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Impact of net-zero emissions on atmospheric CO_(2) concentration in China: Ideal simulations based on the GEOS-Chem model

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Science China Earth Sciences 2025年第3期68卷 867-881页

作者： Jingye TAN Jun WANG Huiqin MAO Hengmao WANG Zhiqiang LIU Meirong WANG Ran YAN Xunmei WANG Fei JIANG Frontiers Science Center for Critical Earth Material Cycling International Institute for Earth System ScienceNanjing UniversityNanjing 210023China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China Satellite Environmental Application Center Ministry of Environmental ProtectionBeijing 100094China CMA Key Open Laboratory of Transforming Climate Resources to Economy Chongqing Institute of Meteorological SciencesChongqing 401147China State Key Laboratory of Climate System Prediction and Risk Management Key Laboratory of Meteorological DisasterMinistry of EducationCollaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjing 210044China Xizang Field Station for Scientific Observation and Research on Atmospheric Water Cycle in Mêdog Xigazêand Mêdog National Climate ObservatoryXizang Meteorological ServiceLhasa 850000China

Elevated atmospheric carbon dioxide(CO_(2)) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to curb the rising CO_(2) concentrations. To investigate the impact of China's carbon neutrality goal on atmospheric CO_(2) concentrations, we conducted a series of ideal simulations from 2015 to 2019 using a global 3D chemistry transport model, Goddard Earth Observing system Chemistry(GEOS-Chem). Compared with the column-averaged dry-air mole fraction of atmospheric CO_(2) (XCO_(2) ) from Orbiting Carbon Observatory-2(OCO-2) and surface CO_(2) measurements in Obs Pack, we find that GEOS-Chem effectively reproduces the spatiotemporal variability of CO_(2) . The model exhibits a root mean square error(RMSE) of 1.51 ppm(R^(2)=0.89) for OCO-2 XCO_(2) in China and 2.65 ppm(R^(2)=0.75) for surface CO_(2) concentrations at the WLG station. Further, compared to 2.83 ppm yr^(-1)in the control experiment, we suggest that net-zero CO_(2) emissions in China decelerate the increasing trends of XCO_(2) to 1.81 ppm yr^(-1),making a decrease of approximately 35.89%. Meanwhile, the seasonal cycle amplitude(SCA) of XCO_(2) is moderately reduced from 7.39±0.81 to 6.75±0.70 ppm, representing a relative reduction of 9.91%. Spatially, net-zero CO_(2) emissions induce a more significant decrease in XCO_(2) trends over northern and southern China, while their impact on SCA is more evident in northern and northeastern China. Moreover, ideal experiments demonstrate that zero fossil CO_(2) emissions lead to a greater attenuation of the linear trends of XCO_(2) by 40.81%, while the absence of terrestrial CO_(2) sinks largely diminishes the SCA by 16.61%. Additionally,trends and SCA in surface CO_(2) concentrations exhibit almost identical decreasing responses to net-zero CO_(2) emissions but display greater sensitivities compared to XCO_(2) . Overall, our study underscores the pote

关键词： XCO_(2) GEOS-Chem Carbon neutrality Net-zero emissions Surface CO_(2)concentrations

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Enhancing Security and Privacy in Federated Learning Using Low-Dimensional Update Representation and Proximity-Based Defense

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3372-3385页

作者： Li, Wenjie Fan, Kai Zhang, Jingyuan Li, Hui Lim, Wei Yang Bryan Yang, Qiang Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi'an710071 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduce a novel framework named Federated Learning with Low-Dimensional Update Representation and Proximity-Based defense (FLURP), designed to address privacy preservation and resistance to Byzantine attacks in distributed learning environments. FLURP employs $\mathsf {LinfSample}$LinfSample method, enabling clients to compute the $l{ }$l∞ norm across sliding windows of updates, resulting in a Low-Dimensional Update Representation (LUR). Calculating the shared distance matrix among LURs, rather than updates, significantly reduces the overhead of Secure Multi-Party Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and poisoned updates. Additionally, FLURP integrates a privacy-preserving proximity-based defense mechanism utilizing optimized SMPC protocols to minimize communication rounds. Our experiments demonstrate FLURP's effectiveness in countering Byzantine adversaries with low communication and runtime overhead. FLURP offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security. © 1989-2012 IEEE.

