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EGSNet:An Efficient Glass Segmentation Network Based on Multi-Level Heterogeneous Architecture and Boundary Awareness

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computers, Materials & Continua 2024年第12期81卷 3969-3987页

作者： Guojun Chen Tao Cui Yongjie Hou Huihui Li Qingdao Institute of Software College of Computer Science and TechnologyChina University of Petroleum(East China)Qingdao266580China

Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous *** inefficiency of the models is mainly due to employing homogeneous modules to process features of different *** modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across *** propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and *** divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary *** on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep *** models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced *** the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only *** results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.

关键词： Image segmentation multi-level heterogeneous architecture feature differences

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Multi-scale Spectral Mixture Neural Operator

Multi-scale Spectral Mixture Neural Operator

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

作者： Jing, Fengrui Ding, Hongzhen Song, Tao Qingdao Institute of Software College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350368741

Seeking effective numerical approximations for partial differential equations (PDEs) is a major challenge in modern science and technology. Recently, AI-inspired data-driven solvers, such as neural operators, have achieved great success in quickly PDE solving. However, in the design of neural operators, the processing of frequency domain information is crucial, while the importance of low-frequency information is excessively overlooked. In order to reduce low-frequency error in PDE solving, we propose the multi scale spectral mixture neural operator (MSSMNO) architecture and design a residual learning structure that transfers residuals in multi-scale cycle by combining the frequency domain learning patterns of the FNO method. Meanwhile, we designed interpolation operator and restriction operator to effectively transmit and reconstruct high-frequency information in MSSMNO. Experimentally, MSSMNO achieves state-of-the-art and yields a relative error reduction of 21.9% averaged on four classical benchmarks. © 2025 IEEE.

关键词： neural operator PDE

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KGM4DTI: A Depression Knowledge Graph-Driven Multi-semantic Multi-view Computational Framework for Drug-Target Interaction Prediction

KGM4DTI: A Depression Knowledge Graph-Driven Multi-semantic ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tian, Xu Han, Peifu Li, Xue Ding, Hongzhen He, Wei Jing, Fengrui Wang, Shuang Wang, Xun Song, Tao Qingdao Institute of Software College of Computer Science and Technology Shandong Qingdao266580 China

ISBN: (纸本)9798350386226

Depression is a prevalent global mental health issue that significantly impacts both individuals and societies. Current methods often overlook the heterogeneity of depressive symptoms and fail to fully leverage available data, underscoring the need for more advanced computational methods DTI prediction. This paper introduces KGM4DTI, a multi-semantic, multi-view, and multi-level meta-path Transformer framework that integrates Transformer and GNN. KGM4DTI effectively captures both global semantic and local structural information between interaction pairs within a depression knowledge graph (DepKG), which comprises 2,258 drugs, 3,360 proteins, 6 subtypes of depression, 196 phenotypes, and 238 pathways. Comparative experiments have demonstrated that KGM4DTI significantly outperforms existing state-of-the-art methods, achieving an AUROC of 99.72 and an AUPR of 99.76. The robustness of framework is further confirmed through extensive ablation studies, which highlight the crucial role of integrating both global and local information. These results underscore the potential of KGM4DTI in accurately predicting DTI. This approach has promising implications for drug discovery, particularly in developing effective treatments for depression, and could extend its applicability to broader mental health disorders. The data and code are available at https://***/Tianxyuu/KGM4DTI/. © 2024 IEEE.

关键词： Knowledge graph

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LLMaaS: Serving Large-Language Models on Trusted Serverless Computing Platforms

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 405-415页

作者： Cai, Zinuo Ma, Rongbo Fu, Yicheng Zhang, Weishan Ma, Ruhui Guan, Haibing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Qingdao Institute of Software College of Computer Science and Technology Qingdao266555 China

In recent years, the emergence of large-language models (LLMs) has profoundly transformed our production and lifestyle. These models have shown tremendous potential in fields, such as natural language processing, speech recognition, and recommendation systems, and are increasingly playing crucial roles in applications such as human-computer interaction and intelligent customer service. Efficient inference solutions for LLMs in data centers have been extensively researched, with a focus on meeting users' quality of service requirements. In this article, we focus on two additional requirements that responsible LLM inference should meet under QoS conditions: security throughout the model execution process and low maintenance requirements for the inference system. Therefore, we propose LLMaaS, a trusted model inference platform based on a serverless computing platform aimed at providing inference as a service for LLMs. First, we design a trusted serverless computing platform based on software guard extension (SGX), which includes distributed identity verification and SGX device plugins to ensure the security and trustworthiness of the inference process. Additionally, to reduce the maintenance requirements of the system, we enhance the SGX-based deep learning computing framework, including replacing PyTorch and using a greedy algorithm for graph partitioning. We conduct tests on four typical large models, and the experimental results demonstrate that, with minimal overhead and user code modifications, we can ensure the security of model execution. © 2020 IEEE.

