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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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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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SS4Rec: Continuous-Time Sequential Recommendation with State Space Models

arXiv

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arXiv 2025年

作者： Xiao, Wei Wang, Huiying Zhou, Qifeng Department of Automation Xiamen University Xiamen361005 China School of Engineering Westlake University Hangzhou China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Xiamen University Xiamen China

Sequential recommendation is a key area in the field of recommender systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Differences in pseudogene evolution contributed to the contrasting flavors of turnip and Chiifu, two Brassica rapa subspecies

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Plant Communications 2023年第1期4卷 120-135页

作者： Xin Yin Danni Yang Youjie Zhao Xingyu Yang Zhili Zhou Xudong Sun Xiangxiang Kong Xiong Li Guangyan Wang Yuanwen Duan Yunqiang Yang Yongping Yang Plant Germplasm and Genomics Center Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650204China Institute of Tibetan Plateau Research at Kunming Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunmingYunnanChina University of Chinese Academy of Sciences Beijing 100049China

Pseudogenes are important resources for investigation of genome evolution and genomic diversity because they are nonfunctional but have regulatory effects that influence plant adaptation and ***,few systematic comparative analyses of pseudogenes in closely related species have been ***,we present a turnip(Brassica rapa ***)genome sequence and characterize pseudogenes among diploid Brassica species/*** results revealed that the number of pseudogenes was greatest in Brassica oleracea(CC genome),followed by ***(AA genome)and then Brassica nigra(BB genome),implying that pseudogene differences emerged after species *** Brassica AA genomes,pseudogenes were distributed asymmetrically on chromosomes because of numerous chromosomal insertions/rearrangements,which contributed to the diversity among *** differences among subspecies were reflected in the flavor-related glucosinolate(GSL)***,turnip had the highest content of pungent substances,probably because of expansion of the methylthioalkylmalate synthase-encoding gene family in turnips;these genes were converted into pseudogenes in *** ***(Chiifu).RNA interference-based silencing of the gene encoding 2-oxoglutarate-dependent dioxygenase 2,which is also associated with flavor and anticancer substances in the GSL pathway,resulted in increased abundance of anticancer compounds and decreased pungency of turnip and *** findings revealed that pseudogene differences between turnip and Chiifu influenced the evolution of flavor-associated GSL metabolism-related genes,ultimately resulting in the different flavors of turnip and Chiifu.

关键词： turnip genome comparative genomics pseudogene evolution GSL biosynthesis flavor

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Towards Accurate and Compact Architectures via Neural Architecture Transformer

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第10期44卷 6501-6516页

作者： Guo, Yong Zheng, Yin Tan, Mingkui Chen, Qi Li, Zhipeng Chen, Jian Zhao, Peilin Huang, Junzhou South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Tencent Weixin Grp Shenzhen 518054 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China Tencent Tencent AI Lab Shenzhen 518054 Peoples R China

Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods. However, even a well-designed/searched architecture may still contain many nonsignificant or redundant modules/operations (e.g., some intermediate convolution or pooling layers). Such redundancy may not only incur substantial memory consumption and computational cost but also deteriorate the performance. Thus, it is necessary to optimize the operations inside an architecture to improve the performance without introducing extra computational cost. To this end, we have proposed a Neural Architecture Transformer (NAT) method which casts the optimization problem into a Markov Decision Process (MDP) and seeks to replace the redundant operations with more efficient operations, such as skip or null connection. Note that NAT only considers a small number of possible replacements/transitions and thus comes with a limited search space. As a result, such a small search space may hamper the performance of architecture optimization. To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization. Specifically, we present a two-level transition rule to obtain valid transitions, i.e., allowing operations to have more efficient types (e.g., convolution -> separable convolution) or smaller kernel sizes (e.g., 5x5 -> 3x3). Note that different operations may have different valid transitions. We further propose a Binary-Masked Softmax (BMSoftmax) layer to omit the possible invalid transitions. Last, based on the MDP formulation, we apply policy gradient to learn an optimal policy, which will be used to infer the optimized architectures. Extensive experiments show that the transformed architectures significantly outperform

关键词： Architecture optimization neural architecture search compact architecture design operation transition

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Domain compensatory adversarial networks for partial domain adaptation

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第7期80卷 11255-11272页

作者： Huang, Junchu Zhang, Pengyu Zhou, Zhiheng Fan, Kefeng South China Univ Technol Sch Elect & Informat Engn Guangzhou Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Peoples R China China Elect Standardizat Inst Beijing 100007 Peoples R China

