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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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An Automated Penetration Semantic Knowledge Mining Algorithm Based on Bayesian Inference

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Computers, Materials & Continua 2021年第3期66卷 2573-2585页

作者： Yichao Zang Tairan Hu Tianyang Zhou Wanjiang Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000China National Engineering Technology Research Center of the Digital Switching System Zhengzhou450000China NUS Business School National University of SingaporeSingapore119077Singapore

Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration *** rule mining,a data mining technique,has been studied and explored for a long ***,few studies have focused on knowledge discovery in the penetration testing *** experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent *** address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is ***,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing ***,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution ***,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the *** experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.

关键词： Penetration semantic knowledge automated penetration testing Bayesian inference cyber security

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Advancing flood susceptibility modeling using stacking ensemble machine learning: A multi-model approach

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Journal of Geographical Sciences 2024年第8期34卷 1513-1536页

作者： YANG Huilin YAO Rui DONG Linyao SUN Peng ZHANG Qiang WEI Yongqiang SUN Shao AGHAKOUCHAK Amir School of Geography and Tourism Anhui Normal UniversityWuhu 241002AnhuiChina Changjiang River Scientific Research Institute Wuhan 430015China Advanced Interdisciplinary Institute of Environment and Ecology Beijing Normal UniversityZhuhai 519087GuangdongChina Hunan Institute of Water Resources and Hydropower Research Changsha 410007China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Department of Civil and Environmental Engineering University of California IrvineIrvineCA 92697USA

Flood susceptibility modeling is crucial for rapid flood forecasting, disaster reduction strategies, evacuation planning, and decision-making. Machine learning(ML) models have proven to be effective tools for assessing flood susceptibility. However, most previous studies have focused on individual models or comparative performance, underscoring the unique strengths and weaknesses of each model. In this study, we propose a stacking ensemble learning algorithm that harnesses the strengths of a diverse range of machine learning models. The findings reveal the following:(1) The stacking ensemble learning, using RF-XGBCB-LR model, significantly enhances flood susceptibility simulation.(2) In addition to rainfall,key flood drivers in the study area include NDVI, and impervious surfaces. Over 40% of the study area, primarily in the northeast and southeast, exhibits high flood susceptibility, with higher risks for populations compared to cropland.(3) In the northeast of the study area,heavy precipitation, low terrain, and NDVI values are key indicators contributing to high flood susceptibility, while long-duration precipitation, mountainous topography, and upper reach vegetation are the main drivers in the southeast. This study underscores the effectiveness of ML, particularly ensemble learning, in flood modeling. It identifies vulnerable areas and contributes to improved flood risk management.

关键词： flood susceptibility assessment machine learning stacking ensemble learning flood drivers XiangjiangRiverBasin

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Bimetallic chalcogenides for electrocatalytic CO_(2)reduction

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Rare Metals 2021年第12期40卷 3442-3453页

作者： Qian Li Yu-Chao Wang Jian Zeng Xin Zhao Chen Chen Qiu-Mei Wu Li-Miao Chen Zhi-Yan Chen Yong-Peng Lei School of Material Science and Engineering Central South University of Forestry and TechnologyChangsha 410004China State Key Laboratory of Powder Metallurgy Hunan Key Laboratory of Two-Dimensional MaterialsCentral South UniversityChangsha 410083China Energy Materials Computing Center Jiangxi University of Science and TechnologyNanchang 330013China Hunan Provincial Key Laboratory of Chemical Power Sources College of Chemistry and Chemical EngineeringCentral South UniversityChangsha 410083China

Electrocatalytic CO_(2)reduction reaction(CO_(2)RR)converts CO_(2)into valuable chemical fuels,which can effectively alleviate global warming and energy ***,limited by its slow reaction rate and low product selectivity,it is urgent to design efficient,cheap,safe,and highly selective CO_(2)RR *** to the advantages of adjustable electronic structure,abundant active sites,low cost,environmental friendliness and excellent electrochemical performance,bimetallic chalcogenides have aroused great ***,we briefly summarized different bimetallic oxides and sulfides for electrocatalytic CO_(2)RR in the past five *** addition,different hybridizations formed between metal atoms,including intermetallic compounds,heterostructures and metal doping,were *** positive effects on CO_(2)RR catalytic selectivity and activity were deeply ***,we also put forward some views about the future research directions and perspectives in CO_(2)RR *** review aims to provide a reference for the rational design of bimetallic chalcogenides towards electrocatalytic CO_(2)reduction.

