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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

Neural computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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A Contrast-Enhanced Null Subtraction Imaging Method using Dynamic DC Bias in Ultrafast Ultrasound imaging

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2025年第6期72卷 755-771页

作者： Yan, Xin Yang, Xiaodong Jing, Lingling Guo, Wei Wang, Yingqi Su, Xinwei Wang, Yuanyuan Shenyang University of Technology School of Information Science and Engineering Shenyang110870 China Ultrasound Department of Anshan Central Hospital Anshan114000 China CO. Ltd Suzhou215123 China Fudan University Department of Electronic Engineering and the Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai200433 China

Enhancing the resolution and contrast of ultrafast ultrasound imaging is imperative for the accuracy of clinical diagnostics. Null subtraction imaging (NSI) is a nonlinear beamforming technique capable of significantly enhancing lateral resolution. However, it suffers from issues of low-quality speckle pattern and poor contrast performance. To address this issue, we propose a novel contrast-enhanced NSI method that utilizes dynamic DC bias. Innovatively, we construct the dynamic DC bias using a generalized coherence factor (GCF) and a sigmoid transformation function that adapts the DC value based on the signal characteristics of different imaging regions. Furthermore, a normalization scheme is proposed to optimize the beamforming output, ensuring uniform pixel intensity throughout the final image. Simulation, phantom, and in vivo data are utilized for ultrasound beamforming to evaluate the performance of the proposed method. Quantitative results show that the proposed method significantly enhances the contrast ratio (CR) by 197%, the contrast-to-noise ratio (CNR) by 341%, the speckle signal-to-noise ratio (sSNR) by 302%, and the generalized contrast-to-noise ratio (gCNR) by 106% compared to the original NSI (in phantom). Point target imaging results indicate that the proposed method achieves a main lobe width slightly wider than the original NSI method, but much narrower than those of DAS and GCF. These findings confirm that the proposed method significantly enhances imaging contrast while preserving high resolution, which is of great significance for the further clinical application of ultrafast ultrasound imaging. © 1986-2012 IEEE.

关键词： Phantoms

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Coupled-mode phase matching on a silicon chip

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Physical Review A 2025年第3期111卷 033504-033504页

作者： Chao Wu Pingyu Zhu Miaomiao Yu Weihong Luo Qilin Zheng Kaikai Zhang Yingwen Liu Ping Xu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China Hefei National Laboratory Hefei 230088 China

Phase matching lies at the heart of the classical nonlinear process for frequency conversion and all-optical processing, as well as the spontaneous parametric process for the quantum light sources generation. In this work, we experimentally demonstrate the coupled-mode phase-matching (CMPM) engineered by the two-paralleled-coupled waveguides on a silicon-on-insulator (SOI) chip, extending the converted beam and entangled photons to the S band and L band. We measure the spectrum and efficiency of wavelength conversion, which successfully validates the designed CMPM scheme. We further measure the pairs generation rate of the biphoton based on the spontaneous four-wave mixing, which features a narrow bandwidth consistent well with the theoretical simulations. Multiple CMPM conditions can be achieved by exploiting the combinations of even and odd supermodes of the coupled system and tuned by designing a proper coupling gap between the two waveguides. The CMPM can extend the FWM nonlinear process ranging from the O band to the U band and even the midinfrared, enriching the working wavelength to a large extent. In addition, the CMPM features a discrete-band narrow bandwidth, which will greatly enhance the encoding density of frequency and the spectral purity of generated photon. In addition, our methods can be easily extended to a wide range of materials such as silicon nitride, dispensing with the dispersion engineering for phase matching.

关键词： Silicon nitride

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BEVUDA++: Geometric-Aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 5109-5122页

作者： Zhang, Rongyu Liu, Jiaming Li, Xiaoqi Chi, Xiaowei Wang, Dan Du, Li Du, Yuan Zhang, Shanghang Nanjing University School of Electronic Science and Engineering Nanjing210093 China The Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science Beijing100871 China The Hong Kong University of Science and Technology Academy of Interdisciplinary Studies Hung Hom Hong Kong

