new dynamic graph representation learning approach, which aims to estimate the bandwidth capa
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Liemhetcharat, Somchaya. "Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/304.

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Forming an effective multi-robot team to perform a task is a key problem in many domains. The performance of a multi-robot team depends on the robots the team is composed of, where each robot has different capabilities. Team performance has previously been modeled as the sum of single-robot capabilities, and these capabilities are assumed to be known. Is team performance just the sum of single-robot capabilities? This thesis is motivated by instances where agents perform differently depending on their teammates, i.e., there is synergy in the team. For example, in human sports teams, a well-tra
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McGarity, Michael Computer Science &amp Engineering Faculty of Engineering UNSW. "Heterogeneous representations for reinforcement learning control of dynamic systems." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2004. http://handle.unsw.edu.au/1959.4/19350.

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Intelligent agents are designed to interact with, and learn about, their environment so that they can act purposefully towards a goal. One class of problems encountered in building such agents is learning how to respond to dynamic systems with a continuous state space. The goals of this dissertation are to develop a framework for understanding the behaviour of partitioned dynamic systems with continuous underlying state and to translate this framework into algorithms which adaptively form a partition of the continuous space such that the partitioned system is more easily learned and controlled
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Woodbury, Nathan Scott. "Representation and Reconstruction of Linear, Time-Invariant Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.

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Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required)
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Ribeiro, Andre Figueiredo. "Graph dynamics : learning and representation." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34184.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.<br>Includes bibliographical references (p. 58-60).<br>Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table - where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial relations. As a fram
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Terreau, Enzo. "Apprentissage de représentations d'auteurs et d'autrices à partir de modèles de langue pour l'analyse des dynamiques d'écriture." Electronic Thesis or Diss., Lyon 2, 2024. http://www.theses.fr/2024LYO20001.

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La démocratisation récente et massive des outils numériques a donné à tous le moyen de produire de l'information et de la partager sur le web, que ce soit à travers des blogs, des réseaux sociaux, des plateformes de partage, ... La croissance exponentielle de cette masse d'information disponible, en grande partie textuelle, nécessite le développement de modèles de traitement automatique du langage naturel (TAL), afin de la représenter mathématiquement pour ensuite la classer, la trier ou la recommander. C'est l'apprentissage de représentation. Il vise à construire un espace de faible dimension
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Pinder, Ross Andrew. "Representative learning design in dynamic interceptive actions." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/59803/1/Ross_Pinder_Thesis.pdf.

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The overarching aim of this thesis was to investigate how processes of perception and action emerge under changing informational constraints during performance of multi-articular interceptive actions. Interceptive actions provide unique opportunities to study processes of perception and action in dynamic performance environments. The movement model used to exemplify the functionally coupled relationship between perception and action, from an ecological dynamics perspective, was cricket batting. Ecological dynamics conceptualises the human body as a complex system composed of many interacting s
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Moremoholo, T. P. "A review of how to optimize learning from external representations." Journal for New Generation Sciences, Vol 11, Issue 2: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/635.

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Published Article<br>This article reviews research on learning with external representations and provides a theoretical background on how to optimize learning from external representations. General factors, such as the type of material to be learned, learner characteristics and the testing method, are some of the variables that can determine if graphic medium can increase a subject's comprehension and if such comprehension can be accurately measured. These factors are discussed and represented by a model to suggest how external representations can be effectively used in a learning environment.
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Delasalles, Edouard. "Inferring and Predicting Dynamic Representations for Structured Temporal Data." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS296.

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Les données temporelles constituent une partie importante des données digitales. Prévoir leurs prochaines valeurs est une tâche importante et difficile. Les méthodes statistiques standard sont fondées sur des modèles linéaires souvent limitées aux données de faible dimension. Ici, nous utilisons plutôt des méthodes d'apprentissage profond capables de traiter des données structurées en haute dimension. Dans cette thèse, nous nous intéressons aux modèles à variables latentes. Contrairement aux modèles auto-régressifs qui utilisent directement des données passées pour effectuer des prédictions, l
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Mccosker, Christopher Mark. "Interacting constraints in high performance long jumping." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/204284/1/Christopher_McCosker_Thesis.pdf.

