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Transfer learning from grid-structured data to graph-structured data: Application to diagnosis of depression 30th

Transfer learning from grid-structured data to graph-structu...

30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020

作者： Yang, Jiawei Wang, Shaoping Wang, Xingjian Liu, Rui Wang, Yun Cui, Jian Zhou, Yuan Zhou, Jingjing Feng, Yuan Feng, Lei Wang, Gang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing100088 China CAS Key Laboratory of Behavioral Science Institute of Psychology & Magnetic Resonance Imaging Research Center Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Psychology University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811485930

Graphs provide a way to study the characteristics of structural and functional connections between different brain regions. Graph convolutional neural networks have the ability to extract intrinsic local characteristics of networks. However, lack of data is a common problem in medical field, holding back the application of deep learning. Although transfer learning is an effective method to improve performance, it is difficult to find natural graph-structured datasets to pre-train deep learning models. To address this problem, we proposed a novel transfer learning method. The method uses grid-structured source data to pre-train a model, and fine-tuned it with graph-structured data in the task of interest. At last, the method is applied to the diagnosis of depression with a total of 83 samples. By comparing the performance between a fine-tuned model and a fully trained model, we tested the effectiveness of the proposal method. And there is a significant improvement in the accuracy of the pre-trained model. © ESREL2020-PSAM15 *** by Research Publishing, Singapore.

关键词： Diagnosis

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Joint Resource Management for Energy-efficient UAV-Assisted SWIPT-MEC: A Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Chen, Yue Kang, Hui Li, Jiahui Sun, Geng Wang, Boxiong Wang, Jiacheng Liang, Cong Liang, Shuang Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore China Mobile Communications Group Jilin Co. Ltd China Northeast Normal University School of Information Science and Technology Changchun130117 China

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms. © 2014 IEEE.

关键词： Resource allocation

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Low-overhead pieceable fault-tolerant construction of logical controlled-phase circuit for degenerate quantum code*

Low-overhead pieceable fault-tolerant construction of logica...

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IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Chen Lin Guowu Yang Jinzhao Wu Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu P.R. China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

We propose a non-transversal but pieceable fault-tolerant conversion circuit that is used to convert encoded information between five-qubit code and seven-qubit CSS code. Since a syndrome extraction circuit requiring fewer ancillary qubit resources would facilitate the realization of large-scale quantum computations, we further adapt a flag-assisted fault-tolerant syndrome measurement scheme to reduce the cost of ancillary preparation. Numerical simulations are also performed to further analyze the performance of our conversion method.

关键词： Fault tolerance Fault tolerant systems Quantum computing Logic gates Circuit faults Error analysis Error correction codes

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An Omni-directional Antenna with High Gain for WiFi-6 Applications

An Omni-directional Antenna with High Gain for WiFi-6 Applic...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Baifa Yang Haixuan Huang Botao Feng Li Deng College of Electronics and Information Engineering Shenzhen University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

This paper presents a compact high-gain omnidirectional antenna for WiFi-6 applications. The antenna is basically comprised of three pairs of printed dipole patches in series, a coplanar waveguide (CPW) feeding structure and a choking coil, respectively. The printed dipoles fed in series are used to increase radiation gain while the choking coil is employed to provide stable radiation pattern as well as good impedance matching. The simulated results show that a frequency bandwidth of 9.8% (5-7.45 GHz) (S 11

关键词： Dipole antennas Impedance matching Conferences Omnidirectional antennas Coplanar waveguides Information and communication technology Gain

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Differential Urban-Rural Inequalities and Driving Mechanisms of Pm2.5 Exposure in Urban Agglomerations - Evidence from the Central Plains Urban Agglomeration, China

SSRN

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SSRN 2025年

作者： Sun, Xiaofan Wang, Chengyuan Ji, Yaqin Dang, Qiuling Fu, Zhicong Mao, Xuegang Wang, Enheng Jiang, Yan State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China College of Forestry Northeast Forestry University Harbin150040 China Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education Northeast Forestry University Harbin150040 China Research and Development Center of Big Data for Ecosystem Northeast Forestry University Harbin150040 China College of Environmental Science and Engineering Nankai University Tianjin300350 China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin300350 China College of Materials and Energy Lanzhou University Lanzhou730137 China

