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Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

Research in Astronomy and Astrophysics 2023年第6期23卷 86-97页

作者： Zhen-Hong Shang Si-Yu Mu Kai-Fan Ji Zhen-Ping Qiang Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China Yunnan Observatories Chinese Academy of SciencesKunming 650216China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming 650224China

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on *** method transforms the transverse velocity field measurements into an optical flow field prediction *** evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope *** experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning *** the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 *** optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar *** code implementing the proposed method is available on https://***/lygmsy123/transverse-velocity-field-measurement.

关键词： methods:data analysis techniques:image processing Sun:fundamental parameters

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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Biomedical knowledge graph construction of Sus scrofa and its application in anti-PRRSV traditional Chinese medicine discovery

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Animal Diseases 2025年第2期 220-234页

作者： Mingyang Cui Zhigang Hao Yanguang Liu Bomin Lv Hongyu Zhang Yuan Quan Li Qin Hubei Key Laboratory of Agricultural Bioinformatics College of InformaticsHuazhong Agricultural University Hubei Engineering Technology Research Center of Agricultural Big Data College of InformaticsHuazhong Agricultural University

As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug *** by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS) is a common infectious disease in the global swine industry that causes economically great *** Chinese medicine(TCM) has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective ***,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of ***,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e., transR) from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@*** on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of ***,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90% when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/*** summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.

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Generated and Pseudo Content guided Prototype Refinement for Few-shot Point Cloud Segmentation 38

Generated and Pseudo Content guided Prototype Refinement for...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Lili Lang, Congyan Chen, Ziyi Wang, Tao Li, Yidong Liu, Jun School of Computer Science & Technology Beijing Jiaotong University China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computing and Communications Lancaster University United Kingdom

Few-shot 3D point cloud semantic segmentation aims to segment query point clouds with only a few annotated support point clouds. Existing prototype-based methods learn prototypes from the 3D support set to guide the segmentation of query point clouds. However, they encounter the challenge of low prototype quality due to constrained semantic information in the 3D support set and class information bias between support and query sets. To address these issues, in this paper, we propose a novel framework called Generated and Pseudo Content guided Prototype Refinement (GPCPR), which explicitly leverages LLM-generated content and reliable query context to enhance prototype quality. GPCPR achieves prototype refinement through two core components: LLM-driven Generated Content-guided Prototype Refinement (GCPR) and Pseudo Query Context-guided Prototype Refinement (PCPR). Specifically, GCPR integrates diverse and differentiated class descriptions generated by large language models to enrich prototypes with comprehensive semantic knowledge. PCPR further aggregates reliable class-specific pseudo-query context to mitigate class information bias and generate more suitable query-specific prototypes. Furthermore, we introduce a dual-distillation regularization term, enabling knowledge transfer between early-stage entities (prototypes or pseudo predictions) and their deeper counterparts to enhance refinement. Extensive experiments demonstrate the superiority of our method, surpassing the state-of-the-art methods by up to 12.10% and 13.75% mIoU on S3DIS and ScanNet, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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Confidence-aware Contrastive Learning for Selective Classification 41

Confidence-aware Contrastive Learning for Selective Classifi...

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41st International Conference on Machine Learning, ICML 2024

作者： Wu, Yu-Chang Lyu, Shen-Huan Shang, Haopu Wang, Xiangyu Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China

Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the model to estimate the confidence of its prediction. This work provides a generalization bound for selective classification, disclosing that optimizing feature layers helps improve the performance of selective classification. Inspired by this theory, we propose to explicitly improve the selective classification model at the feature level for the first time, leading to a novel Confidence-aware Contrastive Learning method for Selective Classification, CCL-SC, which similarizes the features of homogeneous instances and differentiates the features of heterogeneous instances, with the strength controlled by the model's confidence. The experimental results on typical datasets, i.e., CIFAR-10, CIFAR-100, CelebA, and ImageNet, show that CCL-SC achieves significantly lower selective risk than state-of-the-art methods, across almost all coverage degrees. Moreover, it can be combined with existing methods to bring further improvement. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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A multi-target UAV fault diagnosis method based on feature discriminant dimension reduction under small samples 4

A multi-target UAV fault diagnosis method based on feature d...

