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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Tkstagnet: A Top-K Spatio-Temporal Attention Gating Network for Air Pollution Prediction

SSRN

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SSRN 2024年

作者： Zhou, Hongliang Mao, Yingchi Li, Xiang Rong, Yi Chen, Ling Yin, Changkui College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China

Accurate air pollution prediction is of great significance for human health, travel and environmental management. As an emerging artificial intelligence technology, Transformer has already shown tremendous potential in air pollution prediction. Unfortunately, existing Transformer-based methods are challenging to accurately predict air pollution due to two limitations: 1) they neglect irrelevant spatial and temporal neighbors (e.g., neighboring sensors and timesteps) when modeling spatio-temporal correlations, and 2) they lack an adaptive mechanism to mine different effects caused by various external factors (e.g., meteorological conditions). Jointly taking these limitations into account, we propose a novel Transformer-based architecture for air pollution prediction, named Top-K Spatio-Temporal Attention Gating Network (TKSTAGNet). In this architecture, to effectively extract spatio-temporal correlations, we first design a TopKpooling Spatial Attention (TopK-SA) and a TopKpooling Temporal Attention (TopK-TA). In TopK-SA, the TopKpooling mechanism is introduced into spatial attention to filter out irrelevant spatial neighbors, accurately capturing spatial dependencies. In TopK-TA, the TopKpooling mechanism is integrated with temporal attention to identify and exclude irrelevant temporal neighbors, precisely extracting temporal dependencies. To adaptively fuse various external factors, a gating fusion module is then proposed. We conduct experiments on two real-world air pollution datasets from urban regions in Beijing and Shanghai, respectively. Experimental results demonstrate that TKSTAGNet outperforms state-of-the-art baselines in prediction precision and fitting the variation trends of air pollution. © 2024, The Authors. All rights reserved.

关键词： Air pollution

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Translational perspective on bone-derived cytokines in inter-organ communications

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The Innovation 2023年第1期4卷 18-19页

作者： Fujun Jin Meijing Liu Dake Zhang Xiaogang Wang Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China School of Biomedical and Pharmaceutical Sciences Guangdong University of TechnologyGuangzhou 510006China Clinical Research Platform for Interdiscipline of Stomatology the First Affiliated Hospital of Jinan UniversityJinan UniversityGuangzhou 510632China

OSTEOKINES IN INTER-ORGAN COMMUNICATIONS The concept of“organic wholeness”permeates all the fields of traditional Chinese medicine,which is also widely accepted by modern *** wealth of knowledge regarding inter-organ communications generated in the latest decades provided solid evidence that human physiology and pathophysiology involve systematic interactions between multiple organs or ***,advancing our understanding of the inter-organ communication process is believed to provide a new perspective for treating various human diseases.

关键词： provide organs communication

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DASCDC: Dual Attention-based Sparse Common Deep Clustering Method 9

DASCDC: Dual Attention-based Sparse Common Deep Clustering M...

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9th IEEE International Conference on Behavioural and Social Computing, BESC 2022

作者： Dong, Yao Shi, Jin Deng, Yahan Dong, Yongfeng Wang, Ziqiu School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Computing Tianjin300401 China Data Driven Industrial Intelligent Hebei Engineering Research Center Tianjin300401 China

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

Graph convolutional network (GCN) has been successfully applied in deep clustering on the high dimensional dataset, which is usually processed by extracting the feature and structure information. However, it lacks an effective fusion mechanism of common information between the feature and structure information and does not perform well on the sparse dataset. Therefore, we propose a dual attention-based sparse common deep clustering method(DASCDC). Firstly, we advance a sparse feature representation method with an adversarial loss function to solve the data sparsity problem and improve the discriminator's performance. Then, an information interaction operator is designed to deeply mine the common information and enhance the information fusion capability. Finally, considering the different contribution degrees of various neighbour nodes and information, we promote a dual self-attention mechanism from the node and model levels. Experiment results indicate that the proposed DASCDC algorithm overperforms other state-of-art deep clustering algorithms on non-graph and graph datasets with high dimension and sparsity. © 2022 IEEE.

