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arXiv 2021年

作者： Sun, Qingyun Li, Jianxin Peng, Hao Wu, Jia Ning, Yuanxing Yu, Phillip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Department of Computing Macquarie University Sydney Australia Department of Computer Science University of Illinois at Chicago Chicago United States Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching view of graphs, neglecting finer substructures' semantics, and suffering from interpretation enigmas. This paper presents a novel hierarchical subgraph-level selection and embedding-based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way. SUGAR reconstructs a sketched graph by extracting striking subgraphs as the representative part of the original graph to reveal subgraph-level patterns. To adaptively select striking subgraphs without prior knowledge, we develop a reinforcement pooling mechanism, which improves the generalization ability of the model. To differentiate subgraph representations among graphs, we present a self-supervised mutual information mechanism to encourage subgraph embedding to be mindful of the global graph structural properties by maximizing their mutual information. Extensive experiments on six typical bioinformatics datasets demonstrate a significant and consistent improvement in model quality with competitive performance and interpretability. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning

npj Materials Degradation

引用

npj Materials Degradation 2025年第1期9卷 1-13页

作者： Weiting Chen Lingwei Ma Yiran Li Jinke Wang Zhibin Chen Xin Guo Zongbao Li Xiaogang Li Dawei Zhang Dequan Wu Kun Zhou Thee Chowwanonthapunya Beijing Advanced Innovation Center for Materials Genome Engineering School of Advanced Materials Innovation University of Science and Technology Beijing 100083 Beijing China National Materials Corrosion and Protection Scientific Data Center University of Science and Technology Beijing 100083 Beijing China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang 110004 China Southwest Institute of Technology and Engineering Chongqing 401329 China Faculty of International Maritime Studies Kasetsart University Sriracha Campus 199 Tungsukla Sriracha Chonburi 20230 Thailand

The corrosion failure prediction of coating materials in diverse environments is of great significance for service performance evaluation. This work proposes a two-stage machine learning method that makes use of various data, including environmental factors, physical properties, and coating barrier performance, to accurately evaluate the corrosion degradation of coatings. In the first stage, a 1-year outdoor exposure experiment of polyurethane coatings was conducted in nine representative climatic environments. A semi-supervised collaborative training regression model is established between key environmental data and physical properties of coatings (i.e., glossiness, adhesion, water contact angle, and yellowness). In the second stage, using the predicted physical property data as inputs, a machine learning model is constructed that links physical properties to the barrier performance of coatings and develops binary classification models that can distinguish between intact and damaged coatings. This two-stage modeling strategy provides enhanced prediction accuracy and scientific interpretability by incorporating intermediate physical property parameters.

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Have COVID Lockdowns Really Improved the Globalair Quality? –Hierarchical Observations from the Perspectives of Urban Agglomerations Using Atmospheric Reanalysis data

SSRN

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SSRN 2023年

作者： Das, Rahul Deb Bandopadhyay, Subhajit Ghosh, Subhasis Das, Mridul Chowdhury, Mousumi Sánchez, Alexander Cotrina Mitra, Chandana Kumar, Chandan Data Science & AI Elite Team IBM Germany Research & Development IBM Watson Center Mies-van-der-Rohe-Straße 6 Munich80807 Germany Geography and Environmental Science University of Southampton SouthamptonSO17 1BJ United Kingdom Department of Geosciences College of Sciences and Mathematics Auburn University AuburnAL United States Department of Geography Serampore College West Bengal India Department of Mining Engineering Indian Institute of Engineering Science & Technology Shibpur India Department for Innovation in Biological Agri-Food and Forest Systems Università degli Studi della Tuscia via san Camillo de Lellis snc Viterbo01100 Italy Department of Geology and Geological Engineering Colorado School of Mines GoldenCO80401 United States

