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arXiv 2023年

作者： Peng, Yuanyuan Pan, Lin Luan, Pengpeng Tu, Hongbin Li, Xiong School of Electrical and Automation Engineering East China Jiaotong University Nanchang330000 China School of Computer Science Northwestern Polytechnical University Xi’an710000 China School of Software East China Jiaotong University Nanchang330000 China

Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. Which can effectively capture the spatial attention of curvature objects. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. Compared with ground truths, the presented model acquired high F1 values of 0.826, 0.819 and 0.808, and high accuracy of 0.969, 0.973, and 0.973 on three

关键词： Image enhancement

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MetaSlicing: A Novel Resource Allocation Framework for Metaverse

arXiv

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arXiv 2022年

作者： Chu, Nam H. Hoang, Dinh Thai Nguyen, Diep N. Phan, Khoa T. Dutkiewicz, Eryk Niyato, Dusit Shu, Tao The School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology La Trobe University Melbourne Australia The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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A Swin transformer encoder-based StyleGAN for unbalanced endoscopic image enhancement

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computers in Biology and Medicine 2024年 175卷 108472-108472页

作者： Deng, Bo Zheng, Xiangwei Chen, Xuanchi Zhang, Mingzhe School of Information Science and Engineering Shandong Normal University Jinan250352 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250352 China State Key Laboratory of High–end Server & Storage Technology Jinan250101 China

With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic systems have become an effective tool for reducing the diagnostic costs and shortening the treatment cycle of patients. Typically, the performance of these systems depends on deep learning models which are pre-trained with large-scale labeled data, for example, early gastric cancer based on endoscopic images. However, the expensive annotation and the subjectivity of the annotators lead to an insufficient and class-imbalanced endoscopic image dataset, and these datasets are detrimental to the training of deep learning models. Therefore, we proposed a Swin Transformer encoder-based StyleGAN (STE-StyleGAN) for unbalanced endoscopic image enhancement, which is composed of an adversarial learning encoder and generator. Firstly, a pre-trained Swin Transformer is introduced into the encoder to extract multi-scale features layer by layer from endoscopic images. The features are subsequently fed into a mapping block for aggregation and recombination. Secondly, a self-attention mechanism is applied to the generator, which adds detailed information of the image layer by layer through recoded features, enabling the generator to autonomously learn the coupling between different image regions. Finally, we conducted extensive experiments on a private intestinal metaplasia grading dataset from a Grade-A tertiary hospital. The experimental results show that the images generated by STE-StyleGAN are closer to the initial image distribution, achieving a Fréchet Inception Distance (FID) value of 100.4. Then, these generated images are used to enhance the initial dataset to improve the robustness of the classification model, and achieved a top accuracy of 86 %. © 2024 Elsevier Ltd

关键词： Image enhancement

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Distributed quantum architecture search

arXiv

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arXiv 2024年

作者： Situ, Haozhen He, Zhimin Zheng, Shenggen Li, Lvzhou College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Electronic and Information Engineering Foshan University Foshan528000 China Shenzhen518045 China Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

Variational quantum algorithms, inspired by neural networks, have become a novel approach in quantum computing. However, designing efficient parameterized quantum circuits remains a challenge. Quantum architecture search tackles this by adjusting circuit structures along with gate parameters to automatically discover high-performance circuit structures. In this study, we propose an end-to-end distributed quantum architecture search framework, where we aim to automatically design distributed quantum circuit structures for interconnected quantum processing units with specific qubit connectivity. We devise a circuit generation algorithm which incorporates TeleGate and TeleData methods to enable nonlocal gate implementation across quantum processing units. While taking into account qubit connectivity, we also incorporate qubit assignment from logical to physical qubits within our quantum architecture search framework. A two-stage progressive training-free strategy is employed to evaluate extensive circuit structures without circuit training costs. Through numerical experiments on three VQE tasks, the efficacy and efficiency of our scheme is demonstrated. Our research into discovering efficient structures for distributed quantum circuits is crucial for near-term quantum computing where a single quantum processing unit has a limited number of qubits. Distributed quantum circuits allow for breaking down complex computations into manageable parts that can be processed across multiple quantum processing units. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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CommGPT: A Graph and Retrieval-Augmented Multimodal Communication Foundation Model

arXiv

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arXiv 2025年

作者： Jiang, Feibo Zhu, Wanyun Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Dobre, Octavia A. Hunan Normal University China Hunan University of Technology and Business China Department of Computer Science Brunel University London United Kingdom School of Intelligent Software and Engineering Nanjing University China Southeast University China Memorial University Canada

Large Language Models (LLMs) possess human-level cognitive and decision-making capabilities, making them a key technology for 6G. However, applying LLMs to the communication domain faces three major challenges: 1) Inadequate communication data;2) Restricted input modalities;and 3) Difficulty in knowledge retrieval. To overcome these issues, we propose CommGPT, a multimodal foundation model designed specifically for communications. First, we create high-quality pretraining and fine-tuning datasets tailored in communication, enabling the LLM to engage in further pretraining and fine-tuning with communication concepts and knowledge. Then, we design a multimodal encoder to understand and process information from various input modalities. Next, we construct a Graph and Retrieval-Augmented Generation (GRG) framework, efficiently coupling Knowledge Graph (KG) with Retrieval-Augmented Generation (RAG) for multi-scale learning. Finally, we demonstrate the feasibility and effectiveness of the CommGPT through experimental validation. Copyright © 2025, The Authors. All rights reserved.

