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Late Paleozoic–Early Mesozoic tectonic evolution of the Zongwulong Shan-Qinghai Nanshan tectonic belt in the northern Qaidam continent， northern Tibet

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Journal of Earth Science 2025年

作者： Yonghui Zhao Chen Wu Jie Li Peter J.Haproff Lin Ding State Key Laboratory of Tibetan Plateau Earth System and Resources Environment (TPESRE)， Institute of Tibetan Plateau Research， Chinese Academy of Sciences University of Chinese Academy of Sciences Chongqing Key Laboratory of Complex Oil and Gas Field Exploration and Development， Chongqing University of Science and Technology Department of Earth and Ocean Sciences， University of North Carolina Wilmington

The Zongwulong Shan-Qinghai Nanshan tectonic belt of the northern Tibet Plateau experienced a protracted tectonic history， including the openings and closures of the Proto- and Paleo-Tethyan Oceans. Although the tectonic belt has been extensively studied， details regarding the tectonic processes involved in its development remain controversial. To better constrain the tectonic processes of this tectonic belt， we conducted detailed field geological mapping， zircon U-Pb geochronology， and whole-rock geochemical and Sr-Nd isotopic analyses. Our results show that intrusive rocks in the tectonic belt crystallized in ca. 292–233 Ma， perhaps in an arc/subduction setting. Geochemical and Sr-Nd isotopic data suggest that early Permian–late Triassic ultramafic-intermediate intrusions were sourced from the enriched mantle， whereas intermediate-acidic rocks were sourced from mixed crust-mantle. We present the tectonic model that involves: (1) Early Devonian–early Permian intracontinental extension occurred in the northern margin of the Qaidam continent (ca. 416 Ma–292 Ma); (2) Early Permian–late Triassic northward subduction of the Paleo-Tethyan Ocean resulted in arc magmatism (ca. 292 Ma–233 Ma); and (3) subsequent late Triassic intracontinental extension (ca. 233 Ma–215 Ma). Our results suggest that the late Paleozoic–early Mesozoic development of the Zongwulong Shan-Qinghai Nanshan was related to the opening， subduction， and slab retreat of the Paleo-Tethyan Ocean， which has key implications for the tectonic evolution of the northern Tibetan Plateau.

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CODESWIFT: Accelerating LLM Inference for Efficient Code Generation

arXiv

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arXiv 2025年

作者： Zhao, Qianhui Zhang, Li Liu, Fang Lian, Xiaoli Meng, Qiaoyuanhe Jiao, Ziqian Zhou, Zetong Zhang, Borui Guo, Runlin Li, Jia School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Beihang University China Key Lab of High Confidence Software Technology Peking University China

Code generation is a latency-sensitive task that demands high timeliness, but the autoregressive decoding mechanism of Large Language Models (LLMs) leads to poor inference efficiency. Existing LLM inference acceleration methods mainly focus on standalone functions using only built-in components. Moreover, they treat code like natural language sequences, ignoring its unique syntax and semantic characteristics. As a result, the effectiveness of these approaches in code generation tasks remains limited and fails to align with real-world programming scenarios. To alleviate this issue, we propose CODESWIFT, a simple yet highly efficient inference acceleration approach specifically designed for code generation, without comprising the quality of the output. CODESWIFT constructs a multi-source datastore, providing access to both general and project-specific knowledge, facilitating the retrieval of high-quality draft sequences. Moreover, CODESWIFT reduces retrieval cost by controlling retrieval timing, and enhances efficiency through parallel retrieval and a context- and LLM preference-aware cache. Experimental results show that CODESWIFT can reach up to 2.53× and 2.54× speedup compared to autoregressive decoding in repository-level and standalone code generation tasks, respectively, outperforming state-of-the-art inference acceleration approaches by up to 88%. Our code and data are available at https://***/r/CodeSwift. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Estimation of Moderate-resolution Snow Depth in Xinjiang with Enhanced-resolution Passive Microwave and Reanalysis Data by Machine Learning Methods

