IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第3页

A Generic and Robust Agent to Accomplish Quantum Control Tasks 实现量子控制任务的通用鲁棒智能体

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-06-01 Epub Date: 2026-02-12 DOI: 10.1109/TETCI.2026.3661605

Shihui Zhang;Zibo Miao;Qixiang Zhang;Xin Yi

{"title":"A Generic and Robust Agent to Accomplish Quantum Control Tasks","authors":"Shihui Zhang;Zibo Miao;Qixiang Zhang;Xin Yi","doi":"10.1109/TETCI.2026.3661605","DOIUrl":"https://doi.org/10.1109/TETCI.2026.3661605","url":null,"abstract":"In view of inevitable uncertainties or disturbances in quantum systems, efficient and systematic numerical methods for robust control design are of significant importance. Although reinforcement learning has recently been applied in the field of quantum control, completing quantum control tasks in practice is always challenging due to the lack of data from real-world environments. In this paper, a generic and robust agent can be constructed catering to system imperfections by applying a curriculum meta-reinforcement learning quantum control (CMQC) approach. The proposed approach incorporates an objective module that can break down a complex quantum control objective into multiple sub-objectives using an exponential decay strategy, enabling the agent to gradually learn to solve the final goal through experience transfer and knowledge consolidation. Furthermore, a meta module is employed to achieve adaptation to environmental imperfections. This allows the agent to adjust its strategy in real time, obtaining strong robustness even in the presence of uncertainties. The superiority of CMQC is demonstrated in robust control problems regarding state transfer in a two-level quantum system and realization of the <inline-formula><tex-math>$R_{x}(pi)$</tex-math></inline-formula> gate, with reduced computational cost and improved accuracy achieved. Our results pave the way towards more reliable and secure quantum information processing with uncertainties involved.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 3","pages":"2235-2244"},"PeriodicalIF":6.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MLF-CAST: Multi-Loop Spatio-Temporal Fusion With Cross-Attention for Explainable Triple-Modal MCI-AD Classification 多环路时空融合与交叉注意在可解释的三模态MCI-AD分类中的应用

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-06-01 Epub Date: 2026-04-09 DOI: 10.1109/TETCI.2026.3679246

Hongzhi Kuai;Yu Cao;Jinquan Ji;Shuqiang Wang;Jianzhuo Yan;Ning Zhong

{"title":"MLF-CAST: Multi-Loop Spatio-Temporal Fusion With Cross-Attention for Explainable Triple-Modal MCI-AD Classification","authors":"Hongzhi Kuai;Yu Cao;Jinquan Ji;Shuqiang Wang;Jianzhuo Yan;Ning Zhong","doi":"10.1109/TETCI.2026.3679246","DOIUrl":"https://doi.org/10.1109/TETCI.2026.3679246","url":null,"abstract":"Early diagnosis of Alzheimer's disease (AD) remains a major challenge in neurodegenerative research, particularly in developing a computational and intelligent recognition method. Mild cognitive impairment (MCI) is frequently seen as the intermediate stage of AD, which can help us recognize early AD using multi-modal fusion strategies. In this study, we propose the Multi-Loop Spatio-Temporal Fusion with Cross-Attention (MLF-CAST) model to achieve effective and explainable classification of MCI and AD based on structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (fMRI). The model consists of three tailored feature extractors: a multi-kernel convolutional branch (MKCB) for sMRI, a masked dense modulation branch (MDMB) for dMRI, and a recurrent temporal convolutional branch (RTCB) for fMRI. To achieve optimal multi-modal fusion, a multi-loop spatio-temporal fusion (MLSTF) module is introduced that aligns and integrates modality-specific representations using a multi-loop reinforcement mechanism. Moreover, a hybrid loss strategy is designed to jointly optimize contrastive alignment, cross-modality distillation, and classification objectives. Experiments on the ADNI dataset across three binary classification tasks demonstrate that our methods outperform several state-of-the-art fusion models in terms of accuracy and AUC. Furthermore, the proposed framework offers interpretability by identifying discriminative brain regions and altered connectivity patterns that are consistent with empirical findings.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 3","pages":"2316-2331"},"PeriodicalIF":6.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information IEEE计算智能新兴主题汇刊

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-06-01 Epub Date: 2026-03-28 DOI: 10.1109/TETCI.2026.3691560

引用次数: 0

Spatiotemporal Traffic Speed Forecasting via Conditional Flow-Based Generative Deep Learning With Dynamic Graph Pooling 基于动态图池的条件流生成深度学习时空交通速度预测

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-06-01 Epub Date: 2026-03-25 DOI: 10.1109/TETCI.2026.3670662

