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2024 Photonic networks and Devices, networkS 2024

作者： Gao, Shoufei Sun, Yizhi Chen, Hao Ge, Dawei Wang, Dong Zhang, Dechao Li, Han Ding, Wei Wang, Yingying Co. Ltd. Jiangsu226010 China Guangdong Provincial Key Laboratory of Optical Fibre Sensing and Communication Institute of Photonics Technology Jinan University Guangzhou510632 China College of Physics & Optoelectronic Engineering Jinan University Guangzhou510632 China Department of Fundamental Network Technology China Mobile Research Institute Beijing100053 China

We report the simultaneous achievement of ultralow loss in fundamental mode (0.1-0.2 dB/km) and high loss in higher order modes (db/m level) with an extinction ratio reaching 26000 in a novel four-fold truncated DNANF... 详细信息

关键词： Single mode fibers

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HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

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

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun 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 Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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An Efficient and Privacy-Preserving Data Aggregation Scheme Supporting Arbitrary Statistical Functions in IoT

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China Communications 2022年第6期19卷 91-104页

作者： Haihui Liu Jianwei Chen Liwei Lin Ayong Ye Chuan Huang College of Computer and Cyber Security Fujian Normal UniversityFuzhou 350117China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal UniversityFuzhou 350117China School of Computer Science and Mathematics Fujian University of TechnologyFuzhou 350118China Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhou 350117China

The Internet of Things(IoT)has profoundly impacted our lives and has greatly revolutionized our *** terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent ***,the high frequency of collecting user data will raise people concerns about personal *** recent years,many privacy-preserving data aggregation schemes have been ***,most existing schemes cannot support either arbitrary aggregation functions,or dynamic user group management,or fault *** this paper,we propose an efficient and privacy-preserving data aggregation *** the scheme,we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location *** this basis,the proposed scheme supports not only arbitrary aggregation functions,but also flexible dynamic user ***,the scheme achieves faulttolerant capabilities by utilizing a future data buffering *** analysis reveals that the scheme can achieve the desired security properties,and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

关键词： Internet of Things data aggregation privacy protection arbitrary aggregation functions

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems 46

Intra-Guided Dual-Energy Material Decomposition Method for D...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhong, Xinyun Lyu, Tianling Wu, Zhan Gao, Wei Xi, Yan Zhao, Wei Zhu, Wentao Chen, Yang Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Southeast University School of Computer Science and Engineering Laboratory of Image Science and Technology Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps. © 2024 IEEE.

关键词： Image reconstruction

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Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

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Nuclear Science and Techniques 2022年第3期33卷 62-76页

作者： Kai Chen Li-Bo Zhang Jia-Shun Liu Yuan Gao Zhan Wu Hai-Chen Zhu Chang-Ping Du Xiao-Li Mai Chun-Feng Yang Yang Chen Southeast University Nanjing 210096China School of Cyber Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 210096China Department of Radiology General Hospital of the Northern Theater of the Chinese People’s Liberation ArmyShenyang 110016China Laboratory of Image Science and Technology The School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China Department of Radiology Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjing 210008China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of RadiologyZhongda HospitalSoutheast UniversityNanjing 210009China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China

Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke *** widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion *** the dose of X-rays causes severe noise and artifacts in PCT *** solve this problem,we propose a deep learning method called *** exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into *** decomposes the convolved features into high-and low-frequency *** decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image *** Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent *** extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact *** and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.

关键词： Cerebral perfusion CT Low-dose Image denoising Perfusion parameters

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The Immense Impact of Reverse Edges on Large Hierarchical networks

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Engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer 14

Parallel Optimization of Computational Fluid Dynamics Applic...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Liu, Anjun Liao, Yin Xu, Xihao Chen, YanYan Zhong, Hongjun Wu, Jianming Kong, Bo Raikov, Alexander Guo, Meng Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Industrial Software Jinan China Zhejiang Yuansuan Technology Co. Ltd Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Hangzhou China Qilu University of Technology Shandong Academy of Sciences Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security China Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Shandong Jinan China

ISBN: (纸本)9798350371024

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1-2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry. © 2023 IEEE.

关键词： Supercomputers

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Resource Allocation for MEC in Ultra-dense networks

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Journal of computers (Taiwan) 2025年第1期36卷 143-162页

作者： Ma, Huan Li, Shuyun Wang, Zhihui Dou, Xubing Zhang, Xinchang 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 Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China

In the complex context of 5G ultra-dense networks, the unprecedented growth in traffic and computing demand has made the interaction between edge devices and edge servers extremely intricate due to the time-varying nature of the network environment and resource heterogeneity. The combination of UDN and Mobile Edge Computing not only significantly improves network performance but also expands application scenarios and enhances user experience, marking it as one of the crucial trends in future network development. In this paper, we review recent results and research progress on MEC-based resource allocation in 5G ultra-dense networks. We analyze resource allocation strategies from multiple dimensions, including user association, interference management, energy efficiency optimization, delay reduction, access selection, and user mobility. Finally, we discuss the challenges and open issues that need to be addressed for future development in this area. © 2025 Codon Publications. All rights reserved.

关键词： 5G mobile communication systems

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Towards Public Integrity Audition for Cloud-IoT Data Based on Blockchain

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computer Systems Science & Engineering 2022年第6期41卷 1129-1142页

作者： Hao Yan Yanan Liu Shuo Qiu Shengzhou Hu Weijian Zhang Jinyue Xia School of Network Security Jinling Institute of TechnologyNanjing211169China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal UniversityFuzhou350007China Department of Mathematics and Computer Science Gannan Normal UniversityGanzhou341000China Network Security and Information Office Hohai UniversityNanjing210098China International Business Machines Corporation(IBM) New YorkNYUSA

With the rapidly developing of Internet of Things (IoT), the volume ofdata generated by IoT systems is increasing quickly. To release the pressure ofdata management and storage, more and more enterprises and individuals preferto integrate cloud service with IoT systems, in which the IoT data can be outsourced to cloud server. Since cloud service provider (CSP) is not fully trusted,a variety of methods have been proposed to deal with the problem of data integritychecking. In traditional data integrity audition schemes, the task of data auditing isusually performed by Third Party Auditor (TPA) which is assumed to be ***, in real-life TPA is not trusted as people thought. Therefore, theseschemes suffer from the underlying problem of single-point failure. Moreover,most of the traditional schemes are designed by RSA or bilinear map techniqueswhich consume heavy computation and communication cost. To overcome theseshortcomings, we propose a novel data integrity checking scheme for cloud-IoTdata based on blockchain technique and homomorphic hash. In our scheme, thetags of all data blocks are computed by a homomorphic hash function and storedin blockchain. Moreover, each step within the process of data integrity checking issigned by the performer, and the signatures are stored in blockchain through smartcontracts. As a result, each behavior for data integrity checking in our scheme canbe traced and audited which improves the security of the scheme greatly. Furthermore, batch-audition for multiple data challenges is also supported in our *** formalize the system model of our scheme and give the concrete *** performance analyses demonstrate that our proposed scheme is efficientand practical without the trust-assumption of TPA.

关键词： Blockchain cloud-IoT data integrity checking homomorphic hash function batch audition

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