关键词： Federated learning

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HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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LayerCFL:an efcient federated learning with layer-wised clustering

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Cybersecurity 2025年第1期8卷 72-85页

作者： Jie Yuan Rui Qian Tingting Yuan Mingliang Sun Jirui Li Xiaoyong Li School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijingChina Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijingChina Institute of Computer Science Faculty of Mathematics and Computer ScienceUniversity of GoettingenGöettingenGermany School of Information Technology Henan University of Chinese MedicineZhengzhouHenanChina

Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model *** address this challenge,prior research has introduced clustered FL(CFL),which involves clustering clients and training them *** its potential benefts,CFL can be computationally and communicationally expensive when the data distribution is unknown *** is because CFL involves the entire neural networks of involved clients in computing the clusters during training,which can become increasingly timeconsuming with large-sized *** tackle this issue,this paper proposes an efcient CFL approach called LayerCFL that employs a Layer-wised clustering *** LayerCFL,clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental *** experimental results demonstrate the efectiveness of LayerCFL in mitigating the impact of Non-IID data,improving the accuracy of clustering,and enhancing computational efciency.

关键词： Federated learning Clustered federated learning Non-IID Layer-wised clustering

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TDMF: Text-Guided Denoising and Interactive Medical Image Fusion

TDMF: Text-Guided Denoising and Interactive Medical Image Fu...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Aimei Xu, Jingyuan Wang, Long Lv, Guohua Zhao, Guixin Cheng, Jinyong Qilu University of Technology Shandong Academy of Sciences Jinan China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance. © 2025 IEEE.

关键词： denoising Medical image fusion text-guided fusion

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UAV-Mounted Intelligent Reflecting Surface in Maritime Wireless Powered Communication Network

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China Communications 2025年第3期22卷 254-269页

作者： Liu Yuan Han Fengxia Zhao Shengjie School of Information Science and Electrical Engineering Shandong Jiaotong UniversityJinan 250357China College of Electronic and Information Engineering Tongji UniversityShanghai 201804China School of Software Engineering Tongji UniversityShanghai 201804China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of EducationShanghai 200003China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804China

Intelligent reflecting surface(IRS)assisted with the wireless powered communication network(WPCN)can enhance the desired signal energy and carry out the power-sustaining problem in ocean monitoring *** this paper,we investigate a reliable communication structure where multiple buoys transmit data to a base station(BS)with the help of the unmanned aerial vehicle(UAV)-mounted IRS and harvest energy from the base station *** organically combine WPCN with maritime data collection scenario,a scheduling protocol that employs the time division multiple access(TDMA)is proposed to serve multiple buoys for uplink data ***,we compare the full-duplex(FD)and half-duplex(HD)mechanisms in the maritime data collection system to illustrate different performances under these two *** maximize the fair energy efficiency under the energy harvesting constraints,a joint optimization problem on user association,BS transmit power,UAV’s trajectory and IRS’s phase shift is *** solve the non-convex problem,the original problem is decoupled into several subproblems,and successive convex optimization and block coordinate descent(BCD)methods are employed obtain the near-optimal solutions *** results demonstrate that the UAV-mounted IRS can significantly improve energy efficiency in our considered system.

