检索结果-内蒙古大学图书馆

Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

来源：评论

学校读者我要写书评

暂无评论

Silicon integrated low-loss 4-channel 5-bit optical true-time delay lines 26

Silicon integrated low-loss 4-channel 5-bit optical true-tim...

引用

26th Optoelectronics and Communications Conference, OECC 2021

作者： Liu, Yuanbin Lu, Liangjun Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

ISBN: (纸本)9781557528209

关键词： Timing circuits

来源：评论

学校读者我要写书评

暂无评论

A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Xitong Ma Yuansen Kuang Lin Yuan Cheng Tian Yijie Zeng Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation; 2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily; 3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models.

关键词： knowledge engineering Visualization Terminology Semantics MIMICs Memory management Radiology Feature extraction data models Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Research progress of CDT 8

Research progress of CDT

引用

8th International Conference on Bioinformatics and Biomedical Science, ICBBS 2019

作者： Yuting, Gao Shutong, Shen Sanhong, Deng School of Information Management Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu China

ISBN: (纸本)9781450372510

With the high mortality rate for the cases of malignant tumors, the discovery and early treatment of cancer is critical to improving the 5-year survival rate of cancer. The biggest challenge in control and prevention is how to detect tumors as early as possible, so that the most effective means can be used for treatment. With the help of the WOS database, researchers found that liquid biopsy, genetic material/protein expression and computer imaging play an important role in cancer detection. Through analysis of the history and current status of cancer detection technology development, this paper also aims to provide guidance for the clinical practice of current cancer screening and provide inspiration for future technological development. © 2019 Association for Computing Machinery.

关键词： Gene expression

来源：评论

学校读者我要写书评

暂无评论

The Nord Stream pipeline gas leaks released approximately 220,000 tonnes of methane into the atmosphere

引用

Environmental Science and Ecotechnology 2022年第4期12卷 3-7页

作者： Mengwei Jia Fei Li Yuzhong Zhang Mousong Wu Yingsong Li Shuzhuang Feng Hengmao Wang Huilin Chen Weimin Ju Jun Lin Jianwei Cai Yongguang Zhang Fei Jiang Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjingJiangsu Province210023China Key Laboratory of Coastal Environment and Resources of Zhejiang Province School of EngineeringWestlake UniversityHangzhouZhejiang Province310030China Joint International Research Laboratory of Atmospheric and Earth System Sciences School of Atmospheric SciencesNanjing UniversityNanjingJiangsu Province210023China China Centre for Resources Satellite Data and Application Beijing100094China International Joint Carbon Neutrality Laboratory NanjingJiangsu Province210023China Nantong Academy of Intelligent Sensing NantongJiangsu Province226000China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingJiangsu Province210023China Frontiers Science Center for Critical Earth Material Cycling Nanjing UniversityNanjingJiangsu Province210023China

Sudden mega natural gas leaks of two Nord Stream pipelines in the Baltic Sea(Denmark)occurred from late September to early October 2022,releasing large amounts of methane into the *** inferred the methane emissions of this event based on surface in situ observations using two inversion methods and two meteorological reanalysis datasets,supplemented with satellite-based *** conclude that approximately 220±30 Gg of methane was released from September 26 to October 1,2022.

关键词： methane approximate conclude

来源：评论

学校读者我要写书评

暂无评论

Random-coupled Neural Network

arXiv

引用

arXiv 2024年

作者： Liu, Haoran Liu, Mingzhe Li, Peng Wu, Jiahui Jiang, Xin Zuo, Zhuo Liu, Bingqi The State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu610059 China The Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu University of Technology Chengdu610059 China The School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China The Engineering & Technical College of Chengdu University of Technology Leshan614000 China The Southwestern Institute of Physics Chengdu610225 China The Chengdu University Chengdu610106 China

Improving the efficiency of current neural networks and modeling them in biological neural systems have become popular research directions in recent years. Pulse-coupled neural network (PCNN) is a well applicated model for imitating the computation characteristics of the human brain in computer vision and neural network fields. However, differences between the PCNN and biological neural systems remain: limited neural connection, high computational cost, and lack of stochastic property. In this study, random-coupled neural network (RCNN) is proposed. It overcomes these difficulties in PCNN’s neuromorphic computing via a random inactivation process. This process randomly closes some neural connections in the RCNN model, realized by the random inactivation weight matrix of link input. This releases the computational burden of PCNN, making it affordable to achieve vast neural connections. Furthermore, the image and video processing mechanisms of RCNN are researched. It encodes constant stimuli as periodic spike trains and periodic stimuli as chaotic spike trains, the same as biological neural information encoding characteristics. Finally, the RCNN is applicated to image segmentation, fusion, and pulse shape discrimination subtasks. It is demonstrated to be robust, efficient, and highly anti-noised, with outstanding performance in all applications mentioned above. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