关键词： Electrocatalysis CO_(2)reduction Bimetallic chalcogenides Electronic structure

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Manipulating the film morphology evolution toward green solvent-processed perovskite solar cells

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SusMat 2021年第4期1卷 537-544页

作者： Shiqi Shan Yaokai Li Haotian Wu Tianyi Chen Benfang Niu Yingzhu Zhang Di Wang Chenxia Kan Xuegong Yu Lijian Zuo Hongzheng Chen State Key Laboratory of Silicon Materials MOE Key Laboratory of Macromolecular Synthesis and FunctionalizationDepartment of Polymer Science and EngineeringZhejiang UniversityHangzhouP.R.China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhouP.R.China Zhejiang University-Hangzhou Global Scientific and Technological Innovation Center HangzhouP.R.China

High-performance perovskite solar cells(PVSCs)with low energy consumption and green processing are highly desired,but constrained by the difficulty in morphology control and the poor understanding on morphology evolution *** address this issue,here we studied the effect of antisolvents on the perovskite film *** found that both the antisolvents and the perovskite composition affect the perovskite film morphology greatly via influencing the intermediate phase,and different perovskite compositions require different antisolvents to reach the optimal *** provides the opportunity to achieve high-performance PVSCs with green antisolvent,that is,isopropanol(iPA)by changing the perovskite compositions,and leads to a powerconversionefficiency(PCE)of 21.50% for PVSCs based on MA_(0.6)FA_(0.4)PbI_(3).Further,we fabricated“fully green”PVSCs with all layers prepared by green sol-vents,and the optimal PCE can reach 19%,which represents the highest among PVSCs with full-green *** work provides insight into the perovskite morphology evolution and paves the way toward“green”processing PVSCs.

关键词： green antisolvent morphology evolution perovskite solar cells

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Multiple-relaxation-time finite-difference lattice Boltzmann model for the nonlinear convection-diffusion equation

Multiple-relaxation-time finite-difference lattice Boltzmann...

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第十二届全国流体力学学术会议

作者： Xinmeng Chen Zhenhua Chai Jinlong Shang Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Hubei Key Laboratory of Engineering Modeling and Scientific Computing

In this paper, a multiple-relaxation-time finite-difference lattice Boltzmann method(MRT-FDLBM) is developed for the nonlinear convection-diffusion equation(NCDE). Through designing the equilibrium distribution function and the source term properly, the NCDE can be recovered exactly from MRT-FDLBM. We also conduct the von Neumann stability analysis on the present MRT-FDLBM and its special case, i.e., single-relaxationtime finite-difference lattice Boltzmann method(SRT-FDLBM). Then, a simplified version of MRT-FDLBM(SMRT-FDLBM) is also proposed, which can save about 15% computational cost. In addition, a series of real and complex-value NCDEs, including the isotropic convection-diffusion equation, Burgers-Fisher equation, sine-Gordon equation, heat-conduction equation, and Schr?dinger equation, are used to test the performance of MRT-FDLBM. The results show that both MRT-FDLBM and SMRT-FDLBM have second-order convergence rates in space and time. Finally, the stability and accuracy of five different models are compared, including the MRT-FDLBM, SMRT-FDLBM,SRT-FDLBM, the previous finite-difference lattice Boltzmann method [H. Wang, B. Shi et al., ***. Comput. 309, 334(2017)], and the lattice Boltzmann method(LBM). The stability tests show that the sequence of stability from high to low is as follows: MRT-FDLBM, SMRT-FDLBM,SRT-FDLBM, the previous finite-difference lattice Boltzmann method, and LBM. In most of the precision test results, it is found that the order from high to low of precision is MRT-FDLBM,SMRT-FDLBM, SRT-FDLBM, and the previous finite-difference lattice Boltzmann method.

关键词： lattice Boltzmann method finite-difference multiple-relaxation-time

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Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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Long-Lived Triplet Charge Separated state and Thermally Activated Delayed Fluorescence in a Compact Orthogonal Anthraquinone-Phenothiazine Electron Donor-Acceptor Dyad

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Journal of Physical Chemistry Letters 2022年第11期13卷 2533-2539页

作者： Zhao, Xiaoyu Sukhanov, Andrey A. Jiang, Xiao Zhao, Jianzhang Voronkova, Violeta K. State Key Laboratory of Fine Chemicals School of Chemical Engineering Dalian University of Technology Dalian 116024 China State Key Laboratory of Chemistry and Utilization of Carbon Based Energy Resources College of Chemistry Xinjiang University Urumqi 830017 China Zavoisky Physical-Technical Institute FRC Kazan Scientific Center of Russian Academy of Sciences Kazan 420029 Russian Federation Key Laboratory of Industrial Ecology and Environmental Engineering Ministry of Education School of Environmental Science and Technology Dalian University of Technology Dalian 116024 China