Recently, domain adaptation has stimulated interest in the community of machine learning since it can improve the performance of the model in a new domain (target domain) by borrowing knowledge from a labeled source domain. At the same time, the presence of large-scale labeled datasets also raised significant attention in this scenario: the class labels in the new domain are only a subset of those in the source domain. We propose an adversarial-net-based method in this paper, called domain compensatory adversarial network (DCAN). The critical difficulty of this problem is to reduce the negative impact of source instances with weak discriminability and filter out outlier source classes by exploiting the prior probability of classes. DCAN can identify source instances with weak discriminability and reverse its domain label to compensate for the target label space, which alleviates the negative effect of mismatching label space. Besides, DCAN reweights outlier source classes with the class prior distributions of source data for both category classifier and domain classifier to promote positive transfer. Experiments have revealed the superiority of DCAN compared to the existing methods.

关键词： Domain adaptation Deep learning Image classification

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Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

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

作者： Shenhai Zheng Sian Wen Congyu Li Qing Chen Laquan Li College of Computer Science and Technology Chongqing University of Posts and Telecommunications China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education China School of Science Chongqing University of Posts and Telecommunications China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG.

关键词： Training Image segmentation Breast tumors Annotations Federated learning Feature extraction Skin Lesions Speech processing Biomedical imaging

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Improved Fall-down Detection Algorithm: FE-YOLO

Improved Fall-down Detection Algorithm: FE-YOLO

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Chinese Control and Decision Conference, CCDC

作者： Yuchen Zhang Hongyan Ma Zongyuan Xie School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To address the concerns regarding dataset completeness and detection speed for common fall injuries in the elderly, a comprehensive dataset has been established. Additionally, a lightweight fall detection model named FE-YOLO, which is based on YOLOv8, has been developed. This model replaces the network’s internal feature extraction module with a more efficient Fasternet Block module and introduces an enhanced detection head that utilizes data sharing and grouped convolution techniques. When compared to the YOLOv8n model, the FE-YOLO model demonstrates a reduction in parameters by 43.1%, a decrease in GFLOPs by 51.9%, an improvement in model inference speed by 9.70%, and an increase in FPS by 10.5%. Notably, these enhancements do not significantly impact the average accuracy of the model. The efficiency of the FE-YOLO model renders it suitable for deployment on devices with limited computing power.

关键词： YOLO Convolution Computational modeling Feature extraction Inference algorithms data models Fall detection

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Efficient time series anomaly detection by multiresolution self-supervised discriminative network

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NEUROCOMPUTING 2022年 491卷 261-272页

作者： Huang, Desen Shen, Lifeng Yu, Zhongzhong Zheng, Zhenjing Huang, Min Ma, Qianli South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Hong Kong Univ Sci & Technol Informat Hub Artificial Intelligence Thrust Area Hong Kong Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China

Time series anomaly detection aims to identify abnormal subsequences in time series that are markedly different from the temporal behaviors of the entire sequence. Although previous density-based or proximity-based anomaly detection methods are usually used for anomaly detection, they are still suffering from high computational costs due to the need of traversing the whole training dataset during testing. Recently, reconstruction-based deep learning methods are popular for time series anomaly detection. However, they may not work well because their objective is to recover all information appeared in time series, including high-frequency noises. In this paper, we propose a simple yet efficient method called Multiresolution Self-Supervised Discriminative Network (MS2D-Net) for efficient time series anomaly detection. Specifically, the MS2D-Net includes a multiresolution downsampling module, a feature extraction module, and a self-supervised discrimination module. The multiresolution downsampling module generates some multiresolution samples by downsampling the original time series with different sampling rates and creates different pseudo-labels representing multi-scale behaviors in time series. Then, in the feature extraction module, a shallow convolution network is used to extract temporal dynamics in time series at multiple resolutions. Finally, the self-supervised discrimination module uses the pseudo-labels obtained from the multiresolution downsampling module as the self-supervised information to help separate anomalies from the normal time series samples. Experimental results show that the proposed MS2D-Net can outperform recent strong deep learning baselines on 18 benchmarks for time series anomaly detection with a much lower computational cost. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Self-supervised learning Discriminative network Time series Anomaly detection

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NcoVis: A Visual Analysis Framework for Exploring Academic Collaboration Networks under New Collaborative Relationships 25

NcoVis: A Visual Analysis Framework for Exploring Academic C...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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