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Subclass consistency regularization for learning with noisy labels based on contrastive learning

NEUROCOMPUTING 2025年 614卷

作者： Sun, Xinkai Zhang, Sanguo Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China

A prominent effect of label noise on neural networks is the disruption of the consistency of predictions. While prior efforts primarily focused on predictions' consistency at the individual instance level, they often fell short of fully harnessing the consistency across multiple instances. This paper introduces subclass consistency regularization (SCCR) to maximize the potential of this collective consistency of predictions. SCCR mitigates the impact of label noise on neural networks by imposing constraints on the consistency of predictions within each subclass. However, constructing high-quality subclasses poses a formidable challenge, which we formulate as a special clustering problem. To efficiently establish these subclasses, we incorporate a clustering-based contrastive learning framework. Additionally, we introduce the Q-enhancing algorithm to tailor the contrastive learning framework, ensuring alignment with subclass construction. We conducted comprehensive experiments using benchmark datasets and real datasets to evaluate the effectiveness of our proposed method under various scenarios with differing noise rates. The results unequivocally demonstrate the enhancement in classification accuracy, especially in challenging high-noise settings. Moreover, the refined contrastive learning framework significantly elevates the quality of subclasses even in the presence of noise. Furthermore, we delve into the compatibility of contrastive learning and learning with noisy labels, using the projection head as an illustrative example. This investigation sheds light on an aspect that has hitherto been overlooked in prior research efforts.

关键词： Deep learning Image classification Noisy label Consistency regularization Contrastive learning

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MMA: Video Reconstruction for Spike Camera Based on Multiscale Temporal Modeling and Fine-Grained Attention

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1291-1295页

作者： Alim, Dilmurat Yang, Chen Qing, Laiyun Li, Guorong Huang, Qingming Univ Chinese Acad Sci Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China

This paper presents a Multiscale Temporal Correlation Learning with the Mamba-Fused Attention Model (MMA), an efficient and effective method for reconstructing a video clip from a spike stream. Spike cameras offer unique advantages for capturing rapid scene changes with high temporal resolution. A spike stream contains sufficient information for multiple image reconstructions. However, existing methods generate only a single image at a time for a given spike stream, which results in excessive redundant computations between consecutive frames when aiming at restoring a video clip, thereby increasing computational costs significantly. The proposed MMA addresses such challenges by constructing a spike-to-video model, directly producing an image sequence at a time. Specifically, we propose a U-shaped Multiscale Temporal Correlation Learning (MTCL) to fuse the features at different temporal resolutions for clear video reconstruction. At each scale, we introduce a Fine-Grained Attention (FGA) module for fine-spatial context modeling within a patch and a Mamba module for integrating features across patches. Adopting a lightweight U-shaped structure and fine-grained feature extraction at each level, our method reconstructs high-quality image sequences quickly. The experimental results show that the proposed MMA surpasses current state-of-the-art methods in image quality, computation cost, and model size.

关键词： Feature extraction Streaming media Computational modeling Decoding Cameras Training Image quality Computer architecture Image sequences Spike cameras image reconstruction transformer Mamba Mamba

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A Real-Time Lightweight Perceptron for Cloud-Edge Collaborative Predictive Maintenance of Online Service Systems

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 12640-12657页

作者： Zhang, Linzi Shi, Yong Wang, Donghan Commun Univ China Sch Econ & Management Beijing 100024 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China

In the era of expanding cloud-edge online service systems, predictive maintenance (PdM) based on key performance indicators (KPIs), such as CPU utilization, response rate, and network bandwidth, is essential for system operational reliability and security. Traditional data-driven PdM approaches for cloud-edge services often monitor individual indicators in isolation, neglecting their interrelationships. Although convolutional modules have been utilized for collaborative representation learning of localized high-dimensional KPIs, the high-computational complexity of CNNs and the limited kernel size hinder their efficacy in modeling high-dimensional, time-sensitive KPIs. To address these challenges, we propose a lightweight multichannel multilayer perceptron (MCMLP) framework for collaborative PdM. This framework enables efficient pointwise manipulation to capture inner-indicator hidden patterns within KPIs sequences without relying on grid-based convolution. Our MCMLP enhances locality through a two-stage dual-channel feature extraction process that captures spatial and temporal dependencies, integrating inner-indicator and cross-timeline features. The proposed MCMLP can also be easily plugged into existing CNN-based detectors as a substitute, offering a computationally economical alternative. Empirical results in real-world KPI data from various cloud-edge service scenarios demonstrate that MCMLP significantly outperforms traditional CNN-based methods, with a roughly 22% reduction in training time at a 95% confidence level (alpha = 0.05). This study also provides actionable insights for deploying MCMLP in cloud-edge service systems.

