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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Chongming Ma, Jingyang Li, Songting Zhou, Douglas School of Mathematical Sciences Shanghai Jiao Tong University Shanghai China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Ministry of Education Key Laboratory of Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai China Shanghai Frontier Science Center of Modern Analysis Shanghai Jiao Tong University Shanghai China

Incorporating biological neuronal properties into Artificial Neural Networks (ANNs) to enhance computational capabilities is under active investigation in the field of deep learning. Inspired by recent findings indicating that dendrites adhere to quadratic integration rule for synaptic inputs, this study explores the computational benefits of quadratic neurons. We theoretically demonstrate that quadratic neurons inherently capture correlation within structured data, a feature that grants them superior generalization abilities over traditional neurons. This is substantiated by few-shot learning experiments. Furthermore, we integrate the quadratic rule into Convolutional Neural Networks (CNNs) using a biologically plausible approach, resulting in innovative architectures-Dendritic integration inspired CNNs (Dit-CNNs). Our Dit-CNNs compete favorably with state-of-the-art models across multiple classification benchmarks, e.g., ImageNet-1K, while retaining the simplicity and efficiency of traditional CNNs. All source code are available at https://***/liuchongming1999/Dendritic-integration-inspired-CNN-NeurIPS-2024. © 2024 Neural information processing systems foundation. All rights reserved.

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PEPC: Parallel and Extensible PC Implementation for Causal Structure Learning 23

PEPC: Parallel and Extensible PC Implementation for Causal S...

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7th International Conference on High Performance Compilation, computing and Communications, HP3C 2023

作者： Feng, Yongquan Xu, Liyang Huang