关键词： full-duplex IRS joint optimization TDMA UAV WPCN

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A two-way dynamic adaptive pricing resource allocation model based on combinatorial double auctions in computational network

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Computer Communications 2025年 238卷

作者： Xu, Yanjun Tian, Chunqi Wang, Wei Bai, Lizhi Xia, Xuhui School of Computer Science and Technology Tongji University Shanghai China School of Data Science and Engineering East China Normal University Shanghai China Embedded System and Service Computing Key Laboratory of Ministry of Education China

Efficient resource allocation in computing networks is essential for managing fluctuating demands and optimizing system performance. Traditional auction and pricing models often fail to adapt to diverse demands and supply–demand fluctuations, resulting in inefficiencies. This paper proposes a bidirectional price-adaptive bundled resource auction model that considers not only the autonomous adjustment of sellers’ quotations in response to supply–demand fluctuations but also the impact of these fluctuations on buyers’ willingness to pay and bidding behavior. The model integrates Combinatorial Double Auction (CDA) mechanism and Genetic Algorithm (GA), constructing a bundled resource auction mechanism that accommodates diverse resource demands and an adaptive pricing strategy that dynamically responds to real-time supply–demand variations. This approach enhances resource allocation accuracy in dynamic and competitive computing network environments. Furthermore, a reserve price mechanism and a delay compensation strategy are introduced to ensure that the proposed mechanism satisfies individual rationality, budget balance, and incentive compatibility while maintaining computational efficiency. Simulation results demonstrate that, compared to traditional methods, the proposed model not only improves allocation efficiency and enhances resource utilization but also helps reduce operational costs. Specifically, resource allocation dispersion decreases by 4.26%, while service providers’ revenue increases by 7.47%. This study provides a scalable and adaptive solution for dynamic resource allocation in cloud and edge computing platforms. It contributes significantly to the development of resource management and flexible pricing strategies in markets characterized by diverse demands and fluctuating conditions. © 2025

关键词： Budget control

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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Against Mobile Collusive Eavesdroppers: Cooperative Secure Transmission and Computation in UAV-Assisted MEC Networks

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IEEE Transactions on Mobile computing 2025年第6期24卷 5280-5297页

作者： Zhao, Mingxiong Wang, Zirui Guo, Kun Zhang, Rongqian Quek, Tony Q. S. Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China Yunnan University Yunnan Key Laboratory of Software Engineering Kunming650091 China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Communications and Electronics Engineering Shanghai200241 China Yunnan University School of Information Science and Engineering Kunming650500 China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore National Pilot School of Software Yunnan University Kunming China

In Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users' tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques. © 2002-2012 IEEE.

关键词： Jamming

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Mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation 24th

Mitigating Over-Unlearning in Machine Unlearning with Synt...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Baohai Qu, Youyang Gao, Longxiang Li, Conggai Li, Lin Smith, David Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Data61 Commonwealth Science and Industry Research Organization CanberraVIC Australia College of Business and Law Royal Melbourne Institute of Technology MelbourneVIC Australia

ISBN: (纸本)9789819615445

In machine learning, data privacy and security has become an increasingly growing concern. The introduction of machine unlearning offers the ability to address this issue through the removal of personal and sensitive data from trained models to comply with laws, regulations, and user privacy requirements. However, despite the significant benefits of this technique, performing unlearning operations is only sometimes smooth in practice. When we attempt to remove specific data, the model may over-adjust, resulting in its performance on unlearned data. This phenomenon not only reduces the accuracy of the model on known data but also affects its ability to generalize new data, thus weakening the model’s overall performance. Therefore, in this paper, we analyze the phenomenon of over-unlearning;firstly, we explore how to mitigate the effects of over-unlearning by generating synthetic data to fill in the forgotten parts, using data synthesis-based techniques. Secondly, we combine the data synthesis-based compensation strategy with model fine-tuning to further improve the model’s adaptability and generalization capability by further training the model to accommodate synthetic data. Through comprehensive experimentation, we verify the effectiveness of the proposed data synthesis-based compensation strategy. The experimental results show that the data synthesis-based compensation strategy can effectively mitigate the effects of the over-unlearning phenomenon, as well as maintaining the stability and accuracy of the model after removing specific data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data assimilation

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