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Provincial anthropogenic emission inventory of carbonyl sulfide(COS)in China from 2015 to 2021

Journal of Geographical Sciences 2025年第5期35卷 923-940页

作者： JIANG Yunhao CHEN Bin WANG Shaoqiang LI Tingyu CHEN Shiliang WANG Lunche WANG Lizhe School of Future Technology China University of GeosciencesWuhan 430074China Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of GeosciencesWuhan 430074China Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks Ministry of Natural ResourcesWuhan 430074China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchCASBejing 100101China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of GeosciencesWuhan 430074China

Carbonyl sulfide(COS)is an effective tracer for estimating Gross Primary Productivity(GPP)in the carbon *** the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately *** this study,an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission ***’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr^(-1)from 2015-2021,differing from the trends of other *** an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic,a rapid rebound in emissions occurred following the resumption of economic *** 2021,industrial sources,coal combustion,agriculture and vehicle exhaust accounted for 76.8%,12.3%,10.5%and 0.4%of total COS emissions,*** aluminum industry was the primary COS emitter among industrial sources,contributing40.7% of total ***,Shanxi,and Zhejiang were the top three provinces in terms of anthropogenic COS emissions,reaching 39,21 and 17 Gg S yr-1,***-level regions(hereafter province)with high COS emissions are observed mainly in the eastern and coastal regions of China,which,together with the wind direction,helps explain the pattern of high COS concentrations in the Western Pacific Ocean in *** Green Contribution Coefficient of COS(GCCCOS)was used to assess the relationship between GDP and COS emissions,highlighting the disparity between GDP and COS contributions to green *** part of this analysis,relevant recommendations are proposed to address this *** COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model(STEM),reducing uncertainties in the atmospheric COS source?sink budget and promoting understanding of the atmosphere sulfur cycle.

关键词： anthropogenic emissions emission inventory carbonyl sulfide provincial scale

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Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning

arXiv

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arXiv 2024年

作者： Yang, Shuo Shang, Zirui Wang, Yongqi Deng, Derong Chen, Hongwei Cheng, Qiyuan Wu, Xinxiao Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China

This paper proposes a novel framework for multi-label image recognition without any training data, called data-free framework, which uses knowledge of pre-trained Large Language Model (LLM) to learn prompts to adapt pre-trained Vision-Language Model (VLM) like CLIP to multi-label classification. Through asking LLM by well-designed questions, we acquire comprehensive knowledge about characteristics and contexts of objects, which provides valuable text descriptions for learning prompts. Then we propose a hierarchical prompt learning method by taking the multi-label dependency into consideration, wherein a subset of category-specific prompt tokens are shared when the corresponding objects exhibit similar attributes or are more likely to co-occur. Benefiting from the remarkable alignment between visual and linguistic semantics of CLIP, the hierarchical prompts learned from text descriptions are applied to perform classification of images during inference. Our framework presents a new way to explore the synergies between multiple pre-trained models for novel category recognition. Extensive experiments on three public datasets (MS-COCO, VOC2007, and NUS-WIDE) demonstrate that our method achieves better results than the state-of-the-art methods, especially outperforming the zero-shot multi-label recognition methods by 4.7% in mAP on MS-COCO. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Residual Hyperbolic Graph Convolution Networks

arXiv

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arXiv 2024年

作者： Xue, Yangkai Dai, Jindou Lu, Zhipeng Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Hyperbolic graph convolutional networks (HGCNs) have demonstrated representational capabilities of modeling hierarchical-structured graphs. However, as in general GCNs, over-smoothing may occur as the number of model layers increases, limiting the representation capabilities of most current HGCN models. In this paper, we propose residual hyperbolic graph convolutional networks (R-HGCNs) to address the over-smoothing problem. We introduce a hyperbolic residual connection function to overcome the over-smoothing problem, and also theoretically prove the effectiveness of the hyperbolic residual function. Moreover, we use product manifolds and HyperDrop to facilitate the R-HGCNs. The distinctive features of the R-HGCNs are as follows: (1) The hyperbolic residual connection preserves the initial node information in each layer and adds a hyperbolic identity mapping to prevent node features from being indistinguishable. (2) Product manifolds in R-HGCNs have been set up with different origin points in different components to facilitate the extraction of feature information from a wider range of perspectives, which enhances the representing capability of R-HGCNs. (3) HyperDrop adds multiplicative Gaussian noise into hyperbolic representations, such that perturbations can be added to alleviate the over-fitting problem without deconstructing the hyperbolic geometry. Experiment results demonstrate the effectiveness of R-HGCNs under various graph convolution layers and different structures of product manifolds. © 2024, CC BY.

