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Improving Policy-Oriented Agent-Based Modeling with History Matching: A Case Study

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arXiv 2024年

作者： O’Gara, David Kerr, Cliff C. Klein, Daniel J. Binois, Mickaël Garnett, Roman Hammond, Ross A. Division of Computational and Data Sciences Washington University in St. Louis St. LouisMO United States Institute for Disease Modeling Bill & Melinda Gates Foundation SeattleWA United States Acumes Team Université Côte d’Azur Inria Sophia Antipolis France Department of Computer Science McKelvey School of Engineering Washington University in St. Louis St. LouisMO United States Public Health Brown School Washington University in St. Louis St. LouisMO United States Center on Social Dynamics and Policy Brookings Institution WashingtonDC United States The Santa Fe Institute Santa FeNM United States

Advances in computing power and data availability have led to growing sophistication in mechanistic mathematical models of social dynamics. Increasingly these models are used to inform real-world policy decision-making, often with significant time sensitivity. One such modeling approach is agent-based modeling, which offers particular strengths for capturing spatial and behavioral realism, and for in-silico experiments (varying input parameters and assumptions to explore their downstream impact on key outcomes). To be useful in the real-world, these models must be able to qualitatively or quantitatively capture observed empirical phenomena, forming the starting point for subsequent experimentation. Computational constraints often form a significant hurdle to timely calibration and policy analysis in high-resolution agent-based models. In this paper, we present a technical solution to address this bottleneck, substantially increasing efficiency and thus widening the range of utility for policy models. We illustrate our approach with a case study using a previously published and widely used epidemiological model. © 2024, CC BY.

关键词： Digital elevation model

Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?

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arXiv 2024年

作者： Zhang, Hongyuan Wang, Ching-Wei Muzakky, Hikam Dai, Juan Li, Xuguang Ma, Chenglong Wu, Qian Cui, Xianan Xu, Kunlun He, Pengfei Guo, Dongqian Wang, Xianlong Lee, Hyunseok Zhong, Zhangnan Zhu, Zhu Huang, Bingsheng Medical AI Lab School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan Department of Stomatology Shenzhen University General Hospital Shenzhen University Shenzhen518055 China Institute of Stomatological Research Shenzhen University Shenzhen518055 China Chohotech Technology Co. Ltd. Lianchuang Street Yuhang District Hangzhou311422 China Singapore School of Computer Science Wuhan University Wuhan430072 China Wangxuan Institute of Computer Technology Peking University Beijing100871 China School of Artificial Intelligence Xidian University Xi’an710126 China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China The Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China Digital Healthcare Department Daegu-Gyeongbuk Medical Innovation Foundation Daegu427724 Korea Republic of School of Life Science South China Normal University Guangzhou510631 China Information and Data Department Children’s Hospital Zhejiang University School of Medicine Hangzhou310000 China

Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCAI CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719% and a mean radial error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https: //***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

A systematic review of challenges and proposed solutions in modeling multimodal data

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arXiv 2025年

作者： Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine Institute of General Practice/Family Medicine Faculty of Medicine Medical Center University of Freiburg Elsässer Straße 2M Freiburg79110 Germany Freiburg Center for Data Analysis Modeling and AI University of Freiburg Ernst-Zermelo-Str. 1 Freiburg79104 Germany Institute of Medical Biometry and Statistics Faculty of Medicine Medical Center University of Freiburg Stefan-Meier-Straße 26 Freiburg79104 Germany Institute for Applied Medical Informatics University Medical Center Hamburg-Eppendorf Martinistraße 52 Hamburg20251 Germany Institute of Medical Systems Biology Ulm University Helmholtzstraße 16 Ulm89081 Germany Department of Computer Science Faculty of Engineering University of Freiburg Georges-Köhler-Allee 051 Freiburg79110 Germany

Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications. © 2025, CC BY.

关键词： Clinical research

The importance of analysing data from instrumented infrastructure 2

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The importance of analysing data from instrumented infrastru...

2nd International Conference on Smart Infrastructure and Construction: Driving data-Informed Decision-Making, ICSIC 2019

作者： Lau, F.D.-H. Adams, Niall M. Lloyd's Register Foundation Programme on Data-Centric Engineering Alan Turing Institute London United Kingdom Department of Mathematics Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom

ISBN: (纸本)9780727764669

data acquired from instrumented infrastructure are at the heart of structural health monitoring. Engineers use information extracted from these data to improve their understanding of how the structures respond to stimuli. Little attention and emphasis, however, has been given to analysing the data before using a particular model. Analysing the data using appropriate statistical tools reveals structure that should inform the types of models deployed. In this paper, we emphasise the importance of statistically analysing data before fitting any model. These ideas are illustrated using data collected from a fibre-optic sensor network installed on railway bridges. These data capture the spacetime response of the bridge through a sensor network. During periods of rest, we reveal that the collective sensor data exhibit a distinct dynamic latent structure, which is attributable only to the sensor system. To our knowledge this latent structure has never been documented before. This case study will illustrate that analysing data before implementing any procedure plays a vital role in structural health monitoring applications and if not done correctly, or at all, may lead to erroneous and misleading conclusions. © International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving data-Informed Decision-Making.

