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Downscaling multilayer soil moisture using parameterized depth profiles associated with environmental factors

Journal of Hydrology 2025年 661卷

作者： Zhang, Mo Ge, Yong Ma, Yuxin Jin, Yan Chen, Yingying Liu, Shaomin State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100101 China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang330022 China Jiangxi Provincial Key Laboratory of Ecological Intelligent Monitoring and Comprehensive Treatment of Watershed School of Geography and Environment Jiangxi Normal University Nanchang330022 China New South Wales Department of Climate Change Energy the Environment and Water ParramattaNSW2150 Australia School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China State Key Laboratory of Earth Surface Processes and Hazards Risk Governance Beijing Normal University Beijing100875 China

High-resolution multilayer soil moisture is valuable for water resources management, drought monitoring, and crop optimization. However, integrating process-based models into statistical downscaling methods to estimate multilayer soil moisture can increase both prediction uncertainty and computational complexity of parameterization. Here, three parameterized equations (Peak, Trough, and Balance) were proposed to quantify soil moisture profiles associated with environmental factors, and downscale multilayer soil moisture. Global (0–40 cm) and local (0–10, 10–20, and 20–40 cm) fitting strategies represented the vertical extent of environmental influences. Three machine learning models were employed to downscale satellite soil moisture and predict depth parameters, which were then used to generate sub-surface soil moisture. The results indicated that the local fitting strategy consistently outperformed the global strategy across all depths, reducing the mean absolute error (MAE) and root mean square error (RMSE) by 81.8 % and 80.4 %, and increasing the coefficient of determination (R2) by 22.5 %. Furthermore, local fitting revealed distinct nonlinear and linear patterns within the 0–20 cm and 20–40 cm intervals, respectively. The prediction accuracy of sub-surface soil moisture was improved compared to surface results, with MAE and RMSE reduced by 30.2 %–60.7 % and 34.9 %–56.7 %, respectively. The modified local fitting strategy further enhanced prediction performance, with the Peak–Trough–Balance combination recommended as the optimal configuration. Future studies should account for variations in soil and vegetation cover, refine interval division by incorporating soil-forming processes, and adopt advanced uncertainty quantification methods to enhance its adaptability and robustness. This study provides a reference for multilayer soil moisture downscaling in large-scale regions with depth variability and environmental heterogeneity. © 2025 Elsevier B.V.

关键词： Soil moisture

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Dual topology and versatile Rashba-split surface state configurations in 2M−WS2 and 2M−WSe2

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Physical Review B 2025年第11期111卷 115151-115151页

作者： Lixuan Xu Yuqiang Fang Fuqiang Huang Shihao Zhang Shiheng Liang Yulin Chen Lexian Yang Zhongkai Liu Nan Xu Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Physics and Electronics Hunan University Changsha 410082 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China ShanghaiTech Laboratory for Topological Physics Shanghai 201210 China Department of Physics University of Oxford Oxford OX1 3PU United Kingdom State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Institute for Advanced Studies (IAS) Wuhan University Wuhan 430072 China Wuhan Institute of Quantum Technology Wuhan 430206 China

The 2M-phase transition metal dichalcogenides have recently attracted intensive research interest due to their rich topological and superconducting phase diagrams. Apart from the topological surface states of 2M−WS2 near Γ that originated from the strong topological order, using angle-resolved photoemission spectroscopy, we discover additional Rashba-split states on the surfaces of both 2M−WS2 and 2M−WSe2, which extend in large momentum-energy regions. First-principles calculations well reproduce these states and attribute them to the weak topological orders. The calculations further indicate that the surface state connecting configurations are tunable under moderate pressure, suggesting that 2M−WS2 and WSe2 are promising platforms to study topological phase transition and explore topological superconductivity.