关键词： Knowledge graph

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STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

arXiv

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arXiv 2022年

作者： Ji, Jiahao Wang, Jingyuan Jiang, Zhe Jiang, Jiawei Zhang, Hu State Key Laboratory of Software Development Environment School of Computer Science & Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer & Information Science & Engineering The University of Florida United States MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science & Engineering Beihang University Beijing China

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Differential equations

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SDCM: An Efficient Camellia Oleifera Fruits Detection Algorithm in Dense Occlusion Scenes

SDCM: An Efficient Camellia Oleifera Fruits Detection Algori...

引用

Computing and Big Data (ICCBD), International Conference on

作者： Youyang Zhang Qiuxia Chen Wenhua Cui Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence of the Guang-dong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (数字)9798350317008

ISBN: (纸本)9798350317015

To realize intelligent management in the Camellia oleifera fruit industry and foster the efficient growth of its supply chain, the accurate identification and detection of Camellia oleifera fruits are essential. However, in natural environments, these fruits exhibit challenges such as clustering, mutual occlusion, and obstruction by leaves. This paper proposes a Camellia oleifera fruits detection algorithm based on an improved YOLOv7 deep convolutional neural network, which is referred to as the SDCM network model, and add the function of counting the number of fruits. We introduce an enhancement to the backbone layer of the YOLOv7 model by incorporating a module called SDCM (Shortcut and Dilation Convolution based Module). This module enriches multiscale edge information, enabling the network to extract features more effectively, thus improving detection accuracy. The experimental results show that the detection accuracy of the SDCM network model is significantly improved compared with the original YOLOv7 model, and the detection performance of the model in this paper is superior compared with other classical detection models. By enhancing the detection accuracy of the model and adding the function of fruit counting, the model in this paper can provide more reliable and accurate detection performance and the function of predicting fruit yield for the development of the Camellia oleifera fruit industry, improve the production efficiency, and reduce the artificial resources.

关键词： Industries Accuracy Image edge detection Supply chains Production Predictive models Feature extraction

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SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

arXiv

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arXiv 2023年

作者： Wang, Di Zhang, Jing Du, Bo Xu, Minqiang Liu, Lin Tao, Dacheng Zhang, Liangpei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science Faculty of Engineering The University of Sydney Australia National Engineering Research Center of Speech and Language Information Processing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at SAMRS. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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Cares-Unet: Contour-Guided Attention-Based Res-Unet for Opticdisc and Optic Cup Segmentation

SSRN

引用

SSRN 2023年

作者： Gizaw Tohye, Tewodros Qin, Zhiguang Wake Hundera, Negalign Assefa, Maregu Lejebo Leka, Habte Atandoh, Peter School of Information and Software Engineering University of Electronic Science and Technology of China North Jianshe Road Sichuan Chengdu610054 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China The Zhejiang Institute of Optoelectronics Jinhua China

Background and objectives: Optic disc and Optic cup segmentation is critical in order to identifyglaucoma. But, the nature of fundus images like shape variations of optic disc from person to person ,a lack of contrast and fuzzy borders makes it difficult to easily identify the segmentation region. Veryfew methods have tried to analyze and utilize most important silent features to precisely segment therequired regions from fundus ***: In this study, we introduced CARES-UNet which is based on contour-guided attention toseparate the optic disc and the optic cup regions. The CARES-UNet model’s encoder part is integratingCBAM after each residual block. The model achieves precise boundary detection by incorporatingcontour cues during the segmentation process reducing the likelihood of missing crucial ***, we provide the residual blocks with both the original and contour information to retainthe discriminative regions. Based on the experiments conducted on two publicly available datasetsREFUGUE and DRISHTI-GS .Results: We verify the effectiveness and efficiency of our proposed method on two public *** specifically obtained dice coefficients of 0.98, 0.969 IoU, 0.99 recall, and precision of 0.97 on theREFUGUE dataset. DRISHTI-GS obtain optic disc results with dice coefficients of 0.97, 0.95 IOU,0.99 Recall, and 0.90 ***: These encouraging findings demonstrate the potential of the contour-guided attentionbased RES-UNet model for segmenting the optic disc and the optic cup, enhancing ophthalmologyand enabling more accurate detection and management of eye illnesse © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Large Capacity Image Watermarking Model using SWT and Laplacian Pyramid

Large Capacity Image Watermarking Model using SWT and Laplac...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Baowei Wang Fan Yang Yufeng Wu Changyu Dai Wenjue Huang Xingyuan Zhao Engineering Research Center of Digital Forensics Ministry of Education China Waterford Institute Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

Image watermarking algorithms have developed rapidly in recent years. Most of the Deep Neural Network(DNN)-based image watermarking algorithms embed only 30 bits or 64 bits messages in 128 × 128 images, while few algorithms embed large capacity messages. This is mainly because embedding large capacity messages will significantly influence the performance of algorithms. However, the limited capacity of these messages inhibits their effectiveness in certain data protection scenarios. Therefore, we propose a DNN-based large capacity image water-marking model. By using Stationary Wavelet Transform(SWT) and Laplacian pyramid transform, we expand the embedding capacity and achieve embedding 128 bits messages in 128 × 128 images. Experimental results show that under the same conditions, the proposed model is superior to state-of-the-art models, and has excellent performance.

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