关键词： Structural health monitoring

The energy revolution: Cyber physical advances and opportunities for smart local energy systems

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arXiv 2021年

作者： Verba, Nandor Gaura, Elena McArthur, Stephen Konstantopoulos, George Wu, Jianzhoug Fan, Zhong Athanasiadis, Dimitrios Monasterios, Pablo Rodolfo Baldivieso Morris, Euan Hardy, Jeffrey Centre for Data Science Coventry University United Kingdom Department of Electronic and Electrical Engineering University of Strathclyde United Kingdom Department of Automatic Control & Systems Engineering University of Sheffield United Kingdom Department of Electrical and Electronic Engineering Cardiff University United Kingdom School of Computing and Mathematics Keele University United Kingdom Grantham Institute Imperial College London United Kingdom

The Sensorium competition on predicting large-scale mouse primary visual cortex activity

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arXiv 2022年

作者： Willeke, Konstantin F. Fahey, Paul G. Bashiri, Mohammad Pede, Laura Burg, Max F. Blessing, Christoph Cadena, Santiago A. Ding, Zhiwei Lurz, Konstantin-Klemens Ponder, Kayla Muhammad, Taliah Patel, Saumil S. Ecker, Alexander S. Tolias, Andreas S. Sinz, Fabian H. International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Institute for Theoretical Physics University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Electrical and Computer Engineering Rice University Houston United States

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the SENSORIUM benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (SENSORIUM) or stimulus plus behavior (SENSORIUM+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. © 2022, CC BY-NC-ND.

关键词： Neural networks

Gaining water bodies by climate change benefits water crisis mitigation in central Asia

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science Bulletin 2025年

作者： Su, Yanan Chen, Shengqian Sui, Yijie Li, Xin Xu, Jinhao Che, Xianghong Xie, Tingting Chen, Jianhui Sheng, Yongwei Feng, Min Chen, Fahu State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Research Center of Geospatial Big Data Application Chinese Academy of Surveying and Mapping Beijing100830 China College of Water Resources and Architectural Engineering Tarim University Alar843300 China Key Laboratory of Western China's Environmental Systems Ministry of Education College of Earth and Environmental Sciences Lanzhou University Lanzhou730000 China Department of Geography University of California Los AngelesCA90095 United States Academy of Plateau Science and Sustainability Qinghai Normal University Xining810016 China

Central Asia (CA) faces a severe water crisis exemplified by the shrinking Aral Sea. However, little is known about the entire region, particularly the numerous small water bodies that are vulnerable to climate change yet vital for regional sustainability. We examined water bodies as small as 0.0045 km2 across CA from 1992 to 2020, identifying 66,215 water bodies in 2020, 82.2% of which were previously unstudied. In contrast to the well-documented decline of the Aral Sea, other water bodies have expanded by 10.7% (8714.3 km2), with a net gain of 15,831 lakes since 1992. These findings challenge the perception of a drying CA and provide evidence of a warm-wet climate trend, which is redistributing water resources and creating opportunities for transforming water management to address the long-standing water crisis in CA. © 2025 science China Press

关键词： Central Asia Climate Change Small Water Bodies Water Crisis Water Resource Management

Non-Equilibrium Time-Relaxation Kinetic Model for Compressible Turbulence Modeling

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SSRN

SSRN 2022年

作者： Cao, Guiyu Pan, Liang Xu, Kun Wan, Minping Chen, Shiyi Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Guangdong Shenzhen518055 China Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Laboratory of Mathematics and Complex Systems School of Mathematical Sciences Beijing Normal University Beijing100875 China Shenzhen Research Institute Hong Kong University of Science and Technology Guangdong Shenzhen518057 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Guangdong Shenzhen518055 China

For the first time, the non-equilibrium time-relaxation kinetic model (NTRKM) is proposed for compressible turbulence modeling on unresolved grids. Within the non-equilibrium time-relaxation framework, NTRKM is extended in the form of modified Bhatnagar-Gross-Krook model. Based on the first-order Chapman-Enskog expansion, NTRKM connects with the six-variable macroscopic governing equations. The first five governing equations correspond to the conservative laws in mass, momentum and total energy, while the sixth equation governs the evolution of unresolved turbulence kinetic energy Kutke. The unknowns in NTRKM, including turbulent relaxation time and source term, are determined by essential gradient-type assumption and standard dynamic modeling approach. Current generalized kinetic model on unresolved grids consequently offers a profound mesoscopic understanding for one-equation subgrid-scale turbulence kinetic energy Ksgs model in compressible large eddy simulation. To solve NTRKM accurately and robustly, a non-equilibrium gas-kinetic scheme is developed, which succeeds the well-established gas-kinetic scheme for simulating Navier-Stokes equations. Three-dimensional decaying compressible isotropic turbulence and temporal compressible plane mixing layer on unresolved grids are simulated to evaluate the generalized kinetic model and non-equilibrium gas-kinetic scheme. The performance of key turbulent quantities up to second-order statistics confirms that NTRKM is comparable with the widely-used eddy-viscosity Smagorinsky model (SM) and dynamic Smagorinsky model (DSM). Specifically, compared with the DNS solution in temporal compressible plane mixing layer, the performance of NTRKM is much closer with DSM and better than SM. This study provides a workable approach for compressible turbulence modeling on unresolved grids, enriching the understanding of turbulence modeling within the non-equilibrium time-relaxation framework. © 2022, The Authors. All rights reserved.