A long-lived triplet charge separated state (3CS state lifetime: 0.56 μs) was observed in a compact electron donor-acceptor dyad with electron donor phenothiazine (PTZ) and acceptor anthraquinone (AQ) directly connec... 详细信息

A long-lived triplet charge separated state (³CS state lifetime: 0.56 μs) was observed in a compact electron donor-acceptor dyad with electron donor phenothiazine (PTZ) and acceptor anthraquinone (AQ) directly connected by a single C-N bond (AQ-PTZ). The ¹CS state energy (2.0 eV in cyclohexane) is lower than those of the ³AQ (2.7 eV) or the ³PTZ state (2.6 eV). By oxidation of the PTZ unit, thus increasing of the ¹CS state energy (2.7 eV in cyclohexane), thermally activated delayed fluorescence (TADF) was observed [τ = 17.7 ns (99.9%)/1.5 μs (0.1%)]. Time-resolved electron paramagnetic resonance (TREPR) spectra confirm the electron spin multiplicity of the ³CS state, and the zero-field-splitting (ZFS) parameters |D| and |E| are 48.2 mT and 11.2 mT, respectively. These results are useful for design of compact electron donor-acceptor dyads to access the long-lived ³CS state and study the TADF mechanism. © 2022 American Chemical Society. All rights reserved.

关键词： Electrical energy Energy Quantum mechanics Absorption Fluorescence

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Code Clone Detection Based on Code Semantic Enhancement Model

Code Clone Detection Based on Code Semantic Enhancement Mode...

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Intelligent Communication and Computer engineering (ICICCE), International Conference on

作者： Zhang Chunyan Shen Ziang Liang Chen State Key of Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Department of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

Code clones are code fragments that are similar in composition or semantics of code statements, which can significantly improve software development efficiency and reduce costs by improving the quality of reusable code. However, existing code clone detection methods ignore the source code structure information and sequence information seriously. In this paper, we launched our research on the harder-to-break Type IV code clone detection, and propose a new method, namely Aug_Clone, to complete code clone detection task. Firstly, we utilize our published code semantic enhancement model to obtain the code semantic vector. Secondly, we perform code clone detection by calculating the similarity between these semantic vectors. At last, we evaluate our method on a large public data set, and experimental results show improvement over the state-of-the-art benchmarks, which demonstrates the effectiveness and efficiency of our method.

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单原子在铝合金中的扩散迁移行为:可解释机器学习加速第一原理计算方法

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Science China Materials 2024年第4期67卷 1140-1149页

作者：黄敬涛薛景腾李明伟程源来忠红胡津周飞曲囡刘勇朱景川 School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China National Key Laboratory for Precision Hot Processing of Metals Harbin Institute of TechnologyHarbin 150001China National Key Laboratory of Science and Technology on Advanced Composites in SpecialEnvironments Harbin Institute of TechnologyHarbin 150001 China Center for Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001China State Key Laboratory for Environment-friendly Energy Materials School of Materials Science and EngineringSouthwest University of Science and TechnologyMianyang 621010China

本文采用机器学习加速第一原理计算的方法,研究了铝基体中单原子的扩散迁移行为.建立铝基体中三十多种单原子扩散迁移行为的小样本数据集,以原子半径、离子半径和第一电离能等固有参数作为输入特征值,合金原子与空位之间的相互作用能以... 详细信息

本文采用机器学习加速第一原理计算的方法,研究了铝基体中单原子的扩散迁移行为.建立铝基体中三十多种单原子扩散迁移行为的小样本数据集,以原子半径、离子半径和第一电离能等固有参数作为输入特征值,合金原子与空位之间的相互作用能以及合金原子在铝基体中的扩散势垒作为输出参数.通过相关性分析初步确定描述符与预测目标之间的关系,并利用递归特征消除法确定不同目标的输入特征和描述符数量.通过交叉验证证明所选模型的先进性,并进行微调以优化其性能.为了验证其效率和准确性,CatBoost模型经过了传统算法的严格测试.利用训练有素的模型预测周期表中其他单原子在铝基体中的扩散迁移行为.机器学习加速第一原理计算的结果可为进一步开发新型铝合金提供理论依据.

关键词：机器学习扩散迁移交叉验证输出参数单原子第一原理计算原子半径小样本数据

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