Steep rise in COVID-19 cases globally at the end of 2019 eventually imposed world-wide restrictions (also known as lockdowns) that forced people to shut down all non-essential public places and activities, industrial operations, and businesses to break the chain of viral transmission. Despite growing scientific literature claiming that there was significant improvements in air quality when lockdown measures were in place, authors found that most of such observations were limited to the core city limits or within a limited geographical extent only not considering the heavy anthropogenic activities outside the city boundaries where the major polluting hotspots i.e. heavy factories, power plants, and coal mines were operating 24x7 even during the lockdown period to maintain energy security. Therefore, this study quantified and re-analysed the air quality data using top-down approach. This study analysed six major air quality parameters namely SO2, O3, NO2, PM2.5, AOD500, and UAI. The time-averaged approach was adopted to analyse the data followed by ground validation. High variability and anomalies in air quality parameters were observed at different levels of observations (i.e. city level, country level etc.). However, it was found that during the lockdown period, PM2.5 and NO2 significantly dropped at the country level with few exceptions. Changes were also observed in AOD500, O3, and UAI concentrations from city to country scale. Mixed behaviours among the atmospheric pollutants were observed with changes in scale and time. This makes the claim about air quality improvements during COVID-19 lockdowns very relative to the scale of observation. © 2023, The Authors. All rights reserved.

关键词： Air quality

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MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series

MERITS: Medication Recommendation for Chronic Disease with I...

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IEEE International Conference on data Mining (ICDM)

作者： Shuai Zhang Jianxin Li Haoyi Zhou Qishan Zhu Shanghang Zhang Danding Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Shenyuan Honors College Beihang University China School of Software Beihang University China National University of Singapore >Singapore

ISBN: (纸本)9781665423991

Medication recommendation for chronic diseases based on the complex historical electronic medical records (EMR) is an important and challenging research problem in medical informatics because the medical records are often irregularly sampled and contain many missing data. However, most existing approaches fail to explore the irregular time-series dependencies and ignore the consecutive correlation in dynamic prescription history. To fill this gap, we propose the MEdication Recommendation network on Irregular Time-Series (MERITS), which captures the irregular time-series dependencies with the neural ordinary differential equations (Neural ODE). Meanwhile, it leverages a drug-drug interaction knowledge graph and two learned medication relation graphs to explore the co-occurrence and sequential correlations of the medications. We further propose an attention-based encoder-decoder framework to combine the historical information of patients and medications from EMR. Besides, we collect and annotate a diabetes inpatient medication dataset and demonstrate the effectiveness of MERITS by comparing it with several state-of-the-art methods of medication recommendations.

关键词： Correlation data visualization Ordinary differential equations data models Diabetes History Electronic medical records

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Understanding collective behaviors in reinforcement learning evolutionary games via a belief-based formalization

引用

Physical Review E 2020年第4期101卷 042402-042402页

作者： Ji-Qiang Zhang Si-Ping Zhang Li Chen Xu-Dong Liu Beijing Advanced Innovation Center for Big Data and Brain Computing School of Comuter Science and Engineering Beihang University Beijing 100191 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710062 China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing 100191 China

Collective behaviors by self-organization are ubiquitous in nature and human society and extensive efforts have been made to explore the mechanisms behind them. Artificial intelligence (AI) as a rapidly developing field is of great potential for these tasks. By combining reinforcement learning with evolutionary game (RLEG), we numerically discover a rich spectrum of collective behaviors—explosive events, oscillation, and stable states, etc., that are also often observed in the human society. In this work, we aim to provide a theoretical framework to investigate the RLEGs systematically. Specifically, we formalize AI-agents' learning processes in terms of belief switches and behavior modes defined as a series of actions following beliefs. Based on the preliminary results in the time-independent environment, we investigate the stability at the mixed equilibrium points in RLEGs generally, in which agents reside in one of the optimal behavior modes. Moreover, we adopt the maximum entropy principle to infer the composition of agents residing in each mode at a strictly stable point. When the theoretical analysis is applied to the 2×2 game setting, we can explain the uncovered collective behaviors and are able to construct equivalent systems intuitively. Also, the inferred composition of different modes is consistent with simulations. Our work may be helpful to understand the related collective emergence in human society as well as behavioral patterns at the individual level and potentially facilitate human-computer interactions in the future.

关键词： Collective dynamics Complex systems Evolutionary dynamics

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可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

引用

science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

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Fast Subspace Clustering Based on the Kronecker Product

Fast Subspace Clustering Based on the Kronecker Product

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International Conference on Pattern Recognition

作者： Lei Zhou Xiao Bai Liang Zhang Jun Zhou Edwin Hancock School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Information and Communication Technology Griffith University Nathan Australia University of York York U.K.