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This thesis details a series of studies investigating long jump locomotor pointing tasks through a theoretical framework of ecological dynamics. A multi-methods approach was used to investigate athlete behaviours in competition environments revealing the complexities of field-based locomotor pointing tasks, providing important information for the design of practice environments for practitioners. The development of a new substantive theoretical model of perform, respond and manage, presents as a novel tool for practitioners in understanding emergent behaviour in long jump and can be used to be
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Books on the topic "Dynamic Representation Learning"

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König, Thomas, Xiao Lu, and Thiago N. Silva. Learning to Govern Together in Representative Democracy. Oxford University PressOxford, 2025. https://doi.org/10.1093/9780198959045.001.0001.

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Abstract Learning to Govern Together in Representative Democracy introduces a dynamic theory of coalition governance, focusing on the temporal constraints that coalition parties face when governing together in representative democracies. The book examines how coalition partners, over the course of a legislative term, adapt their strategies for joint policy-making as they encounter varying levels of cooperation and competition in partnership. Two distinct models of learning are developed: the portfolio-learning model, where ministers learn through interactions within their portfolios, and the p
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Baum, Susan, and Robin Schader. Using a Positive Lens. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190645472.003.0003.

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Getting to know students through their positive attributes, along with any obstacles to their success, can lead to academic, social, and behavioral growth. Defining twice exceptional (2e) as those whose high abilities are coupled with learning and/or behavioral challenges, this chapter illustrates 2e using the metaphor of green. This provides a fresh representation what being 2e means and also highlights why conventional educational plans may not be as effective as desired. To engage 2e students, the chapter introduces a practical, dynamic process––The Suite of Tools™—which allows educators to
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Clavio, Galen. Social Media and Sports. Human Kinetics, 2017. http://dx.doi.org/10.5040/9781718221000.

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Social media communications play a huge role in the day-to-day operations of sport teams and organizations. Both current and aspiring sport business professionals need to know how to best leverage social media to meet their organizational goals, and Social Media and Sports With HKPropel Access will help pave the way by emphasizing the strategic, creative, and logistical elements of effective social media practices. Beginning with foundational concepts, students will first examine the history of social media and its impact on sports. They will learn about the categories of content used, includi
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Faflik, David. Urban Formalism. Fordham University Press, 2020. http://dx.doi.org/10.5422/fordham/9780823288045.001.0001.

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Urban Formalism radically reimagines what it meant to “read” a brave new urban world during the transformative middle decades of the nineteenth century. At a time when contemporaries in the twin capitals of modernity in the West, New York and Paris, were learning to make sense of unfamiliar surroundings, city peoples increasingly looked to the experiential patterns, or forms, from their everyday lives in an attempt to translate urban experience into something they could more easily comprehend. Urban Formalism interrogates both the risks and rewards of an interpretive practice that depended on
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The Expected Knowledge: What can we know about anything and everything? Sivashanmugam Palaniappan, 2012.

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Book chapters on the topic "Dynamic Representation Learning"

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Feng, Hao, Yan Liu, Ziqiao Zhou, and Jing Chen. "Dynamic Network Change Detection via Dynamic Network Representation Learning." In Communications and Networking. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41114-5_48.

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Yu, Yanwei, Huaxiu Yao, Hongjian Wang, Xianfeng Tang, and Zhenhui Li. "Representation Learning for Large-Scale Dynamic Networks." In Database Systems for Advanced Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91458-9_32.

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Mendes, M. E. S., and L. Sacks. "Dynamic Knowledge Representation for e-Learning Applications." In Enhancing the Power of the Internet. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45218-8_12.

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Fathy, Ahmed, and Kan Li. "TemporalGAT: Attention-Based Dynamic Graph Representation Learning." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_32.

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Zhang, Si, Yinglong Xia, Yan Zhu, and Hanghang Tong. "Representation Learning on Dynamic Network of Networks." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch34.

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Lowe, Richard. "User-Controllable Animated Diagrams: The Solution for Learning Dynamic Content?" In Diagrammatic Representation and Inference. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25931-2_38.

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Xu, Liancheng, Xiaoxiang Wang, Lei Guo, Jinyu Zhang, Xiaoqi Wu, and Xinhua Wang. "Candidate-Aware Dynamic Representation for News Recommendation." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44195-0_23.