关键词： Navier Stokes equations

Persistent eutrophication and hypoxia in the coastal ocean

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Cambridge Prisms: Coastal Futures 2023年 1卷 e19-e19页

作者： Dai, Minhan Zhao, Yangyang Chai, Fei Chen, Mingru Chen, Nengwang Chen, Yimin Cheng, Danyang Gan, Jianping Guan, Dabo Hong, Yuanyuan Huang, Jialu Lee, Yanting Leung, Kenneth Mei Yee Lim, Phaik Eem Lin, Senjie Lin, Xin Liu, Xin Liu, Zhiqiang Luo, Ya-Wei Meng, Feifei Sangmanee, Chalermrat Shen, Yuan Uthaipan, Khanittha Wan Talaat, Wan Izatul Asma Wan, Xianhui Sean Wang, Cong Wang, Dazhi Wang, Guizhi Wang, Shanlin Wang, Yanmin Wang, Yuntao Wang, Zhe Wang, Zhixuan Xu, Yanping Yang, Jin-Yu Terence Yang, Yan Yasuhara, Moriaki Yu, Dan Yu, Jianmin Yu, Liuqian Zhang, Zengkai Zhang, Zhouling State Key Laboratory of Marine Environmental Science Xiamen University Xiamen China College of Ocean and Earth Sciences Xiamen University Xiamen China Fujian Institute for Sustainable Oceans Xiamen University Xiamen China China-ASEAN College of Marine Sciences Xiamen University Malaysia Sepang Malaysia College of the Environment and Ecology Xiamen University Xiamen China Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies Xiamen University Xiamen China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Thrust of Earth Ocean and Atmospheric Sciences Department of Ocean Science Department of Mathematics The Hong Kong University of Science and Technology Kowloon Hong Kong Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China School of Biological Sciences Area of Ecology and Biodiversity Swire Institute of Marine Science Institute for Climate and Carbon Neutrality Musketeers Foundation Institute of Data Science State Key Laboratory of Marine Pollution The University of Hong Kong Hong Kong Hong Kong State Key Laboratory of Marine Pollution and Department of Chemistry City University of Hong Kong Hong Kong Hong Kong Institute of Ocean and Earth Sciences University of Malaya Kuala Lumpur Malaysia Department of Marine Sciences University of Connecticut Groton CT United States Department of Ocean Science and Engineering Southern University of Science and Technology Shenzhen China Marine and Coastal Resources Research and Development Institute Department of Marine and Coastal Resources Bangkok Thailand Institute of Oceanography and Environment Universiti Malaysia Terengganu Kuala Terengganu Malaysia Department of Geosciences Princeton University Princeton NJ United States State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Ministry of Natural Resour

Coastal eutrophication and hypoxia remain a persistent environmental crisis despite the great efforts to reduce nutrient loading and mitigate associated environmental damages. Symptoms of this crisis have appeared to spread rapidly, reaching developing countries in Asia with emergences in Southern America and Africa. The pace of changes and the underlying drivers remain not so clear. To address the gap, we review the up-To-date status and mechanisms of eutrophication and hypoxia in global coastal oceans, upon which we examine the trajectories of changes over the 40 years or longer in six model coastal systems with varying socio-economic development statuses and different levels and histories of eutrophication. Although these coastal systems share common features of eutrophication, site-specific characteristics are also substantial, depending on the regional environmental setting and level of social-economic development along with policy implementation and management. Nevertheless, ecosystem recovery generally needs greater reduction in pressures compared to that initiated degradation and becomes less feasible to achieve past norms with a longer time anthropogenic pressures on the ecosystems. While the qualitative causality between drivers and consequences is well established, quantitative attribution of these drivers to eutrophication and hypoxia remains difficult especially when we consider the social economic drivers because the changes in coastal ecosystems are subject to multiple influences and the cause-effect relationship is often non-linear. Such relationships are further complicated by climate changes that have been accelerating over the past few decades. The knowledge gaps that limit our quantitative and mechanistic understanding of the human-coastal ocean nexus are identified, which is essential for science-based policy making. Recognizing lessons from past management practices, we advocate for a better, more efficient indexing system of coastal eutrophica

关键词： eutrophication harmful algal bloom hypoxia regime shift socioeconomic-ecological system