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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Yonghao Li, Mengyu Pang, Wei Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University China Mathematical and Computer Sciences Heriot-Watt University United Kingdom University of Trento Italy

ISBN: (纸本)157735897X

Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source information (i.e., statistical information, linguistic information, and factual information) exploration to alleviate the sparsity issues. Then, the graph learning approach is adopted to learn the representation of short texts, which are presented in graph forms. Moreover, we introduce a dual-level (i.e., instance-level and cluster-level) contrastive learning auxiliary task to effectively capture different-grained contrastive information within massive unlabeled data. Meanwhile, previous models merely perform the main task and auxiliary tasks in parallel, without considering the relationship among tasks. Therefore, we introduce a hierarchical architecture to explicitly model the correlations between tasks. We conduct extensive experiments across various benchmark datasets, demonstrating that MI-DELIGHT significantly surpasses previous competitive models. It even outperforms popular large language models on several datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion 39

Skeleton-based Action Recognition with Non-linear Dependency...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Haipeng Yang, Yuheng Lyu, Yingda College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Public Computer Education and Research Center Jilin University China

ISBN: (纸本)157735897X

Human skeleton-based action recognition has long been an indispensable aspect of artificial intelligence. Current state-of-the-art methods tend to consider only the dependencies between connected skeletal joints, limiting their ability to capture non-linear dependencies between physically distant joints. Moreover, most existing approaches distinguish action classes by estimating the probability density of motion representations, yet the high-dimensional nature of human motions invokes inherent difficulties in accomplishing such measurements. In this paper, we seek to tackle these challenges from two directions: (1) We propose a novel dependency refinement approach that explicitly models dependencies between any pair of joints, effectively transcending the limitations imposed by joint distance. (2) We further propose a framework that utilizes the Hilbert-Schmidt Independence Criterion to differentiate action classes without being affected by data dimensionality, and mathematically derive learning objectives guaranteeing precise recognition. Empirically, our approach sets the state-of-the-art performance on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Enhancing thermoelectric performance of n-type AgBi_(3)S_(5)through synergistically optimizing the effective mass and carrier mobility

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Journal of Materiomics 2023年第5期9卷 874-881页

作者： Xin Qian Xiaoxue Zhang Haoran Guo Bangfu Ding Mingjing Chen Jiang-Long Wang Li-Dong Zhao Shu-Fang Wang Key Laboratory of High-precision Computation and Application of Quantum Field Theory of Hebei Province College of Physics Science and TechnologyHebei UniversityBaoding071002China College of Quality and Technical Supervision Hebei UniversityBaoding071002China Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province College of Electron and Information EngineeringHebei UniversityBaoding071002China School of Materials Science and Engineering Beihang UniversityBeijing100191China

AgBi_(3)S_(5) is a new n-type thermoelectric material that is environmentally friendly and composed of elements of earth-abundant,non-toxic and high performance-cost *** compound features an intrinsically low thermal conductivity derived from its complex monoclinic ***,the terrible electrical transport properties greatly limited the improvement of thermoelectric *** previous studies considered that carrier concentration is the main reason for low electrical conductivity and focused on improving carrier concentration by aliovalent ion *** this work,we found that the critical parameter that restricts the electric transport performance of AgBi_(3)S_(5)was the extremely low carrier mobility instead of the carrier *** to the Pisarenko relationships and density functional theory calculations,Nb doping can sharpen the conduction band of AgBi_(3)S_(5),which contributes to reducing the effective mass and improving the carrier *** a further increase of the Nb doping content,the conduction band convergence can enlarge the effective mass and preserve the carrier *** with the decrease in lattice thermal conductivity due to the intensive phone scattering,a maximum ZT value of~0.50 at 773 K was achieved in Ag_(0.97)Nb_(0.03)Bi_(3)S_(5),which was~109.6%higher than that of pure *** work will stimulate the new exploration of high-performance thermoelectric materials in ternary metal sulfides.

关键词： Thermoelectric AgBi_(3)S_(5) Effective mass Carrier mobility ZT value

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Effects of heterogeneous reaction with NO_(2) on ice nucleation activities of feldspar and Arizona Test Dust

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Journal of Environmental sciences 2023年第5期127卷 210-221页

作者： Lanxiadi Chen Chao Peng Jingchuan Chen Jie Chen Wenjun Gu Xiaohong Jia Zhijun Wu Qiyuan Wang Mingjin Tang State Key Laboratory of Organic Geochemistry Guangdong Key Laboratory of Environmental Protection and Resources UtilizationGuangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and ControlGuangzhou Institute of GeochemistryChinese Academy of SciencesGuangzhou 510640China CAS Center for Excellence in Deep Earth Science Guangzhou 510640China University of Chinese Academy of Sciences Beijing 100049China State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Key Laboratory of Aerosol Chemistry and Physics State Key Laboratory of Loess and Quaternary GeologyInstitute of Earth EnvironmentChinese Academy of SciencesXi’an 710061China