Mahdi Khodayar;Ali Farajzadeh Bavil;Mohsen Saffari

{"title":"Spatiotemporal Traffic Speed Forecasting via Conditional Flow-Based Generative Deep Learning With Dynamic Graph Pooling","authors":"Mahdi Khodayar;Ali Farajzadeh Bavil;Mohsen Saffari","doi":"10.1109/TETCI.2026.3670662","DOIUrl":"https://doi.org/10.1109/TETCI.2026.3670662","url":null,"abstract":"Traffic speed forecasting is a crucial problem with significant real-world applications, including optimizingtraffic flow and reducing congestion. Current data-driven approaches struggle with high sample complexity, overfitting, and limited long-range temporal dependencies. To overcome these challenges, we introduce a Conditional Flow-based Generative (CFG) model with dynamic Graph Feature Extraction (GFE) and pooling for spatiotemporal traffic speed forecasting. First, traffic speed measurements are modeled as a dynamic graph, where nodes represent sensor readings and edges encode spatial correlations. A GFE network captures sparse spatial features of the dynamic graph through a hierarchical graph learning architecture, incorporating a graph pooling layer to reduce feature dimensions and a structure learning mechanism for refined graph topologies. These extracted features are fed into a peephole LSTM to capture both short- and long-term spatiotemporal dependencies. Finally, the CFG model predicts future traffic speeds of the sensors by learning the underlying data distribution, improving forecast accuracy by capturing complex patterns, and handling variability in traffic data through generative feature extraction. The proposed framework is tested using three real-world traffic datasets, PEMS-BAY, METR-LA, and PEMS07, and has demonstrated outstanding performance compared to state-of-the-art approaches. This exceptional performance is due to its ability to enhance generalization and prediction accuracy while mitigating overfitting, offering a significant advancement over recent traffic forecasting models.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 3","pages":"2301-2315"},"PeriodicalIF":6.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148516481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-Short-Range Fused Network for Heterogeneous Graph Representation Learning 异构图表示学习的长短程融合网络

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-04-01 Epub Date: 2025-09-29 DOI: 10.1109/TETCI.2025.3611281

Zhenli He;Rui Wang;Cheng Xie

{"title":"Long-Short-Range Fused Network for Heterogeneous Graph Representation Learning","authors":"Zhenli He;Rui Wang;Cheng Xie","doi":"10.1109/TETCI.2025.3611281","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3611281","url":null,"abstract":"Heterogeneous graph representation learning is critical for analyzing complex data structures. Metapaths within this field are vital as they elucidate high-order relationships across the graph, significantly enhancing the model's accuracy and depth of understanding. However, metapaths tend to prioritize long-range dependencies of the target node, which can lead to the oversight of potentially crucial <inline-formula><tex-math>$1^{st}$</tex-math></inline-formula>-order heterogeneous neighbors or short-range dependencies. To address this challenge and circumvent manual labeling, we propose the Long-Short-Range Fusion Network (LSRFN), an innovative unsupervised approach to heterogeneous graph representation learning. LSRFN implements two distinct masking strategies, short-range and long-range, to obscure the features of the target node and its heterogeneous neighbors. These representations are then learned independently under each masking regime. In a subsequent step, features learned with long-range masking are employed to reconstruct the metapath-based adjacency matrix. Concurrently, features from both masking conditions are leveraged to reconstruct the masked node features jointly, culminating in the representation learning process. Our experimental results affirm that LSRFN achieves top-tier performance in node classification and clustering tasks on the majority of datasets and remains competitive on the rest.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 2","pages":"1684-1699"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discourse-Driven Detection of Multimodal Misinformation 话语驱动的多模态错误信息检测

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-04-01 Epub Date: 2025-12-29 DOI: 10.1109/TETCI.2025.3645643

Adrián Girón;Javier Huertas-Tato;David Camacho

{"title":"Discourse-Driven Detection of Multimodal Misinformation","authors":"Adrián Girón;Javier Huertas-Tato;David Camacho","doi":"10.1109/TETCI.2025.3645643","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3645643","url":null,"abstract":"The spread of misinformation on social platforms is amplified by multimodal posts and the discourse they trigger. We present <monospace>LANTERN</monospace>, a lightweight framework that detects fake posts by pairing a compact LVLM (Large Visio-Language Model) with a graph-to-text rendering of reply threads. Comment trees are pruned breadth-first and serialized with depth-based indentation and in-line metadata (author, engagement), then concatenated with image tokens; the LVLM is fine-tuned with low-rank adapters and a linear head, using the end-of-sequence state for classification. On <italic>Fakeddit, <monospace>LANTERN</monospace> attains state-of-the-art accuracy with only 150 k training samples, achieving <bold>97.16% (2-way), <bold>97.05% (3-way), <bold>93.72% (6-way). To assess versatility beyond Reddit, we additionally evaluate <monospace>LANTERN-TEXT</monospace> on <italic>PHEME (text-only rumor cascades) and adapt the pipeline to <italic>Twitter15/16 (structure-only replies), observing robust gains from shallow discourse and and measurable gains even when reply text is absent. Large-scale multimodal pretraining plus lightweight adaptation and structured conversational context yields a practical and reproducible approach to misinformation detection.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 2","pages":"1389-1398"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prototype-Based Modality-Specific Feature Distillation for Incomplete Multimodal Sentiment Analysis 不完全多模态情感分析中基于原型的情态特征提取