Exposure to PM2.5 poses severe risks to public health and sustainable development, with exposure inequalities exacerbated by variations in atmospheric activity and uneven regional development. However, the urban−rural inequalities and natural−human driving mechanisms underlying PM2.5 exposure inequalities within urban agglomerations are poorly understood. Taking the Central Plains Urban Agglomeration (CPUA) in China as an example, this study investigated the spatio-temporal variations of PM2.5 and considered its future trends. The Theil index was employed to quantify PM2.5 exposure inequalities. An interpretable machine learning model (RF-SHAP) was applied to identify the natural and socioeconomic driving factors. We found that 99.68% of the CPUA exhibited a decreasing trend in ground-level PM2.5. The overall Theil index decreased from 0.168 to 0.142, with a rural decline from 0.115 to 0.084, suggesting an overall reduction in air pollution, particularly in rural areas. Conversely, the urban Theil index increased from 0.096 to 0.208, highlighting an increasing disparity in urban PM2.5 exposure. Resource-based cities, such as Changzhi, Jincheng, and Jiaozuo, exhibited the largest PM2.5 exposure inequality. Elevation was identified as the dominant factor influencing overall and rural PM2.5 exposure inequalities, while population density was the primary driver of urban inequalities. This study highlighted the differences in urban−rural PM2.5 inequalities and their drivers at the city agglomeration scale. The aims were to mitigate PM2.5 exposure inequalities through socio-environmental systems, provide evidence for the integrated management of PM2.5 exposure inequalities in city agglomerations, and support regional sustainable development. © 2025, The Authors. All rights reserved.

关键词： Health risks

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Deep Learning Based Valid Bug Reports Determination and Explanation

Deep Learning Based Valid Bug Reports Determination and Expl...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Jianjun He Ling Xu Yuanrui Fan Zhou Xu Meng Yan Yan Lei Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9781728198705

ISBN: (纸本)9781728198712

Bug reports are widely used by developers to fix bugs. Due to the lack of experience, reporters may submit numerous invalid bug reports. Manually determining valid bug reports is a laborious task. Automatically identifying valid bug reports can save time and effort for bug analysis. In this paper, we propose a deep learning-based approach to determine and explain valid bug reports using only textual information i.e., summaries and descriptions of bug reports. Convolutional neural network (CNN) is applied to capture their contextual and semantic features. Moreover, by analyzing the spatial structure of CNN, we backtrack the trained CNN model to get phrases that can explain valid bug reports determination. After inspecting the phrases manually, we summarize some valid bug report patterns. We evaluate our approach on five large-scale open-source projects containing a total of 540491 bug reports. On average, across the five projects, our approach achieves 0.85, 0.80, 0.69 and improves the state-of-the-art approach by 8.97%, 9.59%, 9.52% in terms of AUC, F1-score for valid bug reports, and F1-score for invalid bug reports, respectively. From the summarized patterns, we can find that determining valid bug reports is mainly due to three categories of patterns: Attachment, Environment, and Reproduce.

关键词： Fans Computer bugs Semantics Feature extraction Software reliability Task analysis Open source software

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Improving aerosol retrieval from FY-3D/MERSI by parameterizing a binary quadratic spectral surface reflectance model with urban percentage and vegetation index

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Atmospheric Research 2025年 326卷

作者： Weiqian Ji Leiku Yang Xin Pei Huan Liu Yidan Si Kaimin Sun Yizhe Fan Ping Zhang Xiaoqian Cheng Xiaofeng Lu School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo 454003 China Henan Province Spatial Big Data Acquisition Equipment Development and Application Engineering Technology Research Center Jiaozuo 454003 China National Satellite Meteorological Center (National Centre for Space Weather) China Meteorological Administration Beijing 10081 China Innovation Center for FengYun Meteorological Satellite (FYSIC) China Meteorological Administration Beijing 10081 China Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites China Meteorological Administration Beijing 10081 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430072 China

