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SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 238-256页

作者： Li, Ke Bai, Kaixu Fu, Peng Jiao, Penglong Chen, He Huang, Xinqing Liu, Chaoshun Chang, Ni-Bin School of Geographic Sciences East China Normal University Shanghai200241 China Shanghai200063 China School of Plant Environmental and Soil Sciences Louisiana State University AgCenter Louisiana State University Baton RougeLA70803 United States Shanghai AI Laboratory Shanghai200030 China Shanghai Data & Application Center of High-Resolution Earth Observation System Shanghai Academy of Spaceflight Technology Shanghai201109 China Department of Civil Environmental and Construction Engineering University of Central Florida OrlandoFL32816 United States

Quantification of anthropogenic methane emissions from coal mines and oil & gas facilities is essential to global methane accounting and management. Matched Filter (MF), a classic method for point-source methane emission detection, was proven less effective over heterogeneous land covers given substantial false positives of methane enhancement retrievals due to large uncertainties in background spectrum estimates. To address this challenge, we developed an enhanced MF method, i.e., Sparse Spectral Reconstruction enhanced Matched Filter (SSRMF), to empower accurate methane emission detection in complex terrain from satellite-based hyperspectral images. Specifically, a spectral matrix decomposition and low-rank reconstruction approach was developed to accurately predict background spectrum for each pixel, rather than applying the regional average spectrum that is commonly used in traditional MF method and its alike. Meanwhile, a spatial continuity regularization term was incorporated in the methane enhancement estimation cost function to ensure spatial coherence of methane plumes. Illustration results with synthetic GaoFen-5 hyperspectral images demonstrate the superior advantages of SSRMF over other MF methods, reducing methane enhancement retrieval errors by 80 % and 2.8 times in scenarios with emission flux below 1,000 kg/h. Also, SSRMF enabled to reduce false positives of methane enhancement by 70 % in complex terrain, effectively suppressing artifacts over land covers resembling the methane spectral absorption curve in shortwave infrared bands. Additionally, SSRMF operates 20 % faster than other MF methods by avoiding an iterative optimization of the background spectrum. The error analysis results demonstrate the high robustness of SSRMF against drastic variations in land albedo, aerosol loading, and water vapor, making it more flexible to heterogeneous terrains. By applying SSRMF to 97 clear-sky GaoFen-5A/B hyperspectral images observed during 2019–2022, 126 met

关键词： Cost functions

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UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs 34

UICopilot: Automating UI Synthesis via Hierarchical Code Gen...

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34th ACM Web Conference, WWW 2025

作者： Gui, Yi Wan, Yao Li, Zhen Zhang, Zhongyi Chen, Dongping Zhang, Hongyu Su, Yi Chen, Bohua Zhou, Xing Jiang, Wenbin Zhang, Xiangliang Huazhong University of Science and Technology Wuhan China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology China

ISBN: (纸本)9798400712746

Automating the synthesis of User Interfaces (UIs) plays a crucial role in enhancing productivity and accelerating the development lifecycle, reducing both development time and manual effort. Recently, the rapid development of Multimodal Large Language Models (MLLMs) has made it possible to generate front-end Hypertext Markup Language (HTML) code directly from webpage designs. However, real-world webpages encompass not only a diverse array of HTML tags but also complex stylesheets, resulting in significantly lengthy code. The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. The core idea of UICopilot is to decompose the generation process into two stages: first, generating the coarse-grained HTML hierarchical structure, followed by the generation of fine-grained code. To validate the effectiveness of UICopilot, we conduct experiments on a real-world dataset, i.e., WebCode2M. Experimental results demonstrate that UICopilot significantly outperforms existing baselines in both automatic evaluation metrics and human evaluations. Specifically, statistical analysis reveals that the majority of human annotators prefer the webpages generated by UICopilot over those produced by GPT-4V. © 2025 Copyright held by the owner/author(s).

关键词： Code Generation Design to Code UI Automation

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WebCode2M: A Real-World dataset for Code Generation from Webpage Designs 34

WebCode2M: A Real-World Dataset for Code Generation from Web...

