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50th International Conference on Very Large data Bases, VLDB 2024

作者： Li, Xunkai Liao, Meihao Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology China Peking University National Engineering Laboratory for Big Data Analytics and Applications China

Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and deployment. Compared with undirected graphs, directed graphs (digraphs) fit the demand for modeling more complex topological systems by capturing more intricate relationships between nodes. While some directed GNNs have been introduced, their inspiration mainly comes from deep learning architectures, which lead to redundant complexity and computation, making them inapplicable to large-scale databases. To address these issues, we propose LightDiC, a scalable variant of the digraph convolution based on the magnetic Laplacian. Since topology-related computations are conducted solely during offline pre-processing, LightDiC achieves exceptional scalability, enabling downstream predictions to be trained separately without incurring recursive computational costs. Theoretical analysis shows that LightDiC achieves message passing based on the complex field, which corresponds to the proximal gradient descent process of the Dirichlet energy optimization function from the perspective of digraph signal denoising, ensuring its expressiveness. Experimental results demonstrate that LightDiC performs comparably well or even outperforms other SOTA methods in various downstream tasks, with fewer learnable parameters and higher efficiency. © 2024, VLDB Endowment. All rights reserved.

关键词： Directed graphs

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A neural kernel framework for relation extraction

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Expert Systems with applications 2025年 286卷

作者： Kai Wang Yanping Chen Weizhe Yang Ruizhang Huang Yongbin Qin Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University PR China State Key Laboratory of Public Big Data Guizhou University PR China

Relation extraction (RE) poses significant challenges due to the complexity of identifying semantic relationships between overlapping entity pairs within sentences. Traditional kernel-based methods effectively leverage instance-level similarities but heavily depend on manually designed kernel functions, requiring substantial domain expertise and limiting their flexibility. On the other hand, neural network approaches excel at automatically learning abstract feature representations but typically neglect critical instance-specific information, potentially missing valuable relational details during inference. These limitations motivate the exploration of a hybrid approach that effectively integrates the strengths of traditional kernels with the representational power of neural networks. In this paper, we propose a neural kernel framework designed to bridge this critical gap. The proposed framework employs neural kernels, whose parameters are initialized based on task-specific objectives and optimized through neural training procedures, enabling adaptive learning of similarity measures without relying on handcrafted kernel functions. By maintaining instance-level details through annotated training examples, neural kernels create discriminative, flexible decision boundaries tailored specifically to the relation extraction task. We further develop three complementary neural kernel components, instance kernel, description kernel, and cluster kernel, to show the advantages of kernel substitution. Extensive experiments on the ACE 2005, SemEval 2010, and CoNLL 2004 datasets demonstrate that our neural kernel framework significantly outperforms existing state-of-the-art methods, achieving F1-scores of 88.11 %, 91.08 %, and 98.63 %, respectively.

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Rethinking Node-wise Propagation for Large-scale Graph Learning 24

Rethinking Node-wise Propagation for Large-scale Graph Learn...

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33rd ACM Web Conference, WWW 2024

作者： Li, Xunkai Ma, Jingyuan Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University National Engineering Laboratory for Big Data Analytics and Applications Beijing China

ISBN: (纸本)9798400701719

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes with the same propagation rules, neglecting their topological uniqueness;(ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required. Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglecting local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. Remarkably, ATP is crafted to be a plug-and-play node-wise propagation optimization strategy, allowing for offline execution independent of the graph learning process in a new perspective. Therefore, this approach can be seamlessly integrated into most scalable GNNs while remaining orthogonal to existing node-wise propagation optimization strategies. Extensive experiments on 12 datasets have demonstrated the effectiveness of ATP. © 2024 ACM.