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Li, Yongfang, Liang Chang, Guanjun Rao, Phatpicha Yochum, Yiqin Luo, and Tianlong Gu. "Representation Learning for Knowledge Graph with Dynamic Step." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2203-7_29.

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Simon, Mathieu Cyrille, Pascal Frossard, and Christophe De Vleeschouwer. "Sequential Representation Learning via Static-Dynamic Conditional Disentanglement." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73226-3_7.

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Liu, Zihao, and Yitong Wang. "DySDGNN: Representation Learning in Dynamic Signed Directed Networks." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5572-1_19.

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Conference papers on the topic "Dynamic Representation Learning"

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Mu, Hongzhang, Shuili Zhang, Quangang Li, Tingwen Liu, and Hongbo Xu. "Dynamic Multi-Modal Representation Learning For Topic Modeling." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10688179.

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Cheng, Siyu, Jingxian Shuai, Fengrui Xiao, Dan Yang, Yuanyi Ma, and Shuangwu Chen. "GraphBot: Botnet Detection via Dynamic Graph Representation Learning." In 2024 10th International Conference on Computer and Communications (ICCC). IEEE, 2024. https://doi.org/10.1109/iccc62609.2024.10942170.

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Wang, Jingjie, Jinwei Lang, Li-Zhuang Yang, and Hai Li. "Learning Dynamic Brain Network Representation Based on Graph Mamba Architecture." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822717.

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Tang, Zhan-Yun, Ya-Rong Liu, and Pan Qin. "Visual Reinforcement Learning Using Dynamic State Representation for Continuous Motion Control." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662669.

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Wang, Ling, Yixiang Huang, and Hao Wu. "Diverse Transformation-Augmented Graph Tensor Convolutional Network for Dynamic Graph Representation Learning." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831069.

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Lei, Meng, Yipeng Wang, and Ying Zhang. "Cross-Domain Multi-Task Representation Learning for Target Recognition with Dynamic Attitudes." In 2024 IEEE INC-USNC-URSI Radio Science Meeting (Joint with AP-S Symposium). IEEE, 2024. http://dx.doi.org/10.23919/inc-usnc-ursi61303.2024.10632469.

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Cheng, Jiale, Dan Hu, Zhengwang Wu, Xinrui Yuan, and Gang Li. "Masked Momentum Contrastive Dynamic Transformer for Self-Supervised Functional Connectivity Representation Learning." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980976.

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Jiang, Liangyan, Chuang Zhu, and Yanxu Chen. "SwinSpikeFormer: Learning Comprehensive Spatial-Temporal Representation to Reconstruct Dynamic Scenes from Spike Streams." In 2024 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2024. https://doi.org/10.1109/icvrv62410.2024.00020.

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Tong, Nuo, He Gong, Shuiping Gou, Wenwei Shi, Tianhuan Li, and Jisheng Li. "Dynamic Representation Learning with Spatial-Frequency Features for Motion Compensated 4D Cone-Beam Reconstruction." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635455.

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Liu, Boming, Chen Zhang, Jin Dong, Prasanna Balaprakash, Yan Liu, and Brett Eiffert. "Enhancing Power Distribution System Resilience with Fusion-GNN: A Dynamic Graph Representation Learning Approach." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905187.

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Reports on the topic "Dynamic Representation Learning"

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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Ambekar, Neelima, Divya Jain, Vishal Patel, Arvind Sakat, Abhishek Shah, and Nagma Shah. Exploring Education's Role in Sustainable Urbanisation through PUKAR's Youth Fellowship Program. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/tesf1707.2024.

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This research explores the potential of the Youth Fellowship Program (YFP) as a supplementary urban educational intervention to formal higher education. The findings highlight the significance of the YFP as a crucial element in enabling research-oriented, problem-focused and action-oriented urban education. Such an approach complements formal urban higher education and addresses existing gaps, making contemporary urban education more responsive to the challenges faced in urban settings. Firstly, the programme recognises critical gaps in the contemporary higher education curriculum, ensuring th
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Mishra, Umakant, and Sagar Gautam. Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1891184.

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider diseas
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