Mineral dust is an important type of ice nucleating particles in the troposphere;however,the effects of heterogeneous reactions on ice nucleation(IN)activities of mineral dust remain to be elucidated.A droplet-freezing apparatus(Guangzhou Institute of Geochemistry Ice Nucleation Apparatus,GIGINA)was developed in thiswork to measure IN activities of atmospheric particles in the immersion freezingmode,and its performancewas validated by a series of experimental *** apparatus was then employed to measure IN activities of feldspar and Arizona Test Dust(ATD)particles before and after heterogeneous reaction with NO_(2)(10±0.5 ppmv)at 40%relative *** surface coverage of nitrate,θ(NO_(3)^(−)),increased to 3.1±0.2 for feldspar after reaction with NO_(2) for 6 hr,and meanwhile the active site density per unit surface area(ns)at-20℃ was reduced from 92±5 to<1.0 cm^(−2) by about two orders of magnitude;however,no changes in nitrate content or IN activities were observed for further increase in reaction time(up to 24 hr).Both nitrate content and IN activities changed continuously with reaction time(up to 24 hr)for ATD particles;after reaction with NO_(2) for 24 hr,θ(NO_(3)^(−))increased to 1.4±0.1 and ns at-20℃ was reduced from 20±4 to 9.7±1.9 cm^(−2) by a factor of∼*** work suggests that heterogeneous reaction with NO_(2),an abundant reactive nitrogen species in the troposphere,may significantly reduce IN activities of mineral dust in the immersion freezing mode.

关键词： Ice nucleation Mineral dust Heterogeneous reaction Nitrogen oxides Aerosol-cloud interaction

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A Morphology Focused Cell Detection Model for Histopathology Images

A Morphology Focused Cell Detection Model for Histopathology...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Zhe Wei, Fangyue Guo, Shuyu Che, Xiaoting Bai, Tian Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

ISBN: (纸本)9798350337488

The accurate automatic recognition of cell locations is of great significance for downstream tasks in pathology. Due to the various size and distribution of different cell types, previous cell detection methods applied fixed circles as labels to localize cell position by default, which makes less precise and more difficult to adapt various cell morphology. In this paper, we integrate adaptive areas of interests based on morphology to address the limitation in detecting cells with irregular shapes in pathological images. Moreover, a EGSI module is proposed to extract global distribution of cells in slices, which enables model to acquire more rich semantic information. We exhibit favorable F1 score of our method on four public datasets. Experimental results demonstrate that the proposed method could detect cells more accurately in those images with different cell morphologies and dense cell distribution. © 2023 IEEE.

关键词： cell detection cellular morphology digital pathology medical image analysis

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Deep Multi-modal Graph Clustering via Graph Transformer Network 39

Deep Multi-modal Graph Clustering via Graph Transformer Netw...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Qianqian Xu, Haiming Zhang, Zihao Feng, Wei Gao, Quanxue School of Telecommunications Engineering Xidian University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China

ISBN: (纸本)157735897X

Current deep multi-modal graph clustering methods primarily rely on Graph Neural Network (GNN) to fully exploit attribute features and graph structures, including message propagation and low-dimensional feature embedding. However, these methods lack further exploration of graph structural information, such as the relationship between nodes and shortest paths. Additionally, they may not sufficiently mine complementary information among multi-modal graph data. To address these issues, we propose a novel Deep Multi-modal Graph Clustering via Graph Transformer Network method, called DMGC-GTN. This method thoroughly dissects and utilizes graph structural information, applying graph smoothing to node features and incorporating various forms of embeddings into the transformer architecture. This achieves a unified embedding of graph structure and multi-modal feature attributes, fully exploiting the complementary information within multi-modal graph data. Extensive experiments demonstrate the effectiveness of our algorithm. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Active Machine Learning for Automatic High-Frequency EMI Source Localization

Active Machine Learning for Automatic High-Frequency EMI Sou...