关键词： Graph neural networks

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Immobilized ZnO based nanostructures and their environmental applications

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Progress in Natural Science:Materials International 2021年第6期31卷 821-834页

作者： Wei Gaoa Yangsi Liu Junzhe Dong Department of Chemical and Materials Engineering The University of Auckland Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing Guyue New Materials Research Institute Beijing University of Technology

In the research of semiconductor metal oxides, zinc oxide(ZnO) nanomaterials have been committed to design and exploration because of their unique physical and chemical properties. ZnO has many structural characteristics and has considerable practical applications in the field of environmental technology. However, ZnO nanomaterials used for environmental functions are often presented in the form of powders. After use, they need to go through recovery processes such as centrifugation and filtration, which is also easy to cause secondary *** above problems can be avoided by fixing ZnO nanostructures on substrates to form easily movable ***, we present a review of our work and some others on the different synthetic strategies, formation mechanism, modification, performance and diversifications of immobilized ZnO based nanostructures, and highlight their photocatalytic related activities for environmental remediation. Various immobilized ZnO based nanostructures including nanorod arrays, 3D nanostructures, and composite heterostructures have been focused. We hope it may serve as a useful reference for researchers interested in immobilized semiconductor nanostructures and their photocatalysis and other environmental applications.

关键词： ZnO Nanostructure Immobilization Photocatalysis Environmental application

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Investigate the relationship between learners' social characteristics and academic achievements 5

Investigate the relationship between learners' social charac...

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5th International Workshop on Statistical Physics and Mathematics for Complex Systems, SPMCS 2017

作者： Liu, Zhi Kang, Lingyun Su, Zhu Liu, Sannyuya Sun, Jianwen National Engineering Research Center for E-Learning Central China Normal University Wuhan China National Engineering Laboratory for Technology of Big Data Applications in Education Central China Normal University Wuhan China

The social interaction behaviors of group learning have obtained a lot of attention from researches, and social network analysis (SNA) plays a critical role in exploring collective interactive patterns. In this paper, we investigated the experimental data from the discussion forum of "Discourse Analysis" course in a university learning platform. By calculating learners social characteristics, we explored the relationship between social characteristics and academic achievements, as well as the differences between various achieving learners. The results show that there exist strong positive correlations between most social characteristics and academic achievements, and the majority of high-achieving learners are located at the core position of the network. In addition, the high-achieving learners exhibited more active participations in discussions and received more posts compared with middle- and low-achieving learners. © Published under licence by IOP Publishing Ltd.

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Generative assimilation and prediction for weather and climate

arXiv

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arXiv 2025年

作者： Yang, Shangshang Nai, Congyi Liu, Xinyan Li, Weidong Chao, Jie Wang, Jingnan Wang, Leyi Li, Xichen Chen, Xi Lu, Bo Xiao, Ziniu Boers, Niklas Yuan, Huiling Pan, Baoxiang Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China School of Atmospheric Sciences Key Laboratory of Mesoscale Severe Weather Ministry of Education Nanjing University Nanjing China Earth System Modelling School of Engineering and Design Technical University of Munich Munich Germany Complexity Science Potsdam Institute for Climate Impact Research Potsdam Germany Chinese Academy of Meteorological Sciences Beijing China College of Meteorology and Oceanography National University of Defense Technology Changsha China School of Mathematical Sciences Peking University Beijing China Chongqing Research Institute of Big Data Peking University Chongqing China China Meteorological Administration Key Laboratory for Climate Prediction Studies National Climate Center Beijing China Xiong’an Institute of Meteorological Artificial Intelligence Xiong’an China

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks [1–4]. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions [5] or climate projections [6]. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales. © 2025, CC BY-NC-SA.

关键词： Prediction models

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Exploring the thermodynamic stability and electronic structure of ZnNb₂O₆(001) surface through first-principles calculations

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Chemical Physics 2025年 597卷

作者： Anqi Yang Qian Chen Lilong Zhang Yao Zhu Quan Xie Institute of New Type Optoelectronic Materials and Technology College of Big Data and Information Engineering Guizhou University Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University 550025 China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural Bioengineering Ministry of Education State-Local Joint Laboratory for Comprehensive Utilization of Biomass Center for R&D of Fine Chemicals of Guizhou University Guiyang 550025 China