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-04-01 Epub Date: 2025-12-29 DOI: 10.1109/TETCI.2025.3641665

Aihua Zheng;Yongbo Wang;Jiaxiang Wang;Chenglong Li;Bing Yin

{"title":"Prototype-Based Modality-Specific Feature Distillation for Incomplete Multimodal Sentiment Analysis","authors":"Aihua Zheng;Yongbo Wang;Jiaxiang Wang;Chenglong Li;Bing Yin","doi":"10.1109/TETCI.2025.3641665","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3641665","url":null,"abstract":"Incomplete multimodal sentiment analysis is to recognize human emotional states by leveraging the incomplete multimodal information including text, audio, and visual data. Existing methods mainly focus on reconstructing from available modalities, while ignoring the specific information of missing modalities and the inconsistent distribution between original and recovered data. In this work, we propose a novel Prototype-based modality-specific feature Distillation Network called ProDNet, which can recover the specific information of missing modalities with more consistent distributions between original and recovered data. In particular, we design a category-specific prototype for each modality to store the modality-specific information, and reconstruct the missing modality by combining the invariant features from existing modalities with the prototype of the missing modality in a feature self-distillation manner. To obtain favorable invariant features, we design an invariant feature distillation strategy, in which the weight-gating mechanism is introduced to enhance invariant feature representation. Finally, to reduce distributional inconsistency, we design a distribution consistency distillation loss to enforce the consistent distribution of original and recovered data by considering the sample correlation between teacher and student networks. Extensive experiments on two benchmark datasets, CMU-MOSI and CMU-MOSEI, demonstrate the superior performance of the proposed method, particularly in scenarios with higher missing rates.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 2","pages":"1905-1917"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CoWS: Self-Supervised Representation Pre-Training for Cross-Machine Fault Diagnosis 奶牛：跨机器故障诊断的自监督表示预训练

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1109/TETCI.2026.3654383

Xiaohan Chen;Rui Yang;Yihao Xue;Zidong Wang

引用次数: 0

A Human-in-the-Loop Framework for Interactive and Explainable Data-Driven Modeling 交互式和可解释数据驱动建模的人在循环框架

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-04-01 Epub Date: 2025-12-08 DOI: 10.1109/TETCI.2025.3637807

Shengbo Hong;Wen Yu;Tianyou Chai

引用次数: 0

A Unified Framework for Epidemic Forecasting and Contact Reconstruction via Bidirectional Enhancement 基于双向增强的流行病预测与接触重建统一框架

IF 6 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2026-04-01 Epub Date: 2025-12-05 DOI: 10.1109/TETCI.2025.3637844

Yuanyuan Zeng;Kai Xiang

{"title":"A Unified Framework for Epidemic Forecasting and Contact Reconstruction via Bidirectional Enhancement","authors":"Yuanyuan Zeng;Kai Xiang","doi":"10.1109/TETCI.2025.3637844","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3637844","url":null,"abstract":"Epidemic forecasting and contact network reconstruction are inherently coupled, yet most existing methods address them independently, limiting performance and interpretability. In this work, we propose <bold>EpiLoop, a unified closed-loop framework that enables bidirectional enhancement between case forecasting and latent contact network inference. EpiLoop integrates two synergistic components: DualView-GraphLSTM, a spatiotemporal graph neural network that models infection dynamics and mobility patterns, and VGAE-CR, a variational graph autoencoder that reconstructs time-varying subpopulation-level contact networks. Crucially, EpiLoop establishes a feedback mechanism: reconstructed networks are fed back to refine future forecasts, while latent infection states from forecasting guide network reconstruction—enabling mutual co-refinement through iterative optimization. We evaluate EpiLoop on real-world COVID-19 trajectory data from four Chinese provinces, using a comprehensive metapopulation network. Results show that EpiLoop achieves state-of-the-art performance, outperforming decoupled baselines in both forecasting accuracy (lower MAE and RMSE) and contact reconstruction (AUC up to 88.8%) under partial observability and behavioral changes. By unifying prediction and mechanism discovery, EpiLoop advances epidemic modeling toward transparent, self-improving systems and generalizes to behavior-modulated dynamics beyond COVID-19.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 2","pages":"1216-1228"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0