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An end-to-end mass spectrometry data classification model with a unified architecture

Scientific Reports 2025年第1期15卷 1-15页

作者： Wang, Yinchu Zhang, Wei Guo, Lin Zhang, Fengyi Liu, Zilong Xiong, Xingchuang Fang, Xiang Center for Metrology Scientific Data and Energy Metrology National Institute of Metrology Beijing 100029 China Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Regulation Department Beijing 100029 China Department of Information Engineering China Jiliang University Hangzhou 310018 China

Mass spectrometry, known for its high sensitivity, selectivity, rich structural information, and rapid analysis capabilities, is widely used in disease diagnosis and bioanalysis. Despite progress in classification methods/tools for data collection in the past decade, problems such as complex data processing, weak model characterization, and large interbatch differences persist. To address these problems, we present MS-DREDFeaMiC, a deep neural network framework for disease diagnosis and bioanalysis via mass spectrometry data that enables end-to-end training. The trained MS-DREDFeaMiC can integrate mixed features, reduce interbatch differences, and enhance feature distinctions among categories. To demonstrate its wide applicability, ten comparative experiments were conducted with seven public datasets and one self-constructed dataset, and MS-DREDFeaMiC yielded state-of-the-art results. MS-DREDFeaMiC achieved average accuracies that were 6.6% and 6.3% higher than those of Transformer and Mamba, respectively. We anticipate that MS-DREDFeaMiC can be directly applied to routine disease diagnosis and that any mass spectrometry-based classification studies can benefit from such an end-to-end trained model. © The Author(s) 2025.

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Gradient Coupled Flow: Performance Boosting on Network Pruning by Utilizing Implicit Loss Decrease

SSRN

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SSRN 2022年

作者： Kang, Xiatao Li, Ping Wu, Jiaying Yao, Jiayi School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transp Changsha410114 China

Network pruning prior to training makes generalization more challenging than ever, while recent studies mainly focus on the trainability of the pruned networks in isolation. This paper explores a new perspective on loss implicit decrease of the data to be trained caused by one-batch training during each round, whose first-order approximation we term gradient coupled flow. We thus present a criterion sensitive to gradient coupled flow (GCS), which is hypothesized to capture those weights most sensitive to performance boosting at initialization. Evaluations are empirically investigated on multiple datasets such as CIFAR10/100, Tiny-ImageNet and MNIST deployed on VGG and ResNet architectures. It is shown that GCS achieves decent performance in both single pruning and iterative pruning before training. In addition, a variant of GCS called GCS-Group performs better at low compression ratios, further confirming the role of implicit loss. Interestingly, our explorations show there exists a linear correlation between generalization and implicit loss decrease based measurements on previous works as well as GCS, which ideally describes causes of accuracy fluctuation in a fine-grained manner. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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A New Simple Stochastic Gradient Descent Type Algorithm With Lower Computational Complexity for Bilevel Optimization

arXiv

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arXiv 2023年

作者： Huo, Haimei Liu, Risheng Su, Zhixun The School of Mathematical Sciences Dalian University of Technology Dalian116024 China The DUT-RU International School of Information Science and Engineering Dalian University of Technology China The Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116024 China The State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian116024 China The Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Dalian116024 China

Bilevel optimization has been widely used in many machine learning applications such as hyperparameter optimization and meta learning. Recently, many simple stochastic gradient descent(SGD) type algorithms(without using momentum and variance techniques) have been proposed to solve the bilevel optimization problems. However, all the existing simple SGD type algorithms estimate the hypergradient via stochastic estimation of Neumann series. In the paper, we propose to estimate the hypergradient via SGD-based Estimation(i.e., solving the linear system with SGD). By using warm start initialization strategy, a new simple SGD type algorithm SSGD based on SGD-based Estimation is proposed. We provide the convergence rate guarantee for SSGD and show that SSGD outperforms the best known computational complexity achieved by the existing simple SGD type algorithms. Our experiments validate our theoretical results and demonstrate the efficiency of our proposed algorithm SSGD in hyperparameter optimization applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear systems

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中国碳收支:1980~2021年

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Science Bulletin 2024年第1期69卷 114-124页

