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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Lu, Runuo Dong, Shoubin Jia, Jianxin Wang, Xusheng Liu, Kai Chen, Jinsong Guo, Shanxin Zheng, Xiaorou Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS. ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Lin, Shiying Hu, Rong Li, Zuoyong Lin, Qinghua Zeng, Kun Wu, Xiang Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Fuzhou University Affiliated Provincial Hospital Provincial Clinical Medical College Fujian Medical University Department of Urology Fuzhou350001 China

In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixed grouping strategy (FGS) can affect the deep model’s capacity to perceive the overall structure of the image. To address these challenges, we propose a medical image segmentation framework, called Kolmogorov-Arnold Network with the adaptive group strategy and contextual Transformer based on Unet (KAC-Unet). First, we propose the adaptive group strategy (AGS) to balance the grouping of different input channels, alleviating the performance degradation caused by differences in group information. Then, we propose the Shift Tokenized Kolmogorov-Arnold Network (KAN) Block to capture complex features in medical images through flexible nonlinear transformations and shift operations. Extensive experiments are conducted on three medical image segmentation datasets. The results demonstrate the effectiveness and superiority of our proposed method compared with state-of-the-art algorithms. © 1963-2012 IEEE.

关键词： Convolutional neural networks

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ReFrame: A Resource-Friendly Cloud-Assisted On-Device Deep Learning Framework for Vision Services

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IEEE Transactions on Services Computing 2025年

作者： Xie, Jianhang Ding, Chuntao Guan, Qingji Zhou, Ao Li, Yidong Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

Cloud-assisted Internet of Things (IoT) device deployment of deep neural networks (DNNs) promotes On-device deep learning to provide users with ubiquitous high-quality services by solving the contradiction between insufficient IoT device resources and intensive demand for high-performance DNN resources. However, most existing methods optimize DNNs by considering one or two terms of transmission, computation, and storage resources, but do not consider all three terms at the same time in cloud-assisted IoT device deployment and updating DNNs. To this end, we propose a non-learnable module-based ResNet and a cloud-assisted on-device deep learning framework, ReFrame, based on the consideration of three indicators: model transmission parameters, computation resources, and storage resources. In the proposed method, we first specify that some parameters in DNNs are non-learnable and randomly initialized, so that, these parameters can be saved and reproduced with a few random seeds. By doing so, the cloud only transmits random seeds and learnable parameters to reduce the number of parameter transmissions. Secondly, we reduce the computation resource consumption of the model by introducing computation-friendly operators, such as pooling, to replace vanilla convolutions. Finally, since random seeds are used to save non-learnable model parameters, on IoT devices we only need to store random seeds and learnable parameters to reproduce the well-trained model. Compared with saving the complete model, our method greatly reduces IoT device storage resource consumption. Experimental results on image classification, object detection, and semantic segmentation tasks demonstrate the effectiveness of the proposed method. Specifically, on the CIFAR-10, our proposed method reduces approximately 89% of FLOPs and 90% of transmitted data in the prototype system compared to ResNet-18. © 2008-2012 IEEE.

关键词： Cloud platforms

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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A generalized complex power iteration-based algorithm for quartic–quadratic optimization problems over a spherical constraint and its applications

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Journal of Computational and Applied Mathematics 2025年 470卷

作者： Tang, Jie Sheng, Zhou Li, Xin Department of Data Science Anhui University of Technology Maanshan Anhui243032 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan Anhui243032 China School of Mathematics Hefei University of Technology Hefei Anhui230009 China

Quartic–quadratic optimization problems over the sphere have received a great attention in various real problems. Observations of three concrete applications primarily trigger our interest in this problem. This paper considers a generalized complex power method to solve the problem. Such a nonconvex optimization problem is studied by giving a suitable shift term to ensure convexity. Several convergence results are established, and we analyze the worst-case complexity in terms of the step-size and the number of iterations. We then apply our method to three tasks: (1) computing the ground state of Bose–Einstein condensation (BEC);(2) computing the quartic–quadratic optimization problems over the ellipsoid, which arises in the problem of computing the ground state of BEC by using a new discretization;and (3) approximating the desired transmit beampattern. The reported numerical results indicate that our method performs relatively quickly and effectively. © 2025 Elsevier B.V.

关键词： Iterative methods

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'Pre-train, prompt' Framework to Boost Graph Neural Networks Performance in EEG analysis

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Cui, Can-Ming Chen, Hong-Yi Chen, Man-Sheng Li, Jiahong Tong, Zhaopeng Fang, Chanmei Wang, Chang-Dong Cai, Yuexin Department of Otolaryngology Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangdong Guangzhou510000 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510000 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530000 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Electroencephalography (EEG) is a vital noninvasive technique used in neuroscience research and clinical diagnosis. However, EEG data have a complex nonEuclidean structure and are often scarce, making training effective graph neural network (GNN) models difficult. We propose a 'pre-train, prompt' framework in graph neural networks for EEG analysis, called GNN-based EEG Prompt Learning (GEPL). The framework first uses unsupervised contrastive learning to pre-train on a large-scale EEG dataset. It then transfers the generic EEG knowledge learned by the model to target EEG datasets through graph prompt learning, thereby enhancing the model's performance with a limited amount of EEG data from the target domain. We tested the framework on five EEG datasets, and the results showed that GEPL outperformed traditional fine-tuning methods in classification accuracy and area under the ROC curve (AUC). GEPL demonstrated improved generalization, robustness, and computational efficiency, thereby significantly reducing the overfitting risks associated with limited EEG data. Moreover, the model provided interpretable results, highlighting relevant brain regions during classification tasks. This research suggests that the 'pre-train, prompt' paradigm is well-suited for EEG analysis and offers potential applications in other domains where data are limited. © 2013 IEEE.