Vision-centric Bird’s Eye View (BEV) perception holds considerable promise for autonomous driving. Recent studies have prioritized efficiency or accuracy enhancements, yet the issue of domain shift has been overlooked, leading to substantial performance degradation upon transfer. We identify major domain gaps in real-world cross-domain scenarios and initiate the first effort to address the Domain Adaptation (DA) challenge in multi-view 3D object detection for BEV perception. Given the complexity of BEV perception approaches with their multiple components, domain shift accumulation across multi-geometric spaces (e.g., 2D, 3D Voxel, BEV) poses a significant challenge for BEV domain adaptation. In this paper, we introduce an innovative geometric-aware teacher-student framework, BEVUDA++, to diminish this issue, comprising a Reliable Depth Teacher (RDT) and a Geometric Consistent Student (GCS) model. Specifically, RDT effectively blends target LiDAR with dependable depth predictions to generate depth-aware information based on uncertainty estimation, enhancing the extraction of Voxel and BEV features that are essential for understanding the target domain. To collaboratively reduce the domain shift, GCS maps features from multiple spaces into a unified geometric embedding space, thereby narrowing the gap in data distribution between the two domains. Additionally, we introduce a novel Uncertainty-guided Exponential Moving Average (UEMA) to further reduce error accumulation due to domain shifts informed by previously obtained uncertainty guidance. To demonstrate the superiority of our proposed method, we execute comprehensive experiments in four cross-domain scenarios, securing state-of-the-art performance in BEV 3D object detection tasks, e.g., 12.9% NDS and 9.5% mAP enhancement on Day-Night adaptation. © 1991-2012 IEEE.

关键词： Teaching

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Edge-Driven Industrial computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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APP: Enabling Soft Real-Time Execution on Densely-Populated Hybrid Memory System 23

APP: Enabling Soft Real-Time Execution on Densely-Populated ...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zheng-Wei Wu Yun-Chih Chen Yuan-Hao Chang Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University and Mohamed bin Zayed University of Artificial Intelligence Department of Computer Science and NTU High Performance and Scientific Computing Center National Taiwan University

ISBN: (纸本)9798350323481

Memory swapping was considered slow and evil, but swapping to Ultra Low-Latency storage like Optane has become a promising solution to save power and cost, helping densely-populated edge server to overcome its DRAM capacity bottleneck. However, the lack of integration between CPU scheduling and memory paging causes soft real-time tasks running on edge servers to miss deadlines under heavy memory multiplexing. We propose APP (Adaptive Page Pinning), lightweight protection of working set memory to ensure meeting soft real-time task deadlines without starving other non-real-time tasks. Experiments show that APP alleviates thrashing in memory-intensive tasks and upholds soft real-time task deadlines.

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Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

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8th International Conference on Data science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Federated Meta Continual Learning for Efficient and Autonomous Edge Inference 24th

Federated Meta Continual Learning for Efficient and Autono...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Bingze Ho, Stella Qu, Youyang Xu, Chenhao Luan, Tom H. Gao, Longxiang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Biomedical Engineering The University of Melbourne VIC Melbourne Australia School of Information Technology Deakin University VIC Geelong Australia School of Cyber Engineering Xidian University Xi’an China

ISBN: (纸本)9789819615476

With an increasing volume of data being collected and processed on edge, Federated Learning (FL) has become a dominant approach to train shared global models on distributed data held by diverse edge clients without exposing privacy. However, with expectations of high-quality user experience, instantaneous intelligent decisions must be made at the network edge to meet performance, security, and latency requirements. Intuitively, this challenge becomes more daunting as edge devices are constrained by limited computing and storage resources. Furthermore, edge clients need to continuously acquire, update, accumulate, and apply knowledge in domains requiring the processing of sensitive data and long-term model training. It has been proven that edge clients often forget previous tasks knowledge after learning new tasks, resulting in catastrophic forgetting. To mitigate this, we propose a Federated Meta Continual Learning (FMCL) approach. It requires edge clients not only to learn new tasks quickly but also to memorize rapidly. Specifically, we adopted a MAML (Model-Agnostic Meta-Learning) framework with memory replay, setting up buffers at edge clients to consolidate old tasks. In such a way, the model learns optimal initialization to quickly adapt to all seen tasks while preventing catastrophic forgetting. Extensive evaluation results indicate that FMCL performs well in alleviating catastrophic forgetting and achieves a good balance between learning new tasks and remembering existing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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