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2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Guo, Jinghai Zhang, Ling Yan, Xin Ma, Hanzhi Li, Da Li, Er-Ping Zhejiang University College of Information Science and Electronic Engineering Hangzhou China National Key Laboratory of Intense Pulsed Radiation Simulation and Effect Xi'an China Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9798350360394

This paper proposes a sparse emission source microscopy (ESM) technique based on active machine learning to localize high-frequency electromagnetic interference (EMI) sources. The proposed method, which combines the query-by-committee (QBC) algorithm and a newly proposed entropy feedback (EF) technique, can significantly improve the efficiency of high-frequency EMI source location by reducing the number of scanning points. The results of simulation and measurement examples show that the proposed method is much more time-efficient compared with the full sampling method. Also, the method demonstrates a noticeable advantage over the uniform sampling and random sampling approaches, and has higher accuracy of EMI source location with the same number of sparse scanning samples. © 2024 IEEE.

关键词： Active learning

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A LINEAR, UNCONDITIONALLY STABLE, SECOND ORDER DECOUPLED METHOD FOR THE NEMATIC LIQUID CRYSTAL FLOWS WITH SAV APPROACH

arXiv

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arXiv 2025年

作者： Cao, Ruonan Yi, Nianyu School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China

In this paper, we present a second order, linear, fully decoupled, and unconditionally energy stable scheme for solving the Erickson-Leslie model. This approach integrates the pressure correction method with a scalar auxiliary variable technique. We rigorously demonstrate the unconditional energy stability of the proposed scheme. Furthermore, we present several numerical experiments to validate its convergence order, stability, and computational efficiency. © 2025, CC BY.

关键词： computational efficiency

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An image segmentation fusion algorithm based on density peak clustering and Markov random field

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Multimedia Tools and Applications 2024年第37期83卷 85331-85355页

作者： Feng, Yuncong Liu, Wanru Zhang, Xiaoli Zhu, Xiaoyan College of Computer Science and Engineering Changchun University of Technology Jilin Changchun130012 China Artificial Intelligence Research Institute Changchun University of Technology Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Image segmentation is a crucial task in the field of computer vision. Markov random fields (MRF) based image segmentation method can effectively capture intricate relationships among pixels. However, MRF typically requires an initial labeling field, and the number of classifications needs to be manually selected. To tackle these issues, we propose a novel medical image segmentation algorithm based on density peak clustering (DPC) and Markov random fields. Firstly, we improve DPC to make it applicable to grayscale images, named GIDPC. In the GIDPC method, local gray density and gray bias are defined to enable the automatic determination of the number of classifications. Then, GIDPC and MRF are combined to achieve image segmentation. Furthermore, a segmentation fusion method is employed to enhance the accuracy of image segmentation. We conduct comparison experiments on the whole brain atlas image library. Our proposed algorithm achieves high average values in uniformity measure, accuracy, precision and sensitivity, respectively. Experimental results demonstrate that the proposed algorithm outperforms other image segmentation methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Multi-Scale Feature Convolutional Neural Network for Particle Image Velocimetry

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Gu, Shusheng Wang, Jingyu Li, Guanxiong Deng, Xiaogang Sichuan University College of Computer Science The National key laboratory of Fundamental Algorithms and Models for Engineering Simulation 610065 China Sichuan University School of Aeronautics and Astronautics The National key laboratory of Fundamental Algorithms and Models for Engineering Simulation 610065 China Sichuan University National key laboratory of Fundamental Algorithms and Models for Engineering Simulation 610065 China

Particle Image Velocimetry (PIV) is a crucial technique in experimental fluid dynamics for non-invasively measuring the velocity components of flow fields. Deep learning methods applied to PIV for velocity field estimation are primarily derived from computer vision. However, these methods often fail to account for the distinctive characteristics of fluid dynamics, such as the very small scales of objects and displacements relative to pixel resolution, high-density particle distributions, complex and irregular motion patterns. As demonstrated in this work, these approaches encounter three main challenges: (i) failure to accurately estimate the motion for very small particles and displacements;(ii) significant computational discrepancies with high-density particle distributions;and (iii) considerable inaccuracies in the analysis of complex flow fields. To address these issues, we propose a novel convolutional neural network, named Multi-Scale Feature Recurrent All-Pairs Field Transforms (MSF-RAFT). Our approach involves: (1) developing a highresolution multi-scale feature extraction network;(2) integrating an innovative residual block to improve the network;and (3) creating a multi-scale feature correction module that constructs a four-layer correlation pyramid. The proposed network has been validated on various datasets and experimental images. MSF-RAFT exhibits superior performance, particularly with high-density small particle images, achieving a significant reduction in estimation error and accurately capturing small vortex details. Comparative analyses with benchmark models demonstrate that MSF-RAFT achieves up to a 33% reduction in estimation error compared to previous best models on complex datasets and provides a flow field estimation that more accurately represents the fluid dynamics around the nacelle. Additionally, evaluations of model size, runtime, and memory consumption demonstrate that MSF-RAFT is highly efficient, making it well-suited to meet the indu

关键词： Flow fields

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