关键词： Photoacoustic spectroscopy

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EC²Detect: Real-Time Online Video Object Detection in Edge-Cloud Collaborative IoT

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IEEE Internet of Things Journal 2022年第20期9卷 20382-20392页

作者： Siyan Guo Cong Zhao Guiqin Wang Jiaqing Yang Shusen Yang National Engineering Laboratory for Big Data Analytics and the School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computing Imperial College London London U.K. National Engineering Laboratory for Big Data Analytics and the School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Industrial Artificial Intelligence Center Pazhou Laboratory Guangzhou China

Video object detection is a fundamental technology of intelligent video analytics for Internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to $4.77\times $ faster), E2E latency (up to $8.12 \times $ lower), and edge-cloud bandwidth occupation ( $17 \times $ lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available at https://***/ECCDetect/ECCDetect .

关键词： Object detection Image edge detection Streaming media Internet of Things Collaboration Visual analytics Feature extraction

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Cross-heterogeneous Domain Authentication Scheme Based on Blockchain

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 92-100页

作者： Jing Liu Yixin Liu Yingxu Lai Rongchen Li Siyu Wu Sami Mian College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Shaanxi Key Laboratory of Network and System Security Xi’an 710071China Science and Technology on Information Assurance Laboratory Beijing 100072China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Fan Gongxiu Honors College Beijing University of TechnologyBeijing 100124China University of Pittsburgh PittsburghPA 15260USA

With the rising popularity of the Internet and the development of big data technology,an increasing number of organizations are opting to cooperate across domains to maximize their *** organizations use public key infrastructure to ensure security in accessing their data and ***,with the continuous development of identity-based encryption(IBE)technology,small-and medium-sized enterprises are increasingly using IBE to deploy internal authentication *** solve the problems that arise when crossing heterogeneous authentication domains and to guarantee the security of the certification process,we propose using blockchain technology to establish a reliable cross-domain authentication *** the distributed and tamper-resistant characteristics of the blockchain,we design a cross-domain authentication model based on blockchain to guarantee the security of the heterogeneous authentication process and present a cross-domain authentication protocol based on *** model does not change the internal trust structure of each authentication domain and is highly ***,on the premise of ensuring security,the process of verifying the signature of the root certificate in the traditional cross-domain authentication protocol is improved to verify the hash value of the root certificate,thereby improving the authentication *** developed prototype exhibits generality and simplicity compared to previous methods.

关键词： public key infrastructure identity-based encryption cross-domain authentication blockchain

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Gsgm: A Group-Sequence-Aware Graph Model for Personalized News Recommender system

SSRN

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SSRN 2024年

作者： Lv, Pengtao Zhang, Qinghui Guan, Zhenhan Deveci, Muhammet Shi, Lei Zhong, Kaiyang Wang, Jingping Xu, Heliang Zhang, Yana Ministry of Education Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China Henan Grain Big Data Analysis and Application Engineering Research Center Henan University of Technology Henan Zhengzhou450001 China The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul4940 Turkey State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages Minzu University of China Beijing100081 China Southwestern University of Finance and Economics China

News recommender system is an important method of helping readers identify interesting stories. Although human behaviors exhibit sequential patterns, most current studies on news recommendations ignore the influence of reading sequences. Leveraging sequential patterns in news recommendation is, however, very challenging, as the capture of reading behavior sequence at the individual level may be impossible due to severity of sparsity in the field of news. To address this challenge, we proposed a group-sequence-aware graph model (GSGM) to learn representative reading sequences of groups instead of individuals, where groups are viewed as latent variables and automatically learnt from users' reading behaviours. Besides, when readers select different groups, they are likely to have distinct reading preferences. This finding is used to learn group-dependent personal reading preferences in GSGM. A wide range of experiments are conducted to verify the efficiency and effectiveness of the GSGM on real datasets. Experimental results demonstrate that GSGM outperforms the state-of-the-art competing methods. © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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A Dynamic Multi-Objective Evolutionary Algorithm Based on Niche Prediction Strategy

SSRN

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SSRN 2022年

作者： Zheng, Jinhua Zhang, Bo Zou, Juan Yang, Shengxiang Hu, Yaru Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

