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arXiv 2025年

作者： Zhang, Tianxiang Wen, Zhaokun Kong, Bo Liu, Kecheng Zhang, Yisi Zhuang, Peixian Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education the School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Shunde Graduate School of University of Science and Technology Beijing China

Referring Remote Sensing Image Segmentation (RRSIS) is critical for ecological monitoring, urban planning, and disaster management, requiring precise segmentation of objects in remote sensing imagery guided by textual descriptions. This task is uniquely challenging due to the considerable vision-language gap, the high spatial resolution and broad coverage of remote sensing imagery with diverse categories and small targets, and the presence of clustered, unclear targets with blurred edges. To tackle these issues, we propose BTDNet, a novel framework designed to bridge the vision-language gap, enhance multi-scale feature interaction, and improve fine-grained object differentiation. Specifically, BTDNet introduces: (1) the Bidirectional Spatial Correlation (BSC) for improved vision-language feature alignment, (2) the Target-Background TwinStream Decoder (T-BTD) for precise distinction between targets and non-targets, and (3) the Dual-Modal Object Learning Strategy (D-MOLS) for robust multimodal feature reconstruction. Extensive experiments on the benchmark datasets RefSegRS and RRSIS-D demonstrate that BTDNet achieves state-of-the-art performance. Specifically, BTDNet improves the overall IoU (oIoU) by 3.76 percentage points (80.57) and 1.44 percentage points (79.23) on the two datasets, respectively. Additionally, it outperforms previous methods in the mean IoU (mIoU) by 5.37 percentage points (67.95) and 1.84 percentage points (66.04), effectively addressing the core challenges of RRSIS with enhanced precision and robustness. datasets and codes are available at https://***/wzk913ysq/BAJP. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Nucleic Acid Cascade Amplification Empowered Self-Powered Sensing Platform Based on the Confined Bioconjugate Catalysis Strong Redox Boosting Mechanism

SSRN

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SSRN 2025年

作者： Jin, Chenchen Wu, Qingnian Ma, Yunzhi Tan, Xue-Cai Huang, Ke-Jing Xie, Shengyu Duan, Yun Education Department of Guangxi Zhuang Autonomous Region Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Products Guangxi Key Laboratory of Chemistry and Engineering of Forest Products Key Laboratory of Chemistry and Engineering of Forest Products State Ethnic Affairs Commission School of Chemistry and Chemical Engineering Guangxi Minzu University Nanning530006 China Analysis and Test Center Chinese Academy of Tropical Agricultural Sciences Haikou571101 China School of Big Data Guangxi Vocational and Technical College Nanning530226 China

Patulin is one of the most common mycotoxins found in moldy fruits and poses a significant threat to human health. This study develops a confined bioconjugate efficiently catalysis self-powered sensing system based on CoOOH nanosheets and glucose oxidase,. In this sensing system, the spider-type Walker is activated when its aptamer is captured by the target analyte to release DNA through an equivalent DNA transducer under the action of the auxiliary factor Mg2+. This process opens a series of hairpin probes, self-assembles into a DNA framework, and activates the catalytic hairpin assembly-hybridization chain reaction cascade amplification. The construction of a DNA network structure on the electrode surface significantly increases the probe loading capacity, thereby greatly enhancing the sensitivity and selectivity of target detection. Under optimized conditions, the detection linear range of this method is from 10-14 to 10-6 mg/mL, with a detection limit of 9.05×10-15 mg/mL (S/N = 3). The application of the confined bioconjugate catalytic system, the equivalent DNA transducer constructed by the spider-type Walker, and the nucleic acid cascade amplification technology has increased the sensitivity of this method by more than 1000 times compared to other electrochemical sensing methods. Additionally, this method exhibits excellent selectivity and a broad detection range, providing a reliable solution for the on-site rapid detection of ultra-trace patulin in food. © 2025, The Authors. All rights reserved.

关键词： Glucose oxidase

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Enhanced NIR-II fluorescence molecular imaging with self-supervised denoising diffusion

Enhanced NIR-II fluorescence molecular imaging with self-sup...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Fu, Lidan Lu, Binchun Xiao, Anqi Bao, Jie Qu, Qiaojun Tian, Jie Hu, Zhenhua CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Department of Radiology First Hospital of Shanxi Medical University Taiyuan China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi’an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (纸本)9781510685987