关键词： Convolution

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Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification 39

Bidirectional Logits Tree: Pursuing Granularity Reconcilemen...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

ISBN: (纸本)157735897X

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Video Summarization using Denoising Diffusion Probabilistic Model

arXiv

引用

arXiv 2024年

作者： Shang, Zirui Zhu, Yubo Li, Hongxi Yang, Shuo Wu, Xinxiao Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Video summarization aims to eliminate visual redundancy while retaining key parts of video to construct concise and comprehensive synopses. Most existing methods use discriminative models to predict the importance scores of video frames. However, these methods are susceptible to annotation inconsistency caused by the inherent subjectivity of different annotators when annotating the same video. In this paper, we introduce a generative framework for video summarization that learns how to generate summaries from a probability distribution perspective, effectively reducing the interference of subjective annotation noise. Specifically, we propose a novel diffusion summarization method based on the Denoising Diffusion Probabilistic Model (DDPM), which learns the probability distribution of training data through noise prediction, and generates summaries by iterative denoising. Our method is more resistant to subjective annotation noise, and is less prone to overfitting the training data than discriminative methods, with strong generalization ability. Moreover, to facilitate training DDPM with limited data, we employ an unsupervised video summarization model to implement the earlier denoising process. Extensive experiments on various datasets (TVSum, SumMe, and FPVSum) demonstrate the effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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Distributed Signal Detection with Multi-shot Weighted Combining

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IEEE Transactions on Vehicular technology 2025年

作者： Pan, Xuesong Zheng, Zhong Liu, Chang Fei, Zesong Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Institute of Technology School of Information and Electronics The Natl. Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Guangdong University of Technology School of Information Engineering Guangzhou510006 China

Recently, the distributed multiple-input multiple-output (MIMO) systems have been widely investigated, where several physically separated access points (APs) connect to a central processing unit (CPU) via fronthaul links to realize joint signal processing. Although appreciable spatial degrees of freedom can be exploited, the huge interaction overhead between APs and CPU is inevitable and prevents this architecture from being practically implemented. To achieve high signal detection accuracy while reducing interaction overhead, we propose a distributed signal detection scheme with multi-shot weighted combining, where the detected signals are iteratively refined via AP detections and subsequent CPU combining. Specifically, the regularized detection is carried out at the APs by penalizing any discrepancies between the local least-square detected signals at the AP and the weighted combined signals fed back from the CPU. The regularized detections are then collected and combined at the CPU for a refined joint detection. Using the operator-valued free probability theory, the combining weights at the CPU only depend on the statistical channel state information between APs and UEs, which alleviates the interaction overhead of fronthaul links. Numerical results demonstrate that the proposed distributed detection with multi-shot combining scheme rapidly converges and achieves improved detection accuracy compared to the detection schemes with one-shot combining. © 1967-2012 IEEE.

关键词： Communication channels (information theory)

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Building Semantic Segmentation Through an Ensemble Architecture of UNet and Graph Convolution Networks 22

Building Semantic Segmentation Through an Ensemble Architect...