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34th ACM Web Conference, WWW 2025

作者： Gui, Yi Li, Zhen Wan, Yao Shi, Yemin Zhang, Hongyu Su, Yi Chen, Bohua Chen, Dongping Wu, Siyuan Zhou, Xing Jiang, Wenbin Jin, Hai Zhang, Xiangliang Huazhong University of Science and Technology Wuhan China Peking University Beijing China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology China

ISBN: (纸本)9798400712746

Automatically generating webpage code from webpage designs can significantly reduce the workload of front-end developers, and recent Multimodal Large Language Models (MLLMs) have shown promising potential in this area. However, our investigation reveals that most existing MLLMs are constrained by the absence of high-quality, large-scale, real-world datasets, resulting in inadequate performance in automated webpage code generation. To fill this gap, this paper introduces WebCode2M, a new dataset comprising 2.56 million instances, each containing a design image along with the corresponding webpage code and layout details. Sourced from real-world web resources, WebCode2M offers a rich and valuable dataset for webpage code generation across a variety of applications. The dataset quality is ensured by a scoring model that filters out instances with aesthetic deficiencies or other incomplete elements. To validate the effectiveness of WebCode2M, we introduce a baseline model based on the Vision Transformer (ViT), named WebCoder, and establish a benchmark for fair comparison. Additionally, we introduce a new metric, TreeBLEU, to measure the structural hierarchy recall. The benchmarking results demonstrate that our dataset significantly improves the ability of MLLMs to generate code from webpage designs, confirming its effectiveness and usability for future applications in front-end design tools. Finally, we highlight several practical challenges introduced by our dataset, calling for further research. © 2025 Copyright held by the owner/author(s).

关键词： Code Generation dataset Design to Code UI Automation

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Graded porous scaffold mediates internal fluidic environment for 3D in vitro mechanobiology

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Computers in Biology and Medicine 2025年 186卷 109674-109674页

作者： De Rosa, Chiara Angela Wright, Christopher J. Xiong, Yi Del Giudice, Francesco Zhao, Feihu Department of Biomedical Engineering Faculty of Science and Engineering Swansea University Swansea United Kingdom Zienkiewicz Institute for Modelling Data and AI Swansea University Swansea United Kingdom School of System Design and Intelligent Manufacturing Southern University of Science and Technology Guangdong Province Shenzhen China Complex Fluid Research Group Department of Chemical Engineering Faculty of Science and Engineering Swansea University Swansea United Kingdom

Most cell types are mechanosensitive, their activities such as differentiation, proliferation and apoptosis, can be influenced by the mechanical environment through mechanical stimulation. In three dimensional (3D) mechanobiological in vitro studies, the porous structure of scaffold controls the local mechanical environment that applied to cells. Many previous studies have focused on the topological design of homogeneous scaffold struts. However, the impact of scaffold inhomogeneity on the mechanical environment, which is essential in mechanobiological application (e.g. for multi-cells co-culture), remains elusive. In this study, we use a computational fluid dynamics (CFD) approach together with data analysis to study the influence of a porosity gradient (10 %–30 % porosity difference) on the local and global mechanical environment (wall shear stress - WSS) within the commonly used structures of triple periodic minimal surfaces (TPMS). In addition, the anisotropy of internal WSS and scaffold permeability caused by the porosity gradient is investigated. It is found that the influence of anisotropy on the average WSS and permeability is up to 11 % and 31 %, respectively. These results, as theoretical references will be useful to tissue engineers and mechanobiologists for scaffold design and in vitro experiment planning such as integrated use of graded scaffold and bioreactors for specific cell types. © 2025 The Authors

关键词： Shear stress

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Cybersecurity Enhancement in IoT-Enabled Public Health Information systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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2025 ai-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Mishra, Mahesh Kumar Trivedi, Lalit Mohan Farooq, Mansoor Nimma, Divya Alazzam, Malik Bader Kiran Bala, B. Delta System and Software Inc 2320 Grover Cleveland Dr McKinneyTX United States Department of ASH Moradabad Institute of Technology UP Moradabad India Department of Cybersecurity American University of Bahrain Bahrain University of Southern Mississippi Data Analyst in UMMC United States Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (纸本)9798331536336

The rising use of IoT devices in Public Health Information systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats. © 2025 IEEE.