作者：夏晓圣任培阳王旭辉刘丹陈修治丹利何斌何洪林居为民梁敏琪陆星劼彭静覃章才夏江周郑博魏静乐旭于贵瑞朴世龙袁文平 School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon CycleSun Yat-sen UniversityZhuhai 510245China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100091China State Key Laboratory of Tibetan Plateau Earth System Resources and EnvironmentInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System ScienceBeijing Normal UniversityBeijing 100875China Synthesis Research Center of China’s Ecosystem Research Network Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China International Institute for Earth System Science Nanjing UniversityNanjing 210023China Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal UniversityTianjin 300387China Institute of Environment and Ecology Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044China

As one of the world's largest emitters of greenhouse gases,China has set itself the ambitious goal of achieving carbon peaking and carbon ***,it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide(CO_(2)),and to monitor China's progress toward these *** state-of-the-art datasets and models,this study comprehensively estimated the anthropogenic CO_(2)emissions from energy,industrial processes and product use,and waste along with natural sources and sinks of CO_(2)for all of China during *** recognize the differences among various methods of estimating greenhouse emissions,the estimates are compared with China's National Greenhouse Gas Inventories(NGHGIs)for 1994,2005,2010,2012,and *** CO_(2)emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO_(2)a^(-1)in *** benefiting from ecological projects(e.g.,Three Norths Shelter Forest System Project),the land carbon sink in China has reached 1.65 Gt CO_(2)a^(-1)averaged through 2010-2021,which is almost 15.81 times that of the carbon sink in the *** average,China's terrestrial ecosystems offset 14.69%±2.49%of anthropogenic CO_(2)emissions through *** provincial-level administrative regions of China,Xizang and Qinghai,have achieved carbon neutrality according to our estimates,but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10%of anthropogenic CO_(2)*** study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO_(2)emissions,but found notable differences for land carbon *** estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

关键词： CO_(2)emission Land carbon sink China Carbon neutrality Carbon budget

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Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix

arXiv

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arXiv 2023年

作者： Lv, Shaogao Wen, Gang Liu, Shiyu Wei, Linsen Li, Ming Department of Statistics and Data Science Nanjing Audit University China University of Electronic Science and Technology of China China School of Astronautics Northwestern Polytechnical University China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China Key Laboratory of Scientific and Engineering Computing Ministry of Education Shanghai Jiao Tong University China

To improve the robustness of graph neural networks (GNN), graph structure learning (GSL) has attracted great interest due to the pervasiveness of noise in graph data. Many approaches have been proposed for GSL to jointly learn a clean graph structure and corresponding representations. To extend the previous work, this paper proposes a novel regularized GSL approach, particularly with an alignment of feature information and graph information, which is motivated mainly by our derived lower bound of node-level Rademacher complexity for GNNs. Additionally, our proposed approach incorporates sparse dimensional reduction to leverage low-dimensional node features that are relevant to the graph structure. To evaluate the effectiveness of our approach, we conduct experiments on real-world graphs. The results demonstrate that our proposed GSL method outperforms several competitive baselines, especially in scenarios where the graph structures are heavily affected by noise. Overall, our research highlights the importance of integrating feature and graph information alignment in GSL, as inspired by our derived theoretical result, and showcases the superiority of our approach in handling noisy graph structures through comprehensive experiments on real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

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IEEE Network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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Synergies quantification and evolution on air pollutant and CO2 emissions driven by different socio-economic effects

SSRN

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SSRN 2024年

作者： Fan, Yu Jing, Siyu Zhan, Min Zhang, Jian School of Economics and Management Beijing Information Science and Technology University Beijing100192 China Beijing Key Laboratory of Big Data Decision Making for Green Development Beijing100192 China School of Digital Media Engineering and Humanities Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory School of Business Central South University Changsha410083 China Beijing International Science and Technology Cooperation Base for Intelligent Decision and Big Data Application Beijing100192 China

Realizing a synergistic reduction of air pollutant and CO2 emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results show the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO2 emissions was about 48% lower when compared with reductions of SO2 (95%), NOX (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NOX and CO2 emissions were stronger driven by final effects structure and types, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Multi-Semantic Markov Random Field Model for Semantic Segmentation of High-Resolution Remote Sensing Images