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Proceedings of the 2022 6th International Conference on Electronic information technology and Computer Engineering

作者： Yuxin Shang Hao Cuib Haitao Guo Shandong Institute of Commerce and Technology China Shandong Institute of Petroleum and Chemical Technology Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781450397148

Extracting building from remote sensing images remains a challenge because some obstacles, such as trees, cars, farmland, and shadows, disturb the identification accuracy. In recnet years, deep convolution neural networks have shown significant improvements in computer vision, however it is still difficult to extract irregular and small buildings. To develop the extraction accuracy of building, we propose a deep learning model, which combined UNet and graph convolution network, to capture the long-range information. Seven deep convolution neural networks were used on the Massachusetts Buildings Dataset for comparison. And the proposed model, which performed best among these compared models, achieved a building Intersection over Union (IOU) of 71.10%, mean IOU of 81.88%, recall of 81.90%, precision 84.36% and F1 score of 83.11%. This result indicated that the proposed model can accurately extract building objects. The visualization results showed that the proposed model performs better in extracting irregular and small buildings and caneffectively extracts buildings from high-resolution remote-sensing images.

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Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

Deep Representation Learning for Electron Ionization Mass Sp...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shibo Liu Xuan Zhang Anlei Jiao Shiwei Sun Longyang Dian Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Microbial Technology Shandong University Qingdao China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI’s 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC).

关键词： Representation learning Deep learning Accuracy Databases Source coding Ionization Predictive models Mass spectroscopy Electrons Software development management

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How to Train Your Neural Network for Molecular Structure Generation from Mass Spectra?

How to Train Your Neural Network for Molecular Structure Gen...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kai Zhao Yanmin Liu Longyang Dian Shiwei Sun Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China State Key Laboratory of Microbial Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Mass spectrometry serves as a pivotal tool for the analysis of small molecules through an examination of their mass-to-charge ratios. Recent advancements in deep learning have markedly enhanced the analysis of mass spectrometric data, facilitating the prediction of novel small molecule structures without the necessity of extensive databases. Nonetheless, the paucity of annotated datasets impedes the efficacious training of molecular generation models predicated on MS 2 spectra. To mitigate this limitation, we introduce ctMSNovelist, an avant-garde method that amalgamates pre-training, fine-tuning, and co-training techniques to construct a more precise model for the generation of molecular structures from tandem mass spectrometry data. This novel approach augments both the training regimen and the predictive accuracy of the MSNovelist model, thereby surmounting the obstacle of limited data. The methodology commences with the pretraining of a Variational Autoencoder (VAE) to generate molecular fingerprints derived from SMILES strings. Subsequently, it undergoes fine-tuning to emulate noisy fingerprints originating from mass spectrometry (MS) data. Concurrently, MSNovelist is co-trained utilizing these simulated fingerprints, inclusive of the highly noisy variants produced in the early stages of VAE training. The incorporation of a substantial volume of noisy data serves to enhance model accuracy and avert overfitting. We evaluated ctMSNovelist using the GNPS dataset and attained a SMILES prediction accuracy of 48.8%, representing a 4.1% enhancement over MSNovelist. It is pertinent to note that the sole distinction between ctMSNovelist and MSNovelist in this experiment was the training process. The code and models are publicly available at https://***/xfcui/ctMSNovelist.

关键词： Training Solid modeling Accuracy Biological system modeling Fingerprint recognition Predictive models Mass spectroscopy Data models Noise measurement Overfitting

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Cooperative structure of Li/Ni mixing and stacking faults for achieving high-capacity Co-free Li-rich oxides

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Journal of Energy Chemistry 2024年第8期95卷 315-324,I0007页

作者： Zhen Wu Yu-Han Zhang Hao Wang Zewen Liu Xudong Zhang Xin Dai Kunyang Zou Xiaoming Lou Xuechen Hu Lijing Ma Yan Liu Yongning Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101ShandongChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Center for High Performance Computing Network Information CenterXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Wuhan Dongfeng Motor Corporation Ltd Wuhan 430056HubeiChina School of Materials Science and Intelligent Engineering Nanjing UniversitySuzhou 215163JiangsuChina Key Laboratory of MEMS of the Ministry of Education Southeast UniversityNanjing 210096JiangsuChina International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high ***,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage ***,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking *** structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p ***,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than *** study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.

关键词： Co-free Li-rich oxides Li/Ni mixing Stacking faults Electronic structure

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