关键词： Contrastive Learning

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Any-Angle Routing Algorithm for Microfluidic Biochips Driven by Flow Path

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2025年第4期62卷 978-988页

作者： Youlin, Pan Shuai, Guo Xing, Huang Genggeng, Liu College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

Continuous-flow microfluidic biochips (CFMBs) have become a hot research topic in recent years due to their ability to perform biochemical assays automatically and efficiently. For the first time, PathDriver+ takes the requirements of the actual fluid transportation into account in the design process of CFMBs and implements the actual fluid transport and removal, and plans separate flow paths for each transport task, which have been neglected in previous work. However, PathDriver+ does not take full advantage of the flexibility of CFMBs routing because it only considers the optimization of flow channel length for the global routing in the mesh model, except for the detailed routing. In addition, PathDriver+ only considers the X architecture, while the existing work shows that the any-angle routing can utilize the routing resources more efficiently and shorten the flow channel length. To address the above issues, we propose a flow path-driven arbitrary angle routing algorithm, which can improve the utilization of routing resources and reduce the flow channel length while considering the actual fluid transportation requirements. The proposed algorithm constructs a search graph based on constrained Delaunay triangulation to improve the search efficiency of routing solutions while ensuring the routing quality. Then, a Dijkstra-based flow path routing method is used on the constructed search graph to generate a routing result with a short channel length quickly. In addition, in the routing process, channel reuse strategy and intersection optimization strategy are proposed for the flow path reuse and intersection number optimization problems, respectively, to further improve the quality of routing results. The experimental results show that compared with the latest work PathDriver+, the length of channels, the number of ports used, and the number of channel intersections are significantly reduced by 33.21%, 11.04%, and 44.79%, respectively, and the channel reuse rate is i

关键词： High level synthesis

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Control Logic Routing for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2025年第4期62卷 950-962页

作者： Cai, Huayang Huang, Xing Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

With the advancement of electronic design automation, continuous-flow microfluidic biochips have become one of the most promising platforms for biochemical experiments. This chip manipulates fluid samples in milliliters or nanoliters by utilizing internal microvalves and microchannels, and thus automatically performs basic biochemical experiments, such as mixing and detection. To achieve the correct bioassay function, the microvalves deployed inside the chip are usually managed by a multiplexer-based control logic, and valves receive control signals from a core input through the control channel for accurate switching. Since biochemical reactions typically require high sensitivity, the length of control paths connecting each valve needs to be reduced to ensure immediate signal propagation, and thus to reduce the signal propagation delay. In addition, to reduce the fabrication cost of chips, a vital issue to be addressed in the logic architecture design is how to effectively reduce the total channel length within the control logic. To address the above issues, we propose a deep reinforcement learning-based control logic routing algorithm to minimize the signal propagation delay and total control channel length, thereby automatically constructing an efficient control channel network. The algorithm employs the dueling deep Q-network architecture as the agent of the deep reinforcement learning framework to evaluate the tradeoff between signal propagation delay and total channel length. Besides, the diagonal channel routing is implemented for the first time for control logic, thus fundamentally improving the efficiency of valve switching operations and reducing the fabrication cost of the chip. The experimental results demonstrate that the proposed algorithm can effectively construct a high-performance and low-cost control logic architecture. © 2025 Science Press. All rights reserved.

关键词： Deep reinforcement learning

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Upconversion Luminescence and Optical Thermometry Behaviors of Yb3+ and Ho3+ Co-Doped Nabi(Moo4)2 Single Crystal

SSRN

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SSRN 2025年

作者： Su, Wenzhi Zhang, Mengyu Zhang, Chuancheng Zou, Yong Liu, Wenpeng Ding, Shoujun School of Microelectronics and Data Science Anhui University of Technology Anhui Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Anhui Maanshan243002 China Anhui Hefei230031 China

Upconversion (UC) fluorescence intensity ratio (FIR) technology has emerged as a promising approach for optical thermometry due to its ability to provide non-contact, high-precision temperature measurements. Unlike conventional UC phosphor-based systems, single-crystal UC materials offer enhanced responsiveness and sensitivity for thermal detection, thanks to their structural stability and superior thermal conductivity. In this study, we successfully synthesized an Yb3+, Ho3+:NaBi(MoO4)2 (NBM) single crystal with exceptional optical quality using the Czochralski (Cz) growth technique. Comprehensive structural characterization was carried out using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM). The results confirmed that Yb3+, Ho3+:NBM crystallizes in a tetragonal structure with the space group I41/a. The emission of different visible light colors under various excitation wavelengths was found to be due to the multiphonon relaxation process. Systematic analysis of power-dependent up-conversion emission spectra conclusively showed that the green and red up-conversion emissions arise from two-photon excitation processes. Moreover, temperature-dependent spectral evolution in the range of 300-500 K was quantitatively analyzed, allowing the calculation of both absolute sensitivity (Sa) and relative sensitivity (Sᵣ) parameters to evaluate the material's optical thermometric performance. These findings highlight the promising potential of Yb3+, Ho3+:NBM single crystal for developing high-performance optical thermometry devices. © 2025, The Authors. All rights reserved.

关键词： Single crystals

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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