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arXiv 2019年

作者： Fu, Fangcheng Jiang, Jiawei Shao, Yingxia Cui, Bin Peking University Center for Data Science & National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing University of Posts and Telecommunications Tencent Inc

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we find it surprising that the existing systems manage the training dataset in different ways, but none of them have studied the impact of data management. To that end, this paper aims to study the pros and cons of different data management methods regarding the performance of distributed GBDT. We first introduce a quadrant categorization of data management policies based on data partitioning and data storage. Then we conduct an in-depth systematic analysis and summarize the advantageous scenarios of the quadrants. Based on the analysis, we further propose a novel distributed GBDT system named Vero, which adopts the unexplored composition of vertical partitioning and row-store and suits for many large-scale cases. To validate our analysis empirically, we implement different quadrants in the same code base and compare them under extensive workloads, and finally compare Vero with other state-of-the-art systems over a wide range of datasets. Our theoretical and experimental results provide a guideline on choosing a proper data management policy for a given workload. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision trees

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Novel Metabolomic and Adiposity Markers Enhance Prediction of Obstructive Sleep Apnea: A Prospective analysis from the China Human Phenobank

SSRN

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SSRN 2025年

作者： Wu, Yudan Ba, Yihan Chen, Chen Wang, Qi Chen, Qinsheng Zhang, Jinxian Chen, Longlong Zhao, Yining Shi, Biru Ye, Lisong Chen, Wei Wang, Chengyan Hu, Zixin Zhang, Jun Feng, Kai Wang, Yan Liu, Yuehua Qi, Yuan Tang, Huiru Luo, Jingchun Tian, Mei State Key Laboratory of Genetic Engineering School of Life Sciences Human Phenome Institute Zhangjiang Fudan International Innovation Center Fudan University Shanghai201203 China Department of Endocrinology and Metabolism Zhongshan Hospital Fudan University Shanghai200032 China Department of Orthodontics Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Chinese Academy of Sciences Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of the Chinese Academy of Sciences Chinese Academy of Sciences Shanghai200031 China Fudan University Shanghai201203 China Department of Radiology Huashan Hospital State Key Laboratory of Medical Neurobiology National Center for Neurological Disorders Fudan University Shanghai200040 China Department of Preventive Dentistry Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases Fudan University Shanghai200433 China Shanghai Academy of Artificial Intelligence for Science Shanghai200232 China

Background: Obstructive sleep apnea (OSA) prevalence is increasing among Asian populations, yet effective biomarker-based screening tools remain underdeveloped. Metabolic dysregulation and adiposity patterns show potential as novel biomarkers for OSA risk stratification, but their predictive utility has not been comprehensively evaluated in Chinese populations. We aimed to identify key metabolic and adiposity biomarkers for OSA and subsequently develop an integrated prediction model to enhance risk assessment in a Chinese ***: In this prospective cohort study (2018-2023), we enrolled 972 adults (aged 20-60 years) from the China Human Phenobank. All participants underwent overnight polysomnography, sleep questionnaires, metabolomic profiling (1,840 metabolites), and DXA-derived body fat distribution analysis (58 adiposity parameters). OSA was defined according to international clinical criteria as apnea hypopnoea index (AHI) ≥5/h accompanied by characteristic symptoms (excessive daytime sleepiness, fatigue, or impaired cognition) or associated comorbidities. Participants were randomly allocated to training (n=777) and test (n=195) cohorts. We employed a multi-staged feature selection approach integrating differential analysis, correlation filtering, and machine learning algorithms (mRMR and LASSO) to identify key biomarkers. Selected features were integrated into a predictive model combining clinical, metabolomic, and adiposity data. Metabolic pathway enrichment analysis identified biological mechanisms underlying OSA *** identified two critical fat distribution markers (visceral adipose tissue volume and gynoid fat) and ten metabolic signatures (including specific TAG-FAs, lipoprotein subtypes, and steroid derivatives) significantly associated with OSA. The gender-specific fat distribution patterns and protective effects of gynoid fat distribution provided new insights into OSA pathophysiology. Notably, we identified H2FC, a specific H

关键词： Risk assessment

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Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

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Research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and Computer Science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research Center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

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Recent advances in Raman technology with applications in agriculture, food and biosystems: A review

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Artificial Intelligence in Agriculture 2019年第3期3卷 1-10页

作者： Shizhuang Weng Wenxiu Zhu Xueyan Zhang Hecai Yuan Ling Zheng Jinling Zhao Linsheng Huang Ping Han Agricultural Product Quality and Safety Risk Assessment Laboratory of Ministry of Agriculture(Beijing) Beijing Academy of Agricultural and Forestry SciencesChina National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui University111 Jiulong RoadHefeiChina

Raman technology,which covers Raman spectroscopy(RS)and its various derivativemethods,has been widely applied in detection of various substances in agriculture,food and *** article reviews the recent advances in two mainstream Raman technologies as RS and SERS,including technical evolution,application and challenges,and spectral processing ***,the origin,principle,defect,and development of RSwere ***,the current situation,existing problems,and development trend of RS and SERS were discussed in agriculture,food,and biosystems,such as adulteration recognition,plant diseases identification,farm chemicals detection,food additives determination,and toxins *** last,the spectral analysis methods include noise reduction,feature extraction or variable selection,and modelingwere introduced in detail,which can realize the automatic and intelligent analysis of spectra without relying on professionals.