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11250-11262页

作者： Yan, Yongchang Qin, Yan Liu, Yongqiang Qiu, Yubao Liu, Yang Chinese Academy of Sciences State Key Laboratory of Desert and Oasis Ecology Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands Research Center for Ecology and Environment of Center Asia Xinjiang Institute of Ecology and Geography Urumqi830011 China Xinjiang University College of Geography and Remote Sensing Science Urumqi830017 China Tianshan Snow cover and Avalanche Observation and Research Station of Xinjiang Xinyuan830046 China Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China

The integration of multi-source data into the passive microwave retrieval of snow depth (SD) is vital for accurately capturing large-scale distribution of SD. However, since the exiting SD retrieval algorithms overlook the impact of snow characteristics on brightness temperature, leading to inadequate representation of SD in complex regions. Therefore, this study constructs and optimizes SD retrieval models using four machine learning algorithms, including extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), categorical boosting (CatBoost), and random forest (RF) combing enhanced-resolution passive microwave data. A variety of variables are integrated into the models, encompassing geolocation, topographic indices, land cover, and snow characteristics (fractional snow cover, snow density, and snow grain size) sourced from ERA5-Land reanalysis data. This approach focuses on accurately estimating the spatiotemporal distribution of SD over Xinjiang, characterized by dry-cold snow. The results indicate that: (1) Upon incorporating auxiliary variables, the SD from the CatBoost model demonstrated superior performances over the other algorithms (R2 : CatBoost > LightGBM > XGBoost > RF). (2) The SD product from the CatBoost model exhibits interannual fluctuations. It slightly overestimates shallow snow (SD 20 cm). (3) The SD product reveals the spatial differentiation. Areas in northern Xinjiang with high-value for SD (SD > 20 cm) are mainly distributed in the Tianshan Mountains and Altai Mountains. In contrast, the southern Xinjiang with high-value for SD (SD > 10 cm) are largely clustered in the high-elevation regions surrounding the Kunlun Mountains. The findings highlight that employing this approach can lead to the establishment of valuable long-term SD datasets for capturing SD distribution © 2008-2012 IEEE.

关键词： Snow

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The mutation in the critical sterility-inducing temperature-regulating gene OsTMS5 and OsCSIT1 enhances cadmium tolerance in rice seedlings

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Plant Growth Regulation 2025年 1-7页

作者： Wang, Tao Deng, Fan Gong, Xue Wu, Gengwei Wang, Lanlan Shen, RenFang Huang, Jiu He, Yi Wang, Feng Zhu, XiaoFang Zhang, Chi Wang, Hua National Key Laboratory for Development and Utilization of Forest Food Resources Zhejiang A & F University Hangzhou 311300 China College of Forestry and Biotechnology Zhejiang A & F University Hangzhou 311300 China College of Advanced Agricultural Sciences Zhejiang A & F University Hangzhou 311300 China State Key Laboratory for Quality and Safety of Agro-Products Institute of Virology and Biotechnology Zhejiang Academy of Agricultural Sciences Hangzhou 310021 China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Science Nanjing 210008 China School of Environment Sciences and Spatial Informatics China University of Mining and Technology Xuzhou 221116 China State Key Laboratory for Quality and Safety of Agro-Products Institute of Environment Resource Soil and Fertilizers Zhejiang Academy of Agricultural Sciences Hangzhou 310021 China

Cadmium (Cd) contamination in farmland poses a threat to food security and human health. This study explores the role of the OsCSIT1 gene in Cd tolerance in rice, using an EMS-mutagenized Ostms5/Oscsit1 double mutant (st1). The st1 mutant exhibited enhanced Cd resistance, with reduced root growth inhibition under 5 μM CdCl₂ treatment compared to the Ostms5 parent line (1892S). Gene expression analysis revealed downregulation of Cd uptake genes (OsNRAMP5, OsCd1) and the transporter OsHMA2, alongside upregulation of detoxification genes (OsABCG36, OsZIP1, OsMTP1) in st1 under Cd stress. These changes correlated with 36% lower Cd accumulation in stems. The findings suggest that OsCSIT1 mutation may alter metal homeostasis by modulating transporter activity, limiting Cd uptake and enhancing vacuolar sequestration. The dual functionality of OsCSIT1—regulating both thermosensitive sterility and Cd tolerance—highlights its potential for developing hybrid rice varieties with reduced grain Cd accumulation. The study provides critical insights into developing climate-resilient, low-Cd rice varieties to address food safety challenges in Cd-contaminated environments. © The Author(s) 2025.