The polar surface of ZnNb₂O₆ material holds considerable importance and practical significance. Using first-principles calculations, the structure, charge redistribution, electronic properties, and thermodynamic stability of the ZnNb₂O₆(001) surface were examined. The crystal was cleaved along the (001) plane to form nine surface slab models, of which five surface terminations are feasible under specific chemical conditions. The optimized structural configurations of these terminations illustrate the charge transfer due to polarity compensation and the differences in electronic structure caused by surface reconstruction. Surface metallization, resulting from the atypical saturation of surface states, acts as a compensatory mechanism for pronounced surface polarity. The ZnNb₂O₆(001) surface slab exhibits a shift in its electronic band structure, transitioning from semiconductor characteristics to metallic properties. The nature of the charge carriers (p-type or n-type) depends on the specific surface termination. The unique electronic properties of the ZnNb₂O₆(001) surface arise from significant variations in volume and polarity compensation. This detailed atomic-level understanding is expected to stimulate further theoretical and experimental investigations into this novel photocatalytic material.

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Comprehensive recovery of W,V,and Ti from spent selective reduction catalysts

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Rare Metals 2023年第10期42卷 3518-3531页

作者： Li-Wen Ma Xiao-Li Xi Jia-Peng Chen Fan Guo Zi-Jie Yang Zuo-Ren Nie Key Laboratory of Advanced Functional Materials Ministry of EducationFaculty of Materials and ManufacturingBeijing University of TechnologyBeijing100124China Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of TechnologyBeijing100124China National Engineering Laboratory for Industrial Big-Data Application Technology Beijing University of TechnologyBeijing100124China

In this study,spent WO_(3)/V_(2)O_(5)-TiO_(2) catalysts used for selective catalytic reduction were treated by a hydrometallurgical process to comprehensively recover valuable metallic elements,such as W,V,and *** and Si impurities were preferentially removed by selective micro wave-assisted alkali leaching.W and V were leached by enhanced high-pressure leaching with efficiencies estimated at 95% and 81%.The leaching of W and V followed the nuclear shrinkage model controlled by the combination of product layer diffusion and interfacial chemical reaction.A synergistic extraction was applied to separate W and V using an extractant mixture of di-(2-ethylhexyl)phosphoric acid P204 and the primary amine *** extraction efficiencies of V and W reached 86.5% and 6.3%,respectively,with a separation coefficient(V/W) of *** product was precipitated after extraction to yield ammonium paratung state(APT) and NH_(4)VO_(3).The TiO_(2)catalyst carrier residue meets commercial specifications for *** comprehensive recovery process with the characteristics of high-pressure leaching and synergistic extraction realizes the resourceful utilization of the spent catalysts.

关键词： Spent selective catalytic reduction(SCR)catalysts Microwaveleaching Pressure leaching Synergistic extraction Comprehensive recovery

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PDDD-PreTrain:A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

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Plant Phenomics 2023年第2期5卷 313-332页

作者： Xinyu Dong Qi Wang Qianding Huang Qinglong Ge Kejun Zhao Xingcai Wu Xue Wu Liang Lei Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China The School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou 510006China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationGuizhou UniversityGuiyang 550025China

Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural *** intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective *** a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision *** the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased ***,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant ***,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic *** address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease *** addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other *** extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant *** addition,our pre-trained models will be open-sourced at https://***/and Zenodo platform https://***/10.5281/zenodo.7856293.

关键词： diagnosis Plant thereby

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Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model

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Plant Phenomics 2023年第3期5卷 501-519页

作者： Qianding Huang Xingcai Wu Qi Wang Xinyu Dong Yongbin Qin Xue Wu Yangyang Gao Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang 550025China National Key Laboratory of Green Pesticide Guizhou UniversityGuiyang 550025China

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food *** detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating ***,existing methods are still limited to single crop disease *** importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile ***,reducing the number of model parameters tends to cause a decrease in model *** solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple *** detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher *** develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing ***,the multistage knowledge distillation technique can make the model lighter while maintaining high *** only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart *** code is available at https://***/QDH/MSKD.

关键词： Plant diagnosis image

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