Multi-Semantic Markov Random Field Model for Semantic Segmen...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Haoyu Fu Chen Zheng Weiheng Xu Yili Zhao Leiguang Wang Faculty of Forestry Southwest Forestry University Kunming China School of Mathematics and Statistics Henan University Kaifeng China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Landscape Architecture and Horticulture Southwest Forestry University Kunming China Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data Southwest Forestry University Kunming China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Remote sensing images have a large imaging field, covering a wide range of land surfaces and encompassing complex geographical scenes. More diverse land cover types and detailed structural information within geographical scenes can be observed with improved image spatial resolution. However, due to the high cost of annotating fine semantic scales, only a few semantic segmentation methods can fully utilize multi-level semantic information. This paper introduces a novel multi-semantic Markov Random Field (MRF-MS) model for semantic segmentation of high-resolution remote sensing images. This method extends the classical object-level Markov Random Field model by introducing a sub-label field to capture semantic hierarchical information in high-resolution images. Assuming the initial categories follow a mixture of Gaussian distribution, this method models the land cover types or structural details of geographical objects within the scene by decomposing the mixture of Gaussian distribution into sub-components. Furthermore, it establishes an interactive contextual structure between the sub-label and initial label fields to model the interaction across different semantic scales. Experimental results on multi-scene high-resolution remote sensing images demonstrate that this approach yields more accurate classification results compared to the latest MRF methods.

关键词： Costs Semantic segmentation Semantics Land surface Imaging Gaussian distribution Spatial resolution

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Multi-scale Semantic Prior Features Guided Deep Neural Network for Urban Street-view Image Inpainting

arXiv

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arXiv 2024年

作者： Zeng, Jianshun Li, Wang Lv, Yanjie Gao, Shuai Qin, YuChu Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

Street-view image has been widely applied as a crucial mobile mapping data source. The inpainting of street-view images is a critical step for street-view image processing, not only for the privacy protection, but also for the urban environment mapping applications. This paper presents a novel Deep Neural Network (DNN), multi-scale semantic prior Feature guided image inpainting Network (MFN) for inpainting street-view images, which generate static street-view images without moving objects (e.g., pedestrians, vehicles). To enhance global context understanding, a semantic prior prompter is introduced to learn rich semantic priors from large pre-trained model. We design the prompter by stacking multiple Semantic Pyramid Aggregation (SPA) modules, capturing a broad range of visual feature patterns. A semantic-enhanced image generator with a decoder is proposed that incorporates a novel cascaded Learnable Prior Transferring (LPT) module at each scale level. For each decoder block, an attention transfer mechanism is applied to capture long-term dependencies, and the semantic prior features are fused with the image features to restore plausible structure in an adaptive manner. Additionally, a background-aware data processing scheme is adopted to prevent the generation of hallucinated objects within holes. Experiments on Apolloscapes and Cityscapes datasets demonstrate better performance than state-of-the-art methods, with MAE, and LPIPS showing improvements of about 9.5% and 41.07% respectively. Visual comparison survey among multi-group person is also conducted to provide performance evaluation, and the results suggest that the proposed MFN offers a promising solution for privacy protection and generate more reliable scene for urban applications with street-view images. © 2024, CC BY.

关键词： Deep neural networks

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Learn to Rectify Label Through Kernel Extreme Learning Machine 16th

Learn to Rectify Label Through Kernel Extreme Learning Machi...

引用

16th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2020

作者： Cai, Qiang Li, Fenghai Li, Haisheng Cao, Jian Li, Shanshan School of Computer Beijing Technology and Business University Beijing China National Engineering Laboratory for Agri-product Quality Traceability Beijing Technology and Business University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China

ISBN: (纸本)9783030775681

Recent studies attempt to construct complicated and redundant Convolutional Neural Networks (CNNs) to improve image classification performance. In this paper, instead of painstakingly designing a CNN’s architecture, we consider promoting classification performance by revising CNN’s classification results. We therefore propose a novel image classification approach that Learns to Rectify Label (LRL) through Kernel Extreme Learning Machine (KELM). It includes two phases: (1) Pre classification, we put images into a trained CNN to generate corresponding incomplete labels. (2) Label Rectification, the incomplete labels are rectified by the KELM’s high-dimensional mapping, so final classification results are acquired. Extensive experiments conducted on public datasets demonstrate the effectiveness of our method. At the meantime, our method has well generalizability that can be integrated with many popular networks. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Image classification

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