关键词： Quality of service

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ScKADE: Single-Cell Multi-Omics Integration with Kolmogorov-Arnold Deep Embedding

ScKADE: Single-Cell Multi-Omics Integration with Kolmogorov-...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wu, Jiarui Wu, Wenhao Li, Hengxiao Wang, Ruiqian Zhang, Yulin Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Shandong University of Science and Technology College of Mathematics and Systems Science Qingdao China

ISBN: (纸本)9798350386226

Single-cell sequencing technologies allow for a high-resolution examination of various biological activities, such as the genome, transcriptome, and epigenetic modifications, across thousands of cells. In contrast, single-omics approaches fall short in fully capturing the intricate molecular mechanisms. In this regrad, integrating multi-omics data can potentially provide a more comprehensive understanding of fundamental biological processes. Although sequencing technologies have emerged that can simultaneously obtain different omics from single cells, they are limited by throughput and the types of omics. A more common scenario is that data from different omics are measured independently, resulting in unpaired cell compositions. Therefore, efficient algorithms are needed to integrate and analyze unpaired multi-omics *** this study, we propose a novel framework for integrating unpaired single-cell multi-omics data based on the Kolmogorov-Arnold deep embedding model (scKADE). This framework introduces Kolmogorov-Arnold Networks (KAN) to map different omics data to a unified common feature space for adversarial alignment. Furthermore, to assess the contribution of different cells in data integration, we employ an unsupervised weight estimation algorithm to enhance the alignment quality. © 2024 IEEE.

关键词： Cytology

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Transformer-Based Gene Expression Levels Prediction Using Multimodal Information

Transformer-Based Gene Expression Levels Prediction Using Mu...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Song, Tao Pan, Zhiyi Song, Haonan Gao, Yuan Dai, Huanhuan Wang, Xun Qingdao Institute of Software China University of Petroleum College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350386226

Gene expression is a pivotal biological process within organisms, and in recent years, the prediction of gene expression levels has garnered increasing attention due to its vast potential in clinical applications. Predicting gene expression levels is a complex problem as gene expression is influenced by multiple factors, including but not limited to gene sequences, epigenetic modifications, transcription factor binding, and micro-environmental conditions. This paper proposes a model named Multimodal Expression, based on the Transformer architecture, which integrates various data types. The model can extract effective features from gene promoter sequences and combine pre-transcriptional and post-transcriptional regulatory information to predict gene expression levels. Experimental results demonstrate that our model can extract more effective information from promoter sequences, and the attention mechanism in the Transformer can integrate multiple data types to jointly predict gene expression levels. Compared to previous methods, our model's R2 values improved by 7.05%, 8.9%, and 1.91% when using gene sequence data alone, gene sequence data combined with mRNA half-life data, and gene sequence data combined with mRNA half-life data and transcription factor data, respectively. © 2024 IEEE.

关键词： Transcription factors

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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science China(Information sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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DCUI-MGraphDTA: Enabling Efficient Inference of a Drug-Target Binding Affinity Prediction Model on DCUs

DCUI-MGraphDTA: Enabling Efficient Inference of a Drug-Targe...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Song, Tao Zhang, Siyu Meng, Xiangyu Zhu, Zeyang Zhu, Xianxi Wang, Xun Qingdao Institute of Software China University of Petroleum College of Computer Science and Technology Qingdao266580 China

ISBN: (纸本)9798350386226

Efficient and accurate identification of drug-target affinity (DTA) is crucial in the virtual screening stage of drug discovery and the reuse of existing drugs. Deep neural networks are becoming popular tools for providing fast and accurate binding affinity prediction. MGraphDTA is one of the best performing models for DTA prediction, utilizing a super-deep graph neural network to extract features, demonstrating good generalization and interpretation capabilities. However, the complex deep neural networks come with a higher computational cost of model inference. Meanwhile, due to the expansion of drug databases, rapid completion of large-scale virtual screening still presents challenges. In this work, we propose an efficient binding affinity prediction method called DCUI-MGraphDTA through a series of optimizations on MGraphDTA to achieve large-scale virtual screening for millions of molecules on the Deep Computing Unit (DCU) cluster. The optimized DCUI-MGraphDTA model achieves a speedup of approximately 5.83 times on a single DCU compared to the original MGraphDTA. As the number of DCUs expands from 1 to 32, the total time for the model to process 1.28 million data significantly decreases, achieving a speedup of approximately 11.9 times. © 2024 IEEE.

关键词： Prediction models

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Scale Feature-Aware Generative Adversarial Network Improve MRI Device Data Imbalance for Healthy Consumption

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 984-996页

作者： Zhao, Yawu Wang, Shudong Qiao, Sibo Zhang, Yulin Wang, Jiehuan Yin, Wenjing Liu, Tiyao Pang, Shanchen China University of Petroleum Qingdao Institute of Software School of Computer Science and Technology Shandong Qingdao China Tiangong University The School of Software Tianjin China Shandong University of Science and Technology College of Mathematics and System Science Shandong Qingdao China Affliated Hospital of Jining Medical University Department of Medical Imaging Shandong Jining China

In consumer healthcare, the imbalance between the imaging data of MRI devices creates a significant barrier to the generalization ability of medical image segmentation. We employ generative adversarial networks to address the challenges inherent in the heterogeneity of data distribution among different MRI devices. First, we propose the Scale Feature Awareness Module (SFAM), which can skillfully capture image details at different scales and broader contextual information. Then, we propose the Dynamic Scale Attention Module (DSAM), which aims to combine feature mappings at different scales for different paths dynamically. We can assign different dynamic weights to each scale to enhance the feature representation. Finally, we propose a multi-scale discriminator to guide the generation of aneurysm images with different diameters. Based on the above modules, we designed Scale Feature Aware Generative Adversarial Network (SFAGAN) to generate medical images with the same distribution. It is experimentally demonstrated that SFAGAN improves PSNR, SSIM, and FID values by 0.64, 0.62, and 7.99, respectively, over the SOTA method. In addition, we use the generated data for downstream segmentation tasks to demonstrate the quality of the generated images. Notably, our SFAGAN has significant performance, making its application in medical systems very feasible. © 1975-2011 IEEE.

关键词： Dynamic contrast enhanced MRI

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ScDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

ScDCMAC:adaptive feedback clustering of scRNA-seq data with ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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