关键词： Cyber attacks

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STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting 27th

STMAE: Spatial Temporal Masked Auto-Encoder for Traffic For...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Cai, Chengyou Wang, Xiaoxiao Wang, Jianjia Yao, Junfeng Qian, Quan Song, Jun School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Shentong Metro Co. Ltd. Shanghai China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou China CSSC Seago System Technology Co. Ltd. Shanghai China Department of Geography Hong Kong Baptist University Hong Kong

ISBN: (纸本)9783031781681

The advancement of intelligent transportation systems underscores the importance of data-driven approaches in traffic forecasting, which plays a crucial role in tasks such as traffic signal control and route guidance, among others. However, the inherent uncertainty stemming from regional traffic dynamics, coupled with intricate spatio-temporal correlations, poses formidable challenges to accurate traffic prediction. Moreover, the complexities inherent in sequence forecasting across varying scales further exacerbate the accuracy dilemma. Recognizing the need for integrating information across spatial and temporal dimensions to enhance forecasting precision, a novel solution termed Spatial Temporal Masked Autoencoder (STMAE) is introduced. The STMAE framework addresses these challenges through a two-stage learning process. In the pre-training phase, an autoencoder architecture is employed to extract spatio-temporal features from the data. In the fine-tuning phase, the pre-trained encoder of the STMAE model undergoes further refinement to specifically target traffic forecasting tasks. Extensive evaluations validate the effectiveness of the proposed STMAE model. Notably, STMAE demonstrates competitive performance, achieving 3.32 Vehs MAE for long-term (60 min) traffic forecasting while operating within a reduced computational budget. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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Candidate Evaluation with Multimodal data-Driven for Recruitment 27th

Candidate Evaluation with Multimodal Data-Driven for Recru...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and ai interviews. However, current person-job matching methods lack substantial data support, while ai interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal ai interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal ai interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

arXiv

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arXiv 2025年

作者： Xia, Xiaobo Liu, Xiaofeng Liu, Jiale Fang, Kuai Lu, Lu Oymak, Samet Currie, William S. Liu, Tongliang School of Computer Science University of Sydney Australia Department of Statistics and Data Science Yale University United States Michigan Institute for Data and AI in Society University of Michigan United States School for Environment and Sustainability University of Michigan United States College of Information Science and Technology Penn State University United States Department of Earth System Science Stanford University United States Department of Electrical Engineering and Computer Science University of Michigan United States

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, offer trans-formative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges including fairness, uncertainty, interpretability, robustness, generalizability, and reproducibility. In this work, we present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model predicting 20 water quality variables (encompassing physical/chemical processes, geochemical weathering, and nutrient cycling) across 482 U.S. basins. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics, the inherent complexity of biogeochemical processes, and variable predictability, emphasizing critical performance fairness concerns. We further propose methodological frameworks for quantitatively evaluating critical aspects of trustworthiness, including uncertainty, interpretability, and robustness, identifying key limitations that could challenge reliable real-world deployment. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management and provides a pathway to offering critical insights for researchers, decision-makers, and practitioners seeking to leverage artificial intelligence (ai) responsibly in environmental management. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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Cybersecurity Enhancement in IoT-Enabled Public Health Information systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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ai-Driven Smart Healthcare for Society 5.0

作者： Mahesh Kumar Mishra Lalit Mohan Trivedi Mansoor Farooq Divya Nimma Malik Bader Alazzam B Kiran Bala Sr SAP Consultant Delta System and Software Inc Texas USA Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Cybersecurity American University of Bahrain Bahrain PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

关键词： Training Accuracy Surveillance Bidirectional long short term memory Threat assessment Internet of Things Computer security Public healthcare Information systems Testing

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