In reality, many multi-objective optimization problems are dynamic. The Pareto optimal front (PF) or Pareto optimal solution (PS) of these dynamic multi-objective problems (DMOPs) changes as the environment change. Therefore, solving such problems requires an optimization algorithm that can quickly track the PF or PS after an environment change. Prediction-based response mechanism is a common method used to deal with environmental changes, which is commonly known as center point-based prediction. However, if the predicted direction of the center point is inaccurate, the predicted population will be biased towards one side. In this paper, we propose a niche prediction strategy based on center and boundary points (PCPB) to solve the dynamic multi-objective optimization problems, which consists of three steps. After environmental changes are detected, the first step is to divide the niche, dividing different individuals in the PS into different niche populations. The second step is to independently predict different niches, and select individuals with good convergence and distribution in the niche to predict the individuals that will produce the next generation. Finally, some different individuals are randomly generated in the next possible PS area to ensure the diversity of the population. To verify whether our proposed strategy is effective and competitive, PCPB was compared with five state-of-the-art strategies. The experimental results show that PCPB performed competitively in solving dynamic multi-objective optimization problems, which proves that our algorithm has good competitiveness. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Siamese Delta Network with Multimodality Fusion for systemic Treatment Response Prediction in Pancreatic Cancer*

Siamese Delta Network with Multimodality Fusion for Systemic...

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

作者： Peijun Hu Xiang Li Na Lu Xueli Bai Tingbo Liang Jingsong Li Research Center for Healthcare Data Science Zhejiang Laboratory Hangzhou China Department of Hepatobiliary and Pancreatic Surgery First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Zhejiang Provincial Key Laboratory of Pancreatic Disease Hangzhou China Zhejiang University Cancer Center Hangzhou China Engineering Research Center of EMR and Intelligent Expert System Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

systemic treatment is a main way for pancreas cancer patients that are ineligible for surgery. A subgroup of patients showed good response to systemic treatment and the rest received limited benefits. CT images provide a non-invasive way to assess the treatment response. Alternative non-image methods include radiology analysis, tumor marker analysis and combination analysis. To combine the image and non-image data, we propose the Siamese Delta Network with Multimodality Fusion (SDN-MF) to predict systemic treatment response in an end-to-end way. First, a Siamese Delta Network (SDN) is designed to process pre-treatment and pre-surgery CT images and get the image feature changes to predict response. Then, patients’ characteristics from EMR and alternative analysis results forms non-image data, which is incorporated into SDN with a multimodality fusion (MF) module. The proposed SDN-MF is evaluated on a private dataset and achieves average AUC value of 0.883 with five cross-validation. Comparison among image-only, non-image-only, and fusion models verifies the superior of multimodality model in predicting systemic treatment response of pancreas cancer patients.

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

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Three-way conflict analysis model via the best-worst method: Balancing subjective preferences and objective data on incomplete and dispersed systems

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International Journal of Approximate Reasoning 2025年 186卷

作者： Junjie Zhu Qinghua Zhang Nanfang Luo Fan Liu Longjun Yin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing 400065 China Tourism Multi-source Data Perception and Decision Technology Key Laboratory of the Ministry of Culture and Tourism Chongqing 400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing 400065 China

In real-world environments, different issues contribute to conflicts with varying weights. However, current conflict analysis weighting models face significant limitations when dealing with incomplete data and dispersed knowledge. Objective methods are sensitive to missing values and struggle to accurately capture authentic preferences, while subjective approaches lack systematic evaluation criteria, leading to substantial randomness in weight assignments. Therefore, a three-way conflict analysis model via the best-worst method is proposed, which is combined with a correlation coefficient method to balance subjective preferences and objective data. First, the trisection of agent pairs is derived through Bayesian minimum risk. Subsequently, a new conflict distance function is defined on the incomplete information system to provide a more precise measurement of conflict degrees. Then, for incomplete and dispersed information systems, a maximal coalition-based agent partitioning algorithm is designed, along with a new weighted voting mechanism to aggregate dispersed knowledge. Finally, the scientific transparency of the weighting process, as well as the robustness and feasibility of the model, are demonstrated through experimental analysis.

关键词： Distributed database systems

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