关键词： Cadmium OsCSIT1 Rice

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Towards practical data alignment in production federated learning

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Frontiers of Computer Science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection

arXiv

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arXiv 2025年

作者： Hou, Yue Zhu, He Liu, Ruomei Su, Yingke Xia, Jinxiang Wu, Junran Xu, Ke State Key Laboratory of Complex & Critical Software Environment Beihang University China Shen Yuan Honors College Beihang University China

With the emerging of huge amount of unlabeled data, unsupervised out-of-distribution (OOD) detection is vital for ensuring the reliability of graph neural networks (GNNs) by identifying OOD samples from in-distribution (ID) ones during testing, where encountering novel or unknown data is inevitable. Existing methods often suffer from compromised performance due to redundant information in graph structures, which impairs their ability to effectively differentiate between ID and OOD data. To address this challenge, we propose SEGO, an unsupervised framework that integrates structural entropy into OOD detection regarding graph classification. Specifically, within the architecture of contrastive learning, SEGO introduces an anchor view in the form of coding tree by minimizing structural entropy. The obtained coding tree effectively removes redundant information from graphs while preserving essential structural information, enabling the capture of distinct graph patterns between ID and OOD samples. Furthermore, we present a multi-grained contrastive learning scheme at local, global, and tree levels using triplet views, where coding trees with essential information serve as the anchor view. Extensive experiments on real-world datasets validate the effectiveness of SEGO, demonstrating superior performance over state-of-the-art baselines in OOD detection. Specifically, our method achieves the best performance on 9 out of 10 dataset pairs, with an average improvement of 3.7% on OOD detection datasets, significantly surpassing the best competitor by 10.8% on the FreeSolv/ToxCast dataset pair. © 2025, CC BY.

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Large-scale Human Mobility Data Regeneration for Open Urban Research 25

Large-scale Human Mobility Data Regeneration for Open Urban ...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Ruixing Zhang Yunqi Liu Liangzhe Han Leilei Sun Chuanren Liu Jibin Wang Weifeng Lv The State Key Laboratory of Complex and Critical Software Environment Beihang University Haidian Beijing China The State Key Laboratory of Complex and Critical Software Environment Beihang University Haidian Beijing China & The Key Laboratory of Data Science and Intelligent Computing International Innovation Institute Beihang University Hangzhou Zhejiang China Business Analytics and Statistics Haslam College of Business The University of Tennessee Knoxville Tennessee USA China Mobile Group IT Center Xicheng Beijing China & The State Key Laboratory of Complex and Critical Software Environment Beihang University Haidian Beijing China

ISBN: (纸本)9798400712456

Large-scale human mobility data contains rich spatial and temporal information for urban sensing, crowd flow modeling, and urban planning. However, it is usually difficult to access wide-coverage, long-term, and consistent-time human mobility data. Most of the publicly available datasets are actually only records of discontinuous trajectories of a very small portion of urban citizens in asynchronous time due to the limited usage of apps for location data collection or the limited number of volunteers. To address this problem and empower open urban research, this paper constructs a high-quality human mobility dataset by generating large-scale citizen trajectories based on massive cellular signaling data. Particularly, we first propose a heatmap diffusion module to generate a probability heatmap that produces plausible trajectories at both the individual and city scales. Then, we propose a masked trajectory AutoEncoder, which can generate individual trajectory embeddings from partially given or empty trajectories. Third, a flexible framework is provided to incorporate the heatmap diffusion module with the masked trajectory embeddings, demonstrating significant flexibility in handling both fully masked trajectories for city-wide analysis and partially masked trajectories for specific locations. We have conducted extensive experiments to validate the utility of the regenerated trajectories at both individual and region levels for various applications. Numerous case studies further illustrate that our model learns not only the distribution of the trajectories but also the semantics of different urban *** summary, this paper provides a Heatmap Diffusion framework based on a Masked Trajectory AutoEncoder to regenerate flexible trajectories for open urban research. Correspondingly, we will try to open a large-scale human mobility data service for open urban research. Further information can be found at https://***/Rising0321/FinalOpenUR.

