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Asymptotical event-based input-output constrained boundary control of flexible manipulator agents under a signed digraph

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science China(Information sciences) 2025年第4期68卷 319-335页

作者： Xiangqian YAO Xiangmin XU Wei HE Yu LIU School of Future Technology South China University of Technology School of Automation and Institute of Artificial Intelligence Beijing Information Science and Technology University School of Intelligence Science and Technology University of Science and Technology Beijing School of Automation Science and Engineering South China University of Technology

This article tackles the boundary event-based bipartite consensus tracking control problem for the flexible manipulator multi-agent network over a signed diagraph. Each follower agent is the flexible manipulator with unknown disturbances,modeling uncertainties, input saturations and backlashes, and asymmetric output constraints. To reduce the continuous updating of control inputs, a new dynamic event-triggering mechanism is used. Under multiple constraints, achieving the asymptotic convergence point by point in space of the manipulator's vibration state is a control challenge. To solve this issue, we propose a new asymptotic convergence lemma. In control design, radial basis neural networks are employed to estimate nonlinear uncertain terms and the barrier Lyapunov function is used to accomplish the output constraints. Based on the Lyapunov direct method, a novel distributed boundary event-based control algorithm is designed to guarantee that the closed-loop network can reach the asymptotical bipartite consensus tracking and vibration suppression. Moreover, Zeno behaviors can be excluded for each agent. Finally, some numerical results are presented to demonstrate the validity and superiority of the designed control algorithm.

关键词： flexible-link manipulator input-output constraint event-based asymptotical bipartite tracking vibrationprotectłinebreak control

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Feature-Grounded Single-Stage Text-to-Image Generation

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Tsinghua science and technology 2024年第2期29卷 469-480页

作者： Yuan Zhou Peng Wang Lei Xiang Haofeng Zhang School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)***,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approaches the ground-truth image ***,the multistage generation strategy results in complex T2I ***,this study proposes a novel feature-grounded single-stage T2I model,which considers the“real”distribution learned from training images as one input and introduces a worst-case-optimized similarity measure into the loss function to enhance the model's generation *** results on two benchmark datasets demonstrate the competitive performance of the proposed model in terms of the Frechet inception distance and inception score compared to those of some classical and state-of-the-art models,showing the improved similarities among the generated image,text,and ground truth.

关键词： text-to-image(T2I) feature-grounded single-stage generation Generative Adversarial Network(GAN)

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Optimizing SDN Controller to Switch Latency for Controller Placement Problem

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Informatica (Slovenia) 2024年第8期48卷 165-176页

作者： Zobary, Firas School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

Software-Defined Networking (SDN) updates network flexibility by decoupling the data plane from control planes, employing a logically centralized yet physically distributed multi-controller architecture. The optimal placement of controllers and their quantity presents a significant challenge known as the Controller Placement Problem (CPP). This study addresses the optimization of average propagation delay between controllers and switches, introducing an enhancement version of well-known K-Means algorithm for network partitioning and controller placement, called an Advanced K-Means algorithm. The proposed algorithm strategically minimizes the average propagation delay by situating controllers in optimal nodes within each sub-network. Evaluation through simulations on the Internet OS3E topology demonstrates the algorithm's efficacy, showcasing a 22%, 11%, 7%, and 3% reduction in average propagation delay compared to DBCP, POCO, CNPA, and HDIDS, respectively. These results establish the proposed algorithm as a competitive solution, emphasizing its capacity to achieve comparable or superior performance in mitigating latency between controllers and switches when compared to existing algorithms. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Controllers

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Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks

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Tsinghua science and technology 2024年第5期29卷 1524-1539页

作者： Ling Tan Ying Liang Jingming Xia Hui Wu Jining Zhu School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China

Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small *** propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once(RCM-YOLO),which improves the identifiability of small masses by increasing the resolution of feature maps,adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters,and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature *** the training process,we propose an adaptive positive sample selection algorithm to automatically select positive samples,which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the *** verify the performance of our model,we used public datasets to carry out the *** results showed that the mean Average Precision(mAP)of RCM-YOLO reached 90.34%,compared with YOLOv5,the missed detection rate for small masses of RCM-YOLO was reduced to 11%,and the single detection time was reduced to 28 *** detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between *** method can help doctors in batch screening of breast images,and significantly promote the detection rate of small masses and reduce misdiagnosis.