关键词： Raman technology Adulteration Plant diseases Farm chemicals Toxins Spectral processing methods

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Class-Dynamic and Hierarchy-Constrained Network for Entity Linking 28th

Class-Dynamic and Hierarchy-Constrained Network for Entity...

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28th International Conference on database Systems for Advanced applications, DASFAA 2023

作者： Wang, Kehang Liu, Qi Zhang, Kai Liu, Ye Tao, Hanqing Huang, Zhenya Chen, Enhong Auhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9783031306716

Entity Linking (EL) aims to map mentions in a text to corresponding entities in a knowledge base. Existing EL methods usually rely on sufficient labeled data to achieve the best performance. However, the massive investment in data makes EL systems viable only to a limited audience. There is ample evidence that introducing entity types can provide the model prior knowledge to maintain the model performance in low-data regimes. Unfortunately, current low-data EL methods usually employ entity types by rule constraints, which are in a shallow manner. Furthermore, they usually ignore fine-grained interaction between mention and its context, resulting in insufficient semantic information of mention representation in low-data regimes. To this end, we propose a Class-Dynamic and Hierarchy-Constrained Network (CDHCN) for entity linking. Specifically, we propose a dynamic class scheme to learn a more effective representation for each entity type. Besides, we formulate a hierarchical constraint scheme to reduce the matching difficulty of the given mention and corresponding candidate entities by utilizing entity types. In addition, we propose an auxiliary task called mention position prediction (MPP) to obtain an informative mention representation in low-data regimes. Finally, extensive in-domain and out-of-domain experiments demonstrate the effectiveness of our method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge based systems

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MULTI-SCALE CONTEXT-GUIDED LUMBAR SPINE DISEASE IDENTIFICATION WITH COARSE-TO-FINE LOCALIZATION AND CLASSIFICATION

arXiv

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arXiv 2022年

作者： Chen, Zifan Zhao, Jie Yu, Hao Zhang, Yue Zhang, Li Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China National Biomedical Imaging Center Peking University China

Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis. However, existing deep learning models with millions of parameters often fail to learn with only hundreds or dozens of medical images. These models also ignore the contextual relationship between adjacent objects, such as between vertebras and intervertebral discs. This work introduces a multi-scale context-guided network with coarse-to-fine localization and classification, named CCFNet, for lumbar spine disease identification. Specifically, in learning, we divide the localization objective into two parallel tasks, coarse and fine, which are more straightforward and effectively reduce the number of parameters and computational cost. The experimental results show that the coarse-to-fine design presents the potential to achieve high performance with fewer parameters and data requirements. Moreover, the multi-scale context-guided module can significantly improve the performance by 6.45% and 5.51% with ResNet18 and ResNet50, respectively. Our code is available at Github. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Evaluating deep graph neural networks

arXiv

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arXiv 2021年

作者： Zhang, Wentao Sheng, Zeang Jiang, Yuezihan Xia, Yikuan Gao, Jun Yang, Zhi Cui, Bin School of EECS Key Laboratory of High Confidence Software Technologies Peking University Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications

Graph Neural Networks (GNNs) have already been widely applied in various graph mining tasks. However, they suffer from the shallow architecture issue, which is the key impediment that hinders the model performance improvement. Although several relevant approaches have been proposed, none of the existing studies provides an in-depth understanding of the root causes of performance degradation in deep GNNs. In this paper, we conduct the first systematic experimental evaluation to present the fundamental limitations of shallow architectures. Based on the experimental results, we answer the following two essential questions: (1) what actually leads to the compromised performance of deep GNNs;(2) when we need and how to build deep GNNs. The answers to the above questions provide empirical insights and guidelines for researchers to design deep and well-performed GNNs. To show the effectiveness of our proposed guidelines, we present Deep Graph Multi-Layer Perceptron (DGMLP), a powerful approach (a paradigm in its own right) that helps guide deep GNN designs. Experimental results demonstrate three advantages of DGMLP: 1) high accuracy - it achieves state-of-the-art node classification performance on various datasets;2) high flexibility - it can flexibly choose different propagation and transformation depths according to graph size and sparsity;3) high scalability and efficiency - it supports fast training on large-scale graphs. Our code is available in https://***/zwt233/DGMLP. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Efficient Diversity-Driven Ensemble for Deep Neural Networks