关键词： human mobility

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Exploring a new model of urban sustainable development:The potential of oil tea as an ecological product in Guilin

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Chinese Journal of Population,Resources and environment 2025年第1期23卷 114-120页

作者： Zerui Wang Rencai Dong Zongwei Zhu Anxin Lian Yue Cai State Key Laboratory for Ecological Security of Regions and Cities Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China University of Chinese Academy of Sciences Beijing 100049China Guilin Sustainable Development Promotion Center Guilin 541199China Yunnan Key Laboratory for Plateau Mountain Ecology and Restoration of Degrade Environments&School of Ecology and Environment Sciences Yunnan UniversityKunming 650091China

Based on the concept of“Lucid waters and lush mountains are invaluable assets”,Chinese government is relying on the ecological and environmental characteristics of cities,in combination with modern ecological,economic,and sociological theories,to achieve sustainable urban ***,the value realization of ecological products(EP)faces challenges in four major aspects:quantification,trading,transformation,and *** this stage,seeking breakthroughs to form a stable development path for the realization of the value of EPs is *** paper examines the Guilin Sustainable development Innovation Demonstration Zone and conducts a case study on oil tea,a characteristic *** on the value realization of EPs,this paper analyzes the oil tea industry in terms of three functions:ecological protection,regional economic development,and traditional cultural *** on the theory of the gross ecosystem product,this paper examines the role of the value realization of oil tea in improving the quality of the urban living environment and ecosystem functions in *** results can help promote research on the shaping effects of endemic EPs on sustainable development with respect to the urban *** a human-centered perspective,the principles of landsenses ecology and traditional ecological knowledge can help analyze the social,historical,and cultural value of oil *** with on-site research data,a relationship map centered on oil tea culture is created to explore the importance of developing traditional cultural attributes of local EPs in promoting cultural customs,protecting the cultural diversity of ethnic minorities,and driving the development of the cultural tourism *** constructing a sustainable urban development model centered on developing local characteristic EPs with the core elements of ecology-economy-traditional culture,this paper aims to strengthen the ecological protection function of cities,promote high-qualit

关键词： Guilin Value realization of ecological products Oil tea Gross ecosystem product Landsenses ecology Traditional ecological knowledge

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An Automatic Method for Powerline Extraction From ALS Point Cloud of Powerline Corridors

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10803-10829页

作者： Cao, Di Wang, Cheng Liu, Haibo Zhang, Su Du, Meng Wang, Pu Nie, Sheng Cheng, Sijin Chinese Academy of Sciences Key Laboratory of Digital Earth Science Aerospace Information Research Institute Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China University of Chinese Academy of Sciences College of Resources and Environment Beijing100049 China State Grid Economic and technological research institute Company Ltd Beijing102209 China Tsinghua University Institute of Energy Environment and Economy Beijing100084 China Construction Branch of State Grid Jilin Electric Power Company Ltd. Changchun130012 China

Powerlines are critical components of powerline corridors, essential for ensuring a stable and reliable power supply. With the increasing adoption of airborne laser scanning (ALS) in corridor inspections, the demand for efficient and automated methods to extract powerline points from point clouds has grown significantly. This article presents a method for automated powerline extraction from ALS-acquired point clouds, addressing key challenges such as large-scale data processing, complex terrains, and precise segmentation of powerlines and associated components. The proposed approach begins with grid-based pylon location detection and span-based organization of massive powerline corridor data, enabling efficient data processing. A robust segmentation technique is then introduced, transforming the task into a connected component identification problem within a mesh generated using the 3-D Alpha Wrapping algorithm to effectively separate powerlines from other objects. To address irregular gaps in powerline point clouds, a graph-cut-based energy minimization method is developed to identify and merge powerline fragments. Furthermore, a novel slice-based segmentation technique is introduced to accurately differentiate powerlines from components such as pylons, insulator strings, and jumper wires. The method was evaluated on nine datasets spanning 80.32 km, covering voltage levels of 220 kV, 500 kV, and 800 kV. It achieved an average recall of 0.9981, precision of 0.9977, and F_{1} score of 0.9979, demonstrating its robustness, adaptability, and high performance across diverse scenarios. © 2008-2012 IEEE.

关键词： Point cloud compression Feature extraction Poles and towers Accuracy Inspection Laser radar Three-dimensional displays Power lasers Hazards Electricity

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