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N³) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN³/^k). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.

关键词： Adaptation models Computational modeling Scalability Clustering methods Face recognition Memory management data models

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Functionalized graphite into hydrophilic activated carbon: Synthesis, characterization, and adsorption studies

引用

Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Romadiansyah, Taufik Qodar Lakuy, Fadias Rara Ardana Iqbal, Rendy Muhamad Zulfiani, Utari Pratama, Agus Wedi Purnomo, Adi Setyo Subaer, Subaer Gunawan, Triyanda Rahmawati, Zeni Asranudin Widiastuti, Nurul Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Palangka Raya Kampus UPR Tunjung Nyaho Palangkaraya 73111 Indonesia Advanced Membrane Technology Research Center (AMTEC) Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia Johor Skudai 81310 Malaysia Department of Physics Universitas Negeri Makassar Makassar 90223 Indonesia Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia

Functionalization of graphite by alkali activation and oxidation by chemical treatment has not been explored. The hydrophobic nature of carbon poses a particular challenge when used as an agent in water treatment applications in particular. Modification with hydrophilic groups on carbon makes carbon a promising material because of its good properties and used in various sectors. This study focused on the synthesis of hydrophilic activated carbon (HAC) from graphite by activation using sodium hydroxide (NaOH) and hydrophilization with ammonium persulfate (APS). The effects of functionalization on the surface chemical properties, wettability, and morphology were evaluated using various techniques. Fourier transform infrared (FTIR) analysis confirmed the functionalization results with the appearance of hydroxyl groups. X-ray photoelectron spectroscopy (XPS) analysis confirmed the carbon surface properties by total survey scan, showing the presence of N 1s and S 2p peaks on HAC. Contact angle values showed an increase in hydrophilicity from 88 ± 1.5° to 39 ± 1.0° for graphite and HAC, respectively. The absorption time also showed that HAC has active hydrophilic groups that can absorb water droplets in less than 0.60 s. The scanning electron microscopy (SEM) results also showed an increase in the number of pores on HAC. The methylene blue (MB) adsorption study showed that HAC had an adsorption capacity of 49.399 mg/g with a removal percent of 98.798 % at optimum conditions with an initial MB concentration of 50 mg/L, contact time of 240 minutes, and adsorbent dose of 0.1 g/100 mL. The adsorption kinetics of MB, AC, and HAC fitted well with the pseudo-second-order model. The Langmuir model provided the best fit to the experimental data, indicating a homogeneous and monolayer adsorbent surface. Accordingly, the synthesized HAC can effectively and reliably remediate wastewater contamination with organic pollutant. © 2024

关键词： Functionalization Graphite HAC Hydrophilic groups Hydrophilicity

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Higher key rate of measurement-device-independent quantum key distribution through joint data processing

引用

Physical Review A 2021年第1期103卷 012402-012402页

作者： Cong Jiang Zong-Wen Yu Xiao-Long Hu Xiang-Bin Wang Jinan Institute of Quantum Technology SAICT Jinan 250101 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Data Communication Science and Technology Research Institute Beijing 100191 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China Shenzhen Institute for Quantum Science and Engineering and Physics Department Southern University of Science and Technology Shenzhen 518055 China

We propose a method named as double-scanning method to improve the key rate of measurement-device-independent quantum key distribution (MDI-QKD) drastically. In the method, two parameters are scanned simultaneously to tightly estimate the counts of single-photon pairs and the phase-flip error rate jointly. Numerical results show that the method in this work can improve the key rate by 35%–280% in a typical experimental setup. Besides, we study the optimization of MDI-QKD protocol with all parameters including the source parameters and failure probability parameters, over symmetric channel or asymmetric channel. Compared with the optimized results with only the source parameters, the all-parameter-optimization method could improve the key rate by about 10%.

关键词： Quantum communication Quantum cryptography

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A quantum convolutional neural network for image classification

arXiv

引用

arXiv 2021年

作者： Lü, Yanxuan Gao, Qing Lü, Jinhu Ogorzalek, MacIej Zheng, Jin School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Information Technologies Jagiellonian University Kraków30-348 Poland

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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