Efficient Diversity-Driven Ensemble for Deep Neural Networks

引用

International Conference on data engineering

作者： Wentao Zhang Jiawei Jiang Yingxia Shao Bin Cui Center for Data Science Peking University & National Engineering Laboratory for Big Data Analysis and Applications ETH Zurich Switzerland School of Computer Science Beijing University of Posts and Telecommunications

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

The ensemble of deep neural networks has been shown, both theoretically and empirically, to improve generalization accuracy on the unseen test set. However, the high training cost hinders its efficiency since we need a sufficient number of base models and each one in the ensemble has to be separately trained. Lots of methods are proposed to tackle this problem, and most of them are based on the feature that a pre-trained network can transfer its knowledge to the next base model and then accelerate the training process. However, these methods suffer a severe problem that all of them transfer knowledge without selection and thus lead to low diversity. As the effect of ensemble learning is more pronounced if ensemble members are accurate and diverse, we propose a method named Efficient Diversity-Driven Ensemble (EDDE) to address both the diversity and the efficiency of an ensemble. To accelerate the training process, we propose a novel knowledge transfer method which can selectively transfer the previous generic knowledge. To enhance diversity, we first propose a new diversity measure, then use it to define a diversity-driven loss function for optimization. At last, we adopt a Boosting-based framework to combine the above operations, such a method can also further improve diversity. We evaluate EDDE on Computer Vision (CV) and Natural Language Processing (NLP) tasks. Compared with other well-known ensemble methods, EDDE can get highest ensemble accuracy with the lowest training cost, which means it is efficient in the ensemble of neural networks.

关键词： Erbium Kuiper belt Conferences data engineering

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Graph Condensation for Inductive Node Representation Learning 40

Graph Condensation for Inductive Node Representation Learnin...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Gao, Xinyi Chen, Tong Zang, Yilong Zhang, Wentao Hung Nguyen, Quoc Viet Zheng, Kai Yin, Hongzhi The University of Queensland Brisbane Australia Kaiserslautern Germany Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Griffith University Gold Coast Australia University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350317152

Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. To address this limitation, graph condensation has emerged as a promising technique, which constructs a small synthetic graph for efficiently training GNNs while retaining performance. However, due to the topology structure among nodes, graph condensation is limited to condensing only the observed training nodes and their corresponding structure, thus lacking the ability to effectively handle the unseen data. Consequently, the original large graph is still required in the inference stage to perform message passing to inductive nodes, resulting in substantial computational demands. To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning. This enables direct information propagation on the synthetic graph, which is much more efficient than on the original large graph. Specifically, MCond employs an alternating optimization scheme with innovative loss terms from transductive and inductive perspectives, facilitating the mutual promotion between graph condensation and node mapping learning. Extensive experiments demonstrate the efficacy of our approach in inductive inference. On the Reddit dataset, MCond achieves up to 121.5× inference speedup and 55.9× reduction in storage requirements compared with counterparts based on the original graph. © 2024 IEEE.

关键词： Condensation

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Test-Driven Feature Extraction of Web Components

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Journal of Computer science & Technology 2022年第2期37卷 389-404页

作者： Yong-Hao Long Yan-Cheng Chen Xiang-Ping Chen Xiao-Hong Shi Fan Zhou School of Computer Science and Engineering National Engineering Research Center of Digital Life Sun Yat-sen UniversityGuangzhou 510006China School of Design The Hong Kong Polytechnic UniversityHong KongChina Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion The School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China School of Information Technology and Engineering Guangzhou College of CommerceGuangzhou 511363China

With the growing requirements of web applications,web components are developed to package the implementation of commonly-used features for *** some cases,the developer may want to reuse some features which cannot be customized by the component’s ***/she has to extract the implementation by *** is labor-intensive and *** the widely-used test cases which can be useful to specify the software features,a test-driven approach is proposed to extract the implementation of the desired features in web *** satisfaction of the user^requirements is transformed into the passing rate of user-specified test *** this way,the quality of the extraction result can be evaluated ***,a record/replay-based GUI test generation method is proposed to ensure that the extraction result has the correct GUI *** extract the feature implementation,a hierarchical genetic algorithm is proposed to find the code snippet that can pass all the tests and has the approximate smallest *** compare our method with two existing feature extraction *** result shows that our method can extract the correct implementation with the minimum size.A human-subject study is conducted to show the effectiveness and weaknesses of our method in helping users extract the features.

关键词： feature extraction genetic algorithm graphical user interface software reuse

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