The Medium Resolution Spectral Imager (MERSI) Dark Target (DT) algorithm, adapted from the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm, demonstrated aerosol retrieval performance similar to that of MODIS Collection 6.1 (C6.1) DT products. However, discrepancies arose in urban areas, where MERSI retrievals tended to exhibit noticeable positive biases, primarily due to the inadequate representation of urban surface reflectance. To address this issue, this study developed an enhanced algorithm by parameterizing a binary quadratic spectral surface reflectance model using urban percentage (UP) and vegetation index, named the MERSI DT_UP algorithm. Global validation against Aerosol Robotic Network (AERONET) measurements showed that MERSI DT_UP retrievals in 2019 exhibited greater accuracy than MERSI DT retrievals. The correlation coefficient between satellite retrievals and AERONET for MERSI DT_UP ( R = 0.882) was higher than that for MERSI DT ( R = 0.875), and the mean bias was reduced to −0.001 from 0.018. Additionally, the percentage of matchups falling within the expected error envelope (within EE%) of ± (0.05 + 0.2τ) increased by 1.5 %. Across different urbanization levels, MERSI DT_UP retrievals showed significant reductions in mean biases (ranging from 0.003 to 0.217) and notable increases in within EE% (ranging from 2.2 % to 49.3 %) compared to original results. Moreover, improvements were also observed on moderately vegetated and moderately bright surfaces, as demonstrated by the error dependence analysis. The proposed surface reflectance model supports the algorithm development for MERSI and holds potential for application to other sensors.

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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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Mining implicit entity preference from user-item interaction data for knowledge Graph Completion via adversarial learning

arXiv

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arXiv 2020年

作者： He, Gaole Li, Junyi Zhao, Wayne Xin Liu, Peiju Wen, Ji-Rong School of Information Renmin University of China China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods School of Electronics Engineering and Computer Science Peking University China

The task of knowledge Graph Completion (KGC) aims to automatically infer the missing fact information in knowledge Graph (KG). In this paper, we take a new perspective that aims to leverage rich user-item interaction data (user interaction data for short) for improving the KGC task. Our work is inspired by the observation that many KG entities correspond to online items in application systems. However, the two kinds of data sources have very different intrinsic characteristics, and it is likely to hurt the original performance using simple fusion strategy. To address this challenge, we propose a novel adversarial learning approach by leveraging user interaction data for the KGC task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. The discriminator takes the learned useful information from user interaction data as input, and gradually enhances the evaluation capacity in order to identify the fake samples generated by the generator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks, which will be jointly optimized with the discriminator. Such an approach is effective to alleviate the issues about data heterogeneity and semantic complexity for the KGC task. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our approach on the KGC task. The source code is publicly available at https://***/RUCAIBox/UPGAN. Copyright © 2020, The Authors. All rights reserved.

关键词： knowledge graph

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An Omnidirectional Antenna with Wide Bandwidth and High Gain for WiFi-5 and WiFi-6 Applications

An Omnidirectional Antenna with Wide Bandwidth and High Gain...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

This paper proposes an omni-directional printed antenna with broadband and high gain for WiFi-5 and WiFi-6 applications. In order to achieve stable omnidirectional gain, both of the ground plane and the radiating elements are arranged on the same side of the printed circuit board (PCB) substrate. Over the whole working frequency band, selecting the left end of the PCB board to supply power helps to achieve good radiation pattern in the H-plane. Simulated results exhibit that a relative bandwidth of 8% (5.15-7.125 GHz) is obtained. The corresponding gain ranges from 5.14 to 7.74 dBi in the operating band, which is about 4 dBi larger than that of the conventional omnidirectional antennas.

关键词： Conferences Printed circuits Omnidirectional antennas Bandwidth Information and communication technology Broadband antennas Substrates

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