关键词： mammography diagnosis mass detection deep learning cross-layer attention adaptive positive sample selection

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Robust video question answering via contrastive cross-modality representation learning

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science China(Information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG School of Information Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center School of Computer Science and Information Engineering Hefei University of Technology Institutes of Physical Science and Information Technology Anhui University School of Computer Science and Technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

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Knowledge Distillation via Hierarchical Matching for Small Object Detection

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Journal of computer science & technology 2024年第4期39卷 798-810页

作者： Yong-Chi Ma Xiao Ma Tian-Ran Hao Li-Sha Cui Shao-Hui Jin Pei Lyu School of Computer Science and Artificial Intelligence Zhengzhou UniversityZhengzhou 450000China

Knowledge distillation is often used for model compression and has achieved a great breakthrough in image classification,but there still remains scope for improvement in object detection,especially for knowledge extraction of small *** main problem is the features of small objects are often polluted by background noise and not prominent due to down-sampling of convolutional neural network(CNN),resulting in the insufficient refinement of small object features during *** this paper,we propose Hierarchical Matching Knowledge Distillation Network(HMKD)that operates on the pyramid level P2 to pyramid level P4 of the feature pyramid network(FPN),aiming to intervene on small object features before *** employ an encoder-decoder network to encapsulate low-resolution,highly semantic information,akin to eliciting insights from profound strata within a teacher network,and then match the encapsulated information with high-resolution feature values of small objects from shallow layers as the *** this period,we use an attention mechanism to measure the relevance of the inquiry to the feature *** in the process of decoding,knowledge is distilled to the *** addition,we introduce a supplementary distillation module to mitigate the effects of background *** show that our method achieves excellent improvements for both one-stage and twostage object ***,applying the proposed method on Faster R-CNN achieves 41.7%mAP on COCO2017(ResNet50 as the backbone),which is 3.8%higher than that of the baseline.

关键词： knowledge distillation object detection small object detection machine learning

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GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

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science China(Information sciences) 2025年第1期68卷 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer Science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of Computer Science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

关键词： graph representation learning graph neural networks graph contrastive learning graph augmentations graph pooling

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TSCL-FHFN: two-stage contrastive learning and feature hierarchical fusion network for multimodal sentiment analysis

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Neural Computing and Applications 2024年第19期36卷 11121-11135页

作者： Li, Yuqiang Weng, Wenxuan Liu, Chun School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Wuhan430070 China

Multimodal sentiment analysis faces two challenges: modality representation and modality fusion. Most of the existing models rely only on the feature extraction network to learn modality representation, and the fusion mechanism adopted by some models does not perform well. These factors are not conducive to the model learning rich emotional information and further affect the model’s predictive ability. To solve these problems, we propose a multimodal sentiment analysis model based on two-stage contrastive learning and feature hierarchical fusion network (TSCL-FHFN). First, we apply the idea of contrastive learning to unimodal feature representation and multimodal fusion feature representation respectively. By designing a two-stage contrastive learning task, TSCL-FHFN learns similar features for data with the same emotion category and learns distinguishable features for data with different emotion categories. This enables the model to better learn the features of emotional differences. Second, in order to further explore the deep semantic association of multimodal data, we propose a multimodal feature hierarchical fusion network (FHFN). The core idea is to design an attention-based directional cross-modal transformer so that one modality can receive information from the other modality, thereby obtaining complementary information between two modalities. Then, FHFN uses the low-rank tensor fusion method to further learn interactive information between multiple modalities. Finally, we conduct a series of comparative experiments on CMU-MOSI and CMU-MOSEI datasets. Compared with the current representative models, the TSCL-FHFN model achieves better experimental results. In addition, ablation experiments further verify the effectiveness of the improved TSCL-FHFN model. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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Formal Verification of Data Modifications in Cloud Block Storage Based on Separation Logic

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Chinese Journal of Electronics 2024年第1期33卷 112-127页

作者： Bowen ZHANG Zhao JIN Hanpin WANG Yongzhi CAO Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science Peking University School of Computer and Artificial Intelligence Zhengzhou University School of Computer Science and Cyber Engineering Guangzhou University

Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage(CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can provide interfaces for other types. Data modifications in CBS have potential risks such as null reference or data *** verification of these operations can improve the reliability of CBS to some extent. Although separation logic is a mainstream approach to verifying program correctness, the complex architecture of CBS creates some challenges for verifications. This paper develops a proof system based on separation logic for verifying the CBS data modifications. The proof system can represent the CBS architecture, describe the properties of the CBS system state, and specify the behavior of CBS data modifications. Using the interactive verification approach from Coq, the proof system is implemented as a verification tool. With this tool, the paper builds machine-checked proofs for the functional correctness of CBS data modifications. This work can thus analyze the reliability of cloud storage from a formal perspective.

关键词： Formal verification Separation logic Cloud block storage Verification tool Coq

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Multi-Scale Feature Fusion Network for Accurate Detection of Cervical Abnormal Cells

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computers, Materials & Continua 2025年第4期83卷 559-574页

作者： Chuanyun Xu Die Hu Yang Zhang Shuaiye Huang Yisha Sun Gang Li School of Computer and Information Science Chongqing Normal UniversityChongqing401331China School of Artificial Intelligence Chongqing University of TechnologyChongqing401331China

Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical ***,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell *** often refer to surrounding cells to identify *** emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal ***-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall ***,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different *** handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such *** results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.

关键词： Cervical abnormal cells image detection multi-scale feature fusion contextual information

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