关键词： Feature extraction Convolutional neural networks Predictive maintenance Predictive models Long short term memory Computer architecture Collaboration Cloud computing Computational modeling Biological system modeling Bayesian-based posterior test channel fusion perceptron key performance indicators (KPIs) online service systems predictive maintenance (PdM)

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Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China

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ECOLOGICAL INDICATORS 2025年 173卷

作者： Zhang, Peng Xu, Xukan Yang, Wentong Li, Yiming Yao, Shengqi Hohai Univ Business Sch Nanjing 211100 Peoples R China Hohai Univ Inst Stat & Data Sci Nanjing 211100 Peoples R China Key Lab Ind Big Data Min & Knowledge Management Changzhou 213000 Peoples R China

Urban flooding has become a pressing challenge for many countries and regions. Meanwhile, ecosystem-based disaster risk reduction approaches have been recognized as a sustainable and effective strategy for managing flood risks. This study designed a conceptual framework for assessing the supply-demand risk of urban flood resilience (UFR) from the perspective of ecosystem services (ESs). Taking the city of Nanjing, China, as an example, the InVEST model and the multi-criteria comprehensive evaluation method were employed to quantify the supply of UFR provided by natural ecosystems and the demand for UFR from socio-economic systems. Additionally, based on UFR supply-demand evaluation indicators calculated for each subdistrict, the Self- Organizing Map (SOM) was used to cluster the subdistricts. Finally, UFR supply-demand matching was conducted on the subdistrict clusters, and different flood-risk levels were identified based on the supply-demand ratio. The results showed that high flood-risk subdistricts are mainly concentrated in central urban area, low flood-risk subdistricts are primarily in urban periphery, and subdistricts in urban-rural transitional zones exhibit medium flood risk. Statistical analysis revealed that this zonal pattern is closely related to land use types and the distribution of social resources. Therefore, this study provides a scientific basis for developing management strategies of urban flood prevention from the perspective of ESs.

关键词： Urban flood resilience Ecosystem services Self-Organizing Map Supply-demand matching Risk assessment

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Digitizing Highways:Unleashing Potential Through data Element Circulation for Safety,Cost Efficiency,and Environmental Impact Optimization

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Journal of Systems Science and Information 2025年第1期13卷 23-60页

作者： Yanyan JIA Wen LONG Yingjie TIAN School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

As the era of large-scale highway maintenance arrives,the maintenance strategies have transitioned to a holistic approach that prioritizes safety,economic feasibility,and environmental *** research introduces a multi-objective optimization model for highway maintenance that incorporates the interplay of decision-maker preferences across three key objectives:Highway safety performance,maintenance engineering cost,and carbon *** study employs a large-sample data analysis on a subset of the Lianhuo Highway network,which includes 2,842 pavement *** approach mitigates the impact of outliers,ensuring a substantial data buffer that fortifies the model’s capacity for generalization and bolsters its *** findings reveal a Pareto-optimal relationship among the three scrutinized variables.A particularly noteworthy observation is the M-shaped trajectory of carbon emissions,which initially rise,then decline,and ultimately rebound,contingent upon the selected maintenance ***,an examination of the relationship between maintenance costs and safety performance discloses a trend of diminishing marginal returns,illustrating that the incremental gains in safety performance attenuate as maintenance investment escalates.

关键词： data element circulation highway maintenance multi-objective optimization

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Boost Tracking by Natural Language With Prompt-Guided Grounding

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 1088-1100页

作者： Li, Hengyou Liu, Xinyan Li, Guorong Wang, Shuhui Qing, Laiyun Huang, Qingming Univ Chinese Acad Sci Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100045 Peoples R China

TNL (Tracking by Natural Language) aims to locate the target described by a natural language sentence in a video. Most existing TNL methods are typically composed of three modules: object grounding, object tracking, and switching module, and their performance is limited by the poor performance of the grounding and switching modules due to the complex backgrounds and inaccurate information stored in the memory. This paper presents a global-local framework to address these issues, which includes a prompt-guided grounding module, a trained local tracking module, and a memory-based switcher module. The prompt-guided grounding module uses noun prompts to guide the CLIP model in focusing more on target regions and aligning visual features semantically with linguistic features, avoiding being misled by distractors and background. The memory-based switch module stores historical information with higher-quality memory, allowing the model to make more accurate decisions based on reliable data, thus improving the overall performance. Experiments on TNL2K, LaSOT, and OTB-Lang demonstrate the effectiveness and generalizability of the proposed framework.

关键词： Target tracking Grounding Switches Visualization Feature extraction Computational modeling Adaptation models Location awareness Linguistics Memory management Vision-language tracking prompt learning inverse tracking

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SpikeODE: Image Reconstruction for Spike Camera With Neural Ordinary Differential Equation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第11期34卷 11142-11155页

作者： Yang, Chen Li, Guorong Wang, Shuhui Su, Li Qing, Laiyun Huang, Qingming Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Sch Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China

The recently invented retina-inspired spike camera has shown great potential for capturing dynamic scenes. However, reconstructing high-quality images from the binary spike data remains a challenge due to the existence of noises in the camera. This paper proposes SpikeODE, a novel approach to reconstructing clear images by exploring temporal-spatial correlation to depress noises. The main idea of our method is to restore the continuous dynamic process of real scenes in a latent space and learn the temporal correlations in a fine-grained manner. Furthermore, to model the dynamic process more effectively, we design a conditional ODE where the latent state of each timestamp is conditioned on the observed spike data. Subsequently, forward and backward inferences are conducted through the ODE to investigate the correlations between the representation of the target timestamp and the information from both past and future contexts. Additionally, we incorporate a Unet structure with a pixel-wise attention mechanism at each level to learn spatial correlations. Experimental results demonstrate that our method outperforms state-of-the-art methods across several metrics.