The Study of Jet Formation Mechanism in Fermi Blazars

arXiv

引用

arXiv 2024年

作者： Xie, Shangchun Ouyang, Zhihao Wu, Jingyu Xiao, Hubing Zhang, Shaohua Chen, Yongyun Luo, Zhijian Fan, Junhui Shanghai Key Lab for Astrophysics Shanghai Normal University Shanghai200234 China College of Physics and Electronic Engineering Qujing Normal University Qujing655011 China Center for Astrophysics Guangzhou University Guangzhou510006 China Great Bay Brand Center of the National Astronomical Data Center Guangzhou510006 China Key Laboratory for Astronomical Observation and Technology of Guangzhou Guangzhou510006 China Astronomy Science and Technology Research Laboratory of Department of Education of Guangdong Province Guangzhou510006 China

The origin of jet launching mainly comes from two mechanisms: the Blandford–Znajek (BZ) mechanism and the Blandford–Payne (BP) mechanism. However, it is in debate which one is dominating in blazars. In this work, we used a sample of 937 Fermi blazars to study the jet formation mechanism. We studied the correlation between the jet power and the accretion rate, as well as the comparison between jet power estimated by spectral energy distribution (SED) fitting and that estimated by theoretical formula and radio flux density. Our results suggest that there is no correlation between jet power estimated by SED fitting and the accretion rate for BL Lacertaes (BL Lacs), while a positive and weak correlation exists for flat spectrum radio quasars (FSRQs). Meanwhile, to confirm whether the BP and BZ mechanism is sufficient to launch the jet for FSRQs and BL Lacs, we compare the theoretical jet power with that estimated by SED fitting, as well as that by radio emission. We found that the jet power for most of the two subclasses estimated by SED fitting cannot be explained by either the BP or BZ mechanism. While the jet power for most FSRQs estimated by radio flux density can be explained by the BP mechanism, and most BL Lacs can be explained by the BZ mechanism. We also found that FSRQs have higher accretion rates than BL Lacs, implying different accretion disks around their central black holes: FSRQs typically have standard disks, while BL Lacs usually have advection-dominated accretion flow disks. © 2024, CC BY.

关键词： Radiation flux density

来源：评论

学校读者我要写书评

暂无评论

A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Chenglong Shi Haibin Geng Wenfeng Jiang Jinbao Li Chao Wu Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models.

关键词： Correlation Message passing Semantics Collaboration Logic gates Graph neural networks Topology History data mining Cybernetics

来源：评论

学校读者我要写书评

暂无评论

MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Lin Lu Qingzhi Zou Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Due to the exceptional performance of Transform-ers in 2 $D$ medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%.

关键词： Image segmentation Solid modeling Visualization Three-dimensional displays Current transformers Multilayer perceptrons Convolutional neural networks Kernel Synapses Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Decision-Feedback Single-Layer Read Reconstruction and Separation for Three-dimensional Magnetic Recording

Decision-Feedback Single-Layer Read Reconstruction and Separ...

引用

Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Yanzhe Liao Kezheng Zhang Yugen Jian Shaobing Wang Jincai Chen Ping Lu Ke Luo School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Public Health Hubei University of Medicine Shiyan China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Three-dimensional magnetic recording (3DMR) based on energy-assisted recording techniques by either heat or microwave is a leading storage solution. Separation and detection are impeded by inter-symbol interference, inter-track interference, and inter-layer interference, especially when using traditional tunneling magnetoresistive (TMR) readers. To support a multi-layer magnetic recording system at an recording areal density of 4 Tbits/inch 2 on a dual-layer recording medium, we propose a neural network estimator to reconstruct the top-layer read response signal from the decision feedback of a Viterbi detector. Then we separate the bottom-layer signal by subtracting the reconstructed top-layer signal from the mixed readback signal. For the critical bound of channel bit error rate (BER), our method gains over 7 dB than a multiple-output detecting system.

关键词： Viterbi algorithm Magnetic separation Bit error rate Neural networks Interference Microwave theory and techniques Tunneling magnetoresistance

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：