关键词： Cluster analysis

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An Adaptive Mesh Segmentation via Iterative K-Means Clustering 17th

An Adaptive Mesh Segmentation via Iterative K-Means Clusteri...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Lian, Jiguang Li, Haisheng Li, Nan Cai, Qiang Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-product Quality Traceability Beijing100048 China

ISBN: (纸本)9789811663192

In order to improve the reusability of part structural mesh modules, a structural mesh segmentation algorithm based on K-means clustering is proposed. In view of the complex topology of the structural grid, which is prone to over-segmentation and fuzzy boundary, the mesh is mapped into a plane graph and processed on its dual graph. The K value is set as a controllable variable, and the K-means iterative clustering is used to achieve meticulous segmentation. Furthermore, the adaptive mesh segmentation is implemented interactively according to the user’s requirements. Experimental results show that the proposed method has realized good segmentation results and friendly real-time interaction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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Automatic mapping of gully from satellite images using asymmetric non-local LinkNet: A case study in Northeast China

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International Soil and Water Conservation Research 2024年第2期12卷 365-378页

作者： Panpan Zhu Hao Xu Ligang Zhou Peixin Yu Liqiang Zhang Suhong Liu College of Computer Sciences and Technology Chongqing University of Posts and TelecommunicationsChongqing Engineering Research Center for Spatial Big Data Intelligent TechnologyChongqing400065China College of Environment and Ecology Chongqing UniversityChongqing400044China Chongqing Municipal Ecological and Environmental Monitoring Center Chongqing401120China Beijing Key Laboratory of Environmental Remote Sensing and Digital Cities Faculty of Geographical ScienceBeijing Normal UniversityBeijing100875China China National Environmental Monitoring Centre Beijing100012China

Gully erosion can lead to the destruction of farmland and the reduction in crop *** mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control *** narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images *** implement gully erosion mapping,we developed a small training samples-oriented lightweight deep leaning model,called asymmetric non-local LinkNet(ASNL-LinkNet).The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted *** results show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning *** quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to *** study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.

关键词： Gully erosion mapping High-resolution satellite images Deep learning Black soil

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From Exploration to Integration: 15 Years of Multisensory Research at Peking University

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Multisensory Research 2025年 1-17页

作者： Chen, Lihan School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health Peking University Beijing100871 China Peking University Beijing100871 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China

We introduce how the rule of thumb of multisensory integration, which was proposed in the seminal book The Merging of the Senses by Stein and Meredith in 1993, inspired the empirical research work conducted at Multisensory lab, Peking University (China) for the last 15 years. We also outline the potential research trends in the multisensory research field. © LIHAN CHEN, 2025.

关键词： artificial intelligence brain oscillations crossmodal dynamic capture individual differences interoception McGurk effect ventriloquism

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Higher-order Community Detection by Motif-based Modularity Optimization

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IEEE Transactions on big data 2025年

作者： Xiao, Jing Zou, Yu-Cheng Xu, Xiao-Ke Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Dalian Minzu University College of Information and Communication Engineering SEAC Key Laboratory of Big Data Applied Technology Dalian116600 China Beijing Normal University Computational Communication Research Center School of Journalism and Communication Beijing100875 China

Recently higher-order community detection based on network motifs has received increasing attention, because motif-based communities reflect not only mesoscale structures but also functional characteristics of real-life networks. In this study, we propose a Modularity Optimization method for Motif-based Community Detection (MOMCD). In order to approximate the global optimum in modularity optimization, an improved nature-inspired metaheuristic algorithm is proposed as optimization strategy. In addition, by comprehensively utilizing motif-based (higher-order) and edge-based (lower-order) structural information, a neighbor community modification operation and a local search operation are also designed to improve the quality of individuals and promote the convergence of MOMCD. Experimental results show that MOMCD is promising and competitive in identifying motif-based communities from synthetic and real-life networks, which outperforms state-of-the-art approaches in terms of quality and accuracy, and deepens our understanding of network structural and functional characteristics. © 2025 IEEE. All rights reserved,

关键词： Heuristic algorithms

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OmniSQL: Synthesizing High-quality Text-to-SQL data at Scale

arXiv

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arXiv 2025年

作者： Li, Haoyang Wu, Shang Zhang, Xiaokang Huang, Xinmei Zhang, Jing Jiang, Fuxin Wang, Shuai Zhang, Tieying Chen, Jianjun Shi, Rui Chen, Hong Li, Cuiping Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China ByteDance Inc China

Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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datasets of Visualization for Machine Learning

arXiv

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arXiv 2024年

作者： Liu, Can Jiang, Ruike Tan, Shaocong Yu, Jiacheng Yang, Chaofan Shao, Hanning Yuan, Xiaoru Key Laboratory of Machine Perception Ministry of Education School of Intelligence Science and Technology Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China

datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization datasets and provide a comprehensive overview of existing visualization datasets, including their data types, formats, supported tasks, and openness. We propose a what-why-how model for visualization datasets, considering the content of the dataset (what), the supported tasks (why), and the dataset construction process (how). This model provides a clear understanding of the diversity and complexity of visualization datasets. Additionally, we highlight the challenges faced by existing visualization datasets, including the lack of standardization in data types and formats and the limited availability of large-scale datasets. To address these challenges, we suggest future research directions. © 2024, CC BY.

关键词： Machine learning

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