关键词： Spike camera image reconstruction neural ordinary differential equation temporal-spatial correlation

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Efficient Algorithms for Personalized PageRank Computation: A Survey

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2024年第9期36卷 4582-4602页

作者： Yang, Mingji Wang, Hanzhi Wei, Zhewei Wang, Sibo Wen, Ji-Rong Renmin Univ China Beijing 100872 Peoples R China Pazhou Lab Huangpu Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Methods MOE Key Lab Data Engn & Knowledge Engn Guangzhou 510555 Guangdong Peoples R China Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes s and t, the PPR value pi(s)(t)equals the probability that an alpha-discounted random walk from s terminates at t and reflects the importance between s and tin a bidirectional way. As a generalization of Google's celebrated PageRank centrality, PPR has been extensively studied and has found multifaceted applications in many fields, such as network analysis, graph mining, and graph machine learning. Despite numerous studies devoted to PPR over the decades, efficient computation of PPR remains a challenging problem, and there is a dearth of systematic summaries and comparisons of existing algorithms. In this paper, we recap several frequently used techniques for PPR computation and conduct a comprehensive survey of various recent PPR algorithms from an algorithmic perspective. We classify these approaches based on the types of queries they address and review their methodologies and contributions. We also discuss some representative algorithms for computing PPR on dynamic graphs and in parallel or distributed environments.

关键词： Vectors Surveys Clustering algorithms Heuristic algorithms Classification algorithms Reviews Measurement uncertainty Graphs and networks graph algorithms PageRank personalized PageRank

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A learnable sampling method for scalable graph neural networks

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NEURAL NETWORKS 2023年 162卷 412-424页

作者： Zhao, Weichen Guo, Tiande Yu, Xiaoxi Han, Congying Univ Chinese Acad Sci UCAS Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China MediaTek Singapore Singapore

With the development of graph neural networks, how to handle large-scale graph data has become an increasingly important topic. Currently, most graph neural network models which can be extended to large-scale graphs are based on random sampling methods. However, the sampling process in these models is detached from the forward propagation of neural networks. Moreover, quite a few works design sampling based on statistical estimation methods for graph convolutional networks and the weights of message passing in GCNs nodes are fixed, making these sampling methods not scalable to message passing networks with variable weights, such as graph attention networks. Noting the end-to-end learning capability of neural networks, we propose a learnable sampling method. It solves the problem that random sampling operations cannot calculate gradients and samples nodes with an unfixed probability. In this way, the sampling process is dynamically combined with the forward propagation process of the features, allowing for better training of the networks. And it can be generalized to all message passing models. In addition, we apply the learnable sampling method to GNNs and propose two models. Our method can be flexibly combined with different graph neural network models and achieves excellent accuracy on benchmark datasets with large graphs. Meanwhile, loss function converges to smaller values at a faster rate during training than past methods. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Graph neural networks Large-scale data Learnable sampling method

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A semi-supervised concept-cognitive computing system for dynamic classification decision making with limited feedback information

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期315卷 1123-1138页

作者： Mi, Yunlong Wang, Zongrun Quan, Pei Shi, Yong Cent South Univ Sch Business Changsha 410083 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

In dynamic environments, making classification decisions based on classical intelligent decision support systems is a challenge, as the classification performance of decision -making and the time -cost of learning need to be considered simultaneously. Moreover, many tasks of classification decisions lack label information because annotating data is time-consuming, labor-intensive and expensive process. This means that some standard intelligent decision support systems will perform inferior performance if they cannot dynamically make full use of the information behind abundant unlabeled data. Therefore, by incorporating knowledge representation and dynamic updating mechanisms into concept learning processes, we introduce a novel dynamic concept learning approach, namely semi -supervised concept -cognitive computing system (s2C3S), for making classification decisions by jointly utilizing some labeled data and abundant unlabeled data under dynamic environments. A theoretical analysis has shown that the proposed s2C3S can achieve significantly lower computational costs and higher classification accuracies than the existing incremental K Nearest Neighbor method (IKNN) and concept -cognitive computing system (C3S). The experimental results on various datasets further demonstrated that our system is effective for dynamic classification decision -making with limited labeled data under dynamic learning processes. Additionally, s2C3S can also be applied to computer -assisted intelligent diagnosis from the given medical images (such as chest X-ray images) dynamically and accurately.

关键词： Decision support systems Dynamic decision-making data streams Semi-supervised learning Concept-cognitive computing

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