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4th International Symposium on Computer technology and Information science, ISCTIS 2024

作者： An, Xue Zhang, Yizong Li, Shaobo Xiong, Xuan Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350354560

In recent years, deep learning has provided many efficient and accurate solutions to UAV fault diagnosis, but most of the current deep learning can only deal with a single task, whereas the UAV system is a complex coupled system, which needs to perform several different fault position diagnostics (FPD) and fault degree diagnostics (FDD) at the same time. Therefore, this paper proposed a multi-target UAV fault diagnosis method based on feature discriminant dimensionality reduction (MFD-FDDR) under small samples. Firstly, We connect specific features of different tasks with the same spatial definition through the channel dimension. Then, we perform feature discriminant dimensionality reduction (FDDR) for each task feature, and the output characteristics satisfy the input size of the following layer of LSTM. Finally, we validate the effectiveness of our proposed method with extensive experiments on real UAV flight data. © 2024 IEEE.

关键词： discriminant dimensionality reduction fault diagnosis multi-task learning small samples

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The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest 27

The Role of Depth, Width, and Tree Size in Expressiveness of...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Lyu, Shen-Huan Wu, Jin-Hui Zheng, Qin-Cheng Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9781643685489

Random forests are classical ensemble algorithms that construct multiple randomized decision trees and aggregate their predictions using naive averaging. Zhou and Feng [51] further propose a deep forest algorithm with multi-layer forests, which outperforms random forests in various tasks. The performance of deep forests is related to three hyperparameters in practice: depth, width, and tree size, but little has been known about its theoretical explanation. This work provides the first upper and lower bounds on the approximation complexity of deep forests concerning the three hyperparameters. Our results confirm the distinctive role of depth, which can exponentially enhance the expressiveness of deep forests compared with width and tree size. Experiments validate these theoretical findings. The detailed proof and code are available in the full version [31]. © 2024 The Authors.

关键词： Random forests

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Semi-supervised PolSAR image classification method based on contrastive learning 6

Semi-supervised PolSAR image classification method based on ...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Hua, Wenqiang Sun, Nan Liu, Lin The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China School of Computer Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798400707674

Polarimetric synthetic aperture radar (PolSAR) image classification has important application value and a wide range of application scenarios in many fields. Supervised classification methods, which need to use a large amount of labeled data to improve classification accuracy, have achieved satisfactory classification performance. However, it is difficult and expensive to obtain sufficient labeled data for PolSAR images. The problem to be solved is how to obtain high classification accuracy with a limited number of labeled samples. Semi-supervised PolSAR image classification method based on contrastive learning is proposed. Firstly, a supervised PolSAR image classification method based on meta-learning is introduced. Prototypical network based on meta-learning ideas is used as the basic classification model, and a prototypical representation is created for each category. Classification is performed by computing the similarity between the prototypical representation and samples to be classified. Secondly, a method of training feature extractor based on contrastive learning is designed. By maintaining the consistency of features between the same data and distinguishing the differences between different data after data augmentation, the method enables the feature extractor to obtain accurate feature representation. By co-training with meta-learning, so as to improve the classification performance. Finally, experiments on real PolSAR data prove the effectiveness of the proposed method. © 2023 ACM.

关键词： Image classification

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MassNE: Exploring Higher-Order Interactions with Marginal Effect for Massive Battle Outcome Prediction 23

MassNE: Exploring Higher-Order Interactions with Marginal Ef...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Gu, Yin Zhang, Kai Liu, Qi Lin, Xin Huang, Zhenya Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450394161

In online games, predicting massive battle outcomes is a fundamental task of many applications, such as team optimization and tactical formulation. Existing works do not pay adequate attention to the massive battle. They either seek to evaluate individuals in isolation or mine simple pair-wise interactions between individuals, neither of which effectively captures the intricate interactions between massive units (e.g., individuals). Furthermore, as the team size increases, the phenomenon of diminishing marginal utility of units emerges. Such a diminishing pattern is rarely noticed in previous work, and how to capture it from data remains a challenge. To this end, we propose a novel Massive battle outcome predictor with margiNal Effect modules, namely MassNE, which comprehensively incorporates individual effects, cooperation effects (i.e., intra-team interactions) and suppression effects (i.e., inter-team interactions) for predicting battle outcomes. Specifically, we design marginal effect modules to learn how units' marginal utility changing respect to their number, where the monotonicity assumption is applied to ensure rationality. In addition, we evaluate the current classical models and provide mathematical proofs that MassNE is able to generalize several earlier works in massive settings. Massive battle datasets generated by StarCraft II APIs are adopted to evaluate the performances of MassNE. Extensive experiments empirically demonstrate the effectiveness of MassNE, and MassNE can reveal reasonable cooperation effects, suppression effects, and marginal utilities of combat units from the data. © 2023 ACM.

关键词： Forecasting

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Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction 31

Unified Grid Tagging Scheme for Aspect Sentiment Quad Predic...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Su, Guixin Zhang, Yongcheng Wang, Tongguan Wu, Mingmin Sha, Ying Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798891761964

Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance. © 2025 Association for Computational Linguistics.

关键词： Semantics

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