With real-time identification through enhanced contrast, fluorescence molecular imaging in the second near-infrared window (NIR-II, 1000-1700 nm) has been widely investigated in clinical and biomedical scenarios. However, a fundamental challenge of NIR-II fluorescence molecular imaging is the inevitable noise during the photon detection process. Imaging noise not only degrades the quality of images but also amplifies measurement uncertainty by overwhelming the true imaging signal. Moreover, the removal of noise in fluorescence imaging is a critical problem due to the tissue-dependent and multi-factorial characteristics of the imaging process. Supervised denoising networks trained with paired low-quality and high-quality samples have gained popularity with superior performance. Unfortunately, acquiring vast quantities of paired data is consistently a challenging or unattainable task in clinical practice. In this work, we demonstrate that the proposed self-supervised denoising diffusion model can directly suppress noise and enhance visual quality for NIR-II fluorescence molecular imaging without paired training data. Based on the cutting-edge conditional generation process for high quality image synthesis, we propose an adaptive-step guided denoising diffusion model (AGDD) that incorporates noise statistic distribution theory to adaptively determine the inference steps for blind image denoising. Quantitative results of in-vivo and clinical experiments illustrate that the proposed method is an effective self-supervised approach that surpasses competing alternatives, which will promote the development of high-quality and low-cost NIR-II fluorescence molecular imaging. © 2025 SPIE.

关键词： Molecular imaging

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Maintaining high magnesium/lithium selectivity in nanofiltration at high salinity: optimizing membrane charge distribution for synergistic Donnan and ion adhesion effects

引用

Journal of Membrane Science 2025年 729卷

作者： Ji, Wenyan Li, Yuping Duan, Feng Liu, Lulu Cao, Renqiang Yin, Jingya Sun, He Luo, Jianquan Cao, Hongbin State Key Laboratory of Biopharmaceutical Preparation and Delivery Institute of Process Engineering University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100190 China Department of Chemistry Tianjin University Tianjin300072 China Chemistry & Chemical Engineering Data Center Chinese Academy of Sciences Beijing100190 China National Engineering Research Center of Green Recycling for Strategic Metal Resources Beijing100190 China Collaborative Innovation Center of Chemical Science and Engineering Tianjin300072 China

Nanofiltration membranes are essential for lithium recovery from brine, but their selectivity declines under high-salt conditions due to electrostatic shielding and fouling. We introduce a novel strategy to enhance membrane performance by enhancing positive charge density on the membrane surface while creating negative charge microdomains within the membrane. Specifically, we functionalized a polyethyleneimine-based membrane with 1,4-butanesultone through a ring-opening reaction, which balanced ion-membrane affinity and optimized the transport kinetics of water and ions. The resulting membrane exhibited a significant enhancement in permeance up to 11.2 L m−2 h−1 bar−1 and selectivity for Mg2+/Li+ up to 51. Notably, it maintained over 96.21 % MgCl2 rejection across a wide salt concentration range of 1–20 g L−1, outperforming the results reported in literature. The comprehensive testing and mechanistic study indicate that this improved performance is due to the synergy of Donnan effect and ion-attraction, facilitated by the designed charge distribution. A two-stage nanofiltration process employing this membrane reduced the Mg2+/Li+ ratio in brine samples from 43 to 0.25, highlighting its practical application potential. This work introduces a new paradigm for the design of highly selective membranes. © 2025 Elsevier B.V.

关键词： Ring opening polymerization

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Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

arXiv

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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MRST-- An Efficient Monitoring Technology of Summarization on Stream data

引用

Journal of Computer Science & Technology 2007年第2期22卷 190-196页

作者：樊小泊解婷婷李翠平陈红 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree （MRST） which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.

关键词： Haar wavelet sliding window stream data

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Multi-Hop Attention Diffusion Graph Neural Networks For Multimodal Fake News Detection

Multi-Hop Attention Diffusion Graph Neural Networks For Mult...

引用

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

作者： Zhongqiang Huang Dongli Lu Ying Sha Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Fake news often spreads rapidly on social media in various forms, exposing users to a large amount of misinformation and disinformation. Recently, promising results have been achieved in fake news detection. However, most of the approaches only consider one-hop neighborhood information in cross-modal mapping relationships or focus mainly on pairwise interactions of multimodal fake news. However, the fine-grained feature mapping of multimodal fake news may go beyond binary relations. To address these issues, we propose a Multi-Hop Attention Diffusion Graph Neural Networks in this work to capture and complement the multivariate mapping relationships between fine-grained features, while extracting cross-modal higher-order alignment representations for multimodal fake news detection via cross-modal contrastive learning and modal matching. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed approach in the multimodal fake news detection task.

关键词： Visualization Social networking (online) Fuses Spread spectrum communication Contrastive learning Signal processing Feature extraction Graph neural networks Fake news Speech processing

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PB-UAP: Hybride Universal Adversarial Attack for Image Segmentation

PB-UAP: Hybride Universal Adversarial Attack for Image Segme...

引用

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

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training Image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models Network architecture Robustness Speech processing

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