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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zheng Ti Xuze Hao Renhai Chen College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Continual Semantic Segmentation (CSS) aims to continuously learn new classes while mitigating catastrophic forgetting. Existing CSS methods primarily address this challenge through knowledge distillation. While they focus on maintaining stability for old classes, this emphasis often restricts plasticity in learning new ones. In this work, we propose two plug-and-play methods to better balance stability and plasticity. First, for feature map distillation, we replace pixel-level constraints with statistical information extraction within a window, improving the learning of new classes while preserving structural integrity to overcome forgetting. Second, we use relative ranks of predictions to replace exact probabilistic values to loosen the constraints in the logit distribution of old classes. Extensive experiments demonstrate that our methods outperform state-of-the-art methods when integrating into several baselines across various CSS scenarios.

关键词： Semantic segmentation Semantics Signal processing Feature extraction Probabilistic logic information retrieval Acoustics Data mining Speech processing

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Lightweight Single Image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and Parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. The proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. The source codes are available at https://***/kylechuuuuu/ICTSRN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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Provincial anthropogenic emission inventory of carbonyl sulfide (COS) in China from 2015 to 2021

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Journal of Geographical Sciences 2025年第5期35卷 923-945页

作者： JIANG Yunhao CHEN Bin WANG Shaoqiang LI Tingyu CHEN Shiliang WANG Lunche WANG Lizhe School of Future Technology China University of Geosciences Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of Geosciences Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks Ministry of Natural Resources Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchCAS State Key Laboratory of Biogeology and Environmental Geology China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences

Carbonyl sulfide （COS） is an effective tracer for estimating Gross Primary Productivity （GPP） in the carbon *** the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately *** this study,an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission ***’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr-1from 2015–2021,differing from the trends of other *** an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic,a rapid rebound in emissions occurred following the resumption of economic *** 2021,industrial sources,coal combustion,agriculture and vehicle exhaust accounted for 76.8%,12.3%,10.5%and 0.4%of total COS emissions,*** aluminum industry was the primary COS emitter among industrial sources,contributing40.7%of total ***,Shanxi,and Zhejiang were the top three provinces in terms of anthropogenic COS emissions,reaching 39,21 and 17 Gg S yr-1,***-level regions （hereafter province） with high COS emissions are observed mainly in the eastern and coastal regions of China,which,together with the wind direction,helps explain the pattern of high COS concentrations in the Western Pacific Ocean in *** Green Contribution Coefficient of COS (GCCCOS) was used to assess the relationship between GDP and COS emissions,highlighting the disparity between GDP and COS contributions to green *** part of this analysis,relevant recommendations are proposed to address this *** COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model （STEM）,reducing uncertainties in the atmospheric COS source?sink budget and promoting understanding of the atmosphere sulfur cycle.

关键词： anthropogenic emissions carbonyl sulfide emission inventory provincial scale

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ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Motor Imagery Recognition

ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Mo...

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Xue, Haixiao Huang, Yuwen Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Motor imagery electroencephalogram recognition is a key area in brain-computer interfaces, with applications in human-computer interaction, rehabilitation, and virtual reality. Traditional methods often overlook the brain's topological characteristics and the correlations between EEG channels, resulting in suboptimal decoding. To address this, we propose an adaptive spatio-temporal graph neural network (ASTGNN), which constructs a brain graph topology and adaptively learns the topological adjacency matrix, exploring both common and individual electrode connections. The spatial features are extracted using a graph convolutional network, while a gated position-aware self-attention mechanism captures movement information and global dependencies, enhancing temporal feature extraction. Experiments show that ASTGNN significantly improves recognition, achieving an average accuracy of 87.36%. © 2025 SPIE.

关键词： Electroencephalography

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DTS: Dynamic Traffic Scrubbing Against Link Flooding Attacks with Programmable Data Plane

DTS: Dynamic Traffic Scrubbing Against Link Flooding Attacks...

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Liu, Fang Meng, Zhaoran Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Link flood attacks (LFA) represent a significant cybersecurity threat due to their ability to cause extensive network disruptions. Traditional traffic scrubbing methods, relying on static rules, often struggle to counter dynamic LFAs effectively. This paper introduces the dynamic traffic scrubbing (DTS) method, leveraging programmable data planes. DTS integrates moving target defense principles into traffic scrubbing, dynamically deploying and adjusting scrubbing rules to thwart attackers. Game theory is employed to analyze the distributed denial of service attack and defense strategies. Experimental results demonstrate that DTS effectively mitigates dynamic LFAs with minimal overhead, showcasing its robustness in defending against sophisticated cyber threats. © 2025 SPIE.

关键词： Denial-of-service attack

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Cross-Modal Ship Grounding: Towards Large Model for Enhanced Few-Shot Learning 27th

Cross-Modal Ship Grounding: Towards Large Model for Enhanced...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hu, Quan Chen, Li Feng, Zhida Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781124

A growing body of research indicates that employing large models for adaptation to downstream tasks often yields remarkable performance. However, in the domain of ship detection, the potential of these large models is frequently underutilized due to domain shift issues. This paper introduces the Cross-Modal Ship Grounding (CSG) model, which leverages an efficient Cross-Modal Adapter (CMA) technology to transfer the general detection capabilities of large models to ship images, addressing domain shift with minimal training costs. To mitigate the challenges posed by complex and variable background interference, the Water-Land Separation (WLS) module is proposed to focus specifically on the water area. This module effectively addresses the issue of background target interference, thereby enhancing the model’s accuracy in complex scenes. Empirical evaluations on both private and public datasets demonstrate that the CSG model surpasses all state-of-the-art models in performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Finite-Time Consensus of Second-Order Multiagent Systems With Input Saturation via Hybrid Sliding-Mode Control

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 14623-14632页

作者： Jiang, Xiaowei Jiao, Ranran Li, Bo Zhang, Xianhe Yan, Huaicheng China University of Geosciences School of Automation Wuhan430079 China Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Anhui University of Finance and Economics School of Finance Bengbu233030 China Hubei Normal University School of Electrical Engineering and Automation Huangshi435002 China Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China

This paper addresses the finite-time consensus (FTC) issue for second-order multi-agent systems (MASs) with nonlinear disturbances. To tackle the challenges posed by increasingly complex communication environments, an innovative integral sliding-mode surface is designed using event-triggered control. Furthermore, the study explores communication constraints between the leader and followers, employing impulsive control to facilitate communication only at specific intervals. A novel hybrid integral sliding-mode control protocol is advanced for the second-order MASs, which effectively eliminates the "Zeno phenomenon" and notably reduces both communication frequency and energy consumption, thereby enhancing overall communication efficiency. Ultimately, the efficacy of the put forward protocol is verified through simulation examples including comparison experiment. © 2004-2012 IEEE.

关键词： Protocols Sliding mode control Event detection Multi-agent systems Costs Training Topology Switches Sufficient conditions Stability analysis

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Comments and extensions on "State-equivalent form and minimum-order compensator design for rectangular descriptor systems"

arXiv

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arXiv 2025年

作者： Shi, Shuo Zhang, Juan Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China

This technical note presents a counterexample showing that the equivalence conditions proposed by Geng et al. (IEEE Trans. Automat. Control, 2024), which use a minimum-order compensator (MOC) to achieve desired designs, including generalized regularity or both generalized regularity and free of impulse, are sufficient but not necessary. Furthermore, revised equivalence conditions are introduced, along with an equivalence condition ensuring the closed-loop system remains generalized regular, impulse-free, and stable using MOC. Additionally, it is shown that output feedback can replace the MOC, achieving the same design without increasing dimensionality. These findings are validated through a circuit *** Codes 93B05, 93B52, 15A03 Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback

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HOZ++: Versatile Hierarchical Object-to-Zone Graph for Object Navigation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Zhang, Sixian Song, Xinhang Yu, Xinyao Bai, Yubing Guo, Xinlong Li, Weijie Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

The goal of object navigation task is to reach the expected objects using visual information in unseen environments. Previous works typically implement deep models as agents that are trained to predict actions based on visual observations. Despite extensive training, agents often fail to make wise decisions when navigating in unseen environments toward invisible targets. In contrast, humans demonstrate a remarkable talent to navigate toward targets even in unseen environments. This superior capability is attributed to the cognitive map in the hippocampus, which enables humans to recall past experiences in similar situations and anticipate future occurrences during navigation. It is also dynamically updated with new observations from unseen environments. The cognitive map equips humans with a wealth of prior knowledge, significantly enhancing their navigation capabilities. Inspired by human navigation mechanisms, we propose the Hierarchical Object-to-Zone (HOZ++) graph, which encapsulates the regularities among objects, zones, and scenes. The HOZ++ graph helps the agent to identify the current zone and the target zone, and computes an optimal path between them, then selects the next zone along the path as the guidance for the agent. Moreover, the HOZ++ graph continuously updates based on real-time observations in new environments, thereby enhancing its adaptability to new environments. Our HOZ++ graph is versatile and can be integrated into existing methods, including end-to-end RL and modular methods. Our method is evaluated across four simulators, including AI2-THOR, RoboTHOR, Gibson, and Matterport 3D. Additionally, we build a realistic environment to evaluate our method in the real world. Experimental results demonstrate the effectiveness and efficiency of our proposed method. The code is available at https://***/sx-zhang/HOZplus. © 1979-2012 IEEE.

关键词： Digital elevation model

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UMIT: Unifying Medical Imaging Tasks via Vision-Language Models

arXiv

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arXiv 2025年

作者： Yu, Haiyang Yi, Siyang Niu, Ke Zhuo, Minghan Li, Bin Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

With the rapid advancement of deep learning, particularly in the field of medical image analysis, an increasing number of Vision-Language Models (VLMs) are being widely applied to solve complex health and biomedical challenges. However, existing research has primarily focused on specific tasks or single modalities, which limits their applicability and generalization across diverse medical scenarios. To address this challenge, we propose UMIT, a unified multi-modal, multi-task VLM designed specifically for medical imaging tasks. UMIT is able to solve various tasks, including visual question answering, disease detection, and medical report generation. In addition, it is applicable to multiple imaging modalities (e.g., X-ray, CT and PET), covering a wide range of applications from basic diagnostics to complex lesion analysis. Moreover, UMIT supports both English and Chinese, expanding its applicability globally and ensuring accessibility to healthcare services in different linguistic contexts. To enhance the model’s adaptability and task-handling capability, we design a unique two-stage training strategy and fine-tune UMIT with designed instruction templates. Through extensive empirical evaluation, UMIT outperforms previous methods in five tasks across multiple datasets. The performance of UMIT indicates that it can significantly enhance diagnostic accuracy and workflow efficiency, thus providing effective solutions for medical imaging applications. The code and adopted datasets are available at https://***/dzosamu/UMIT/tree/main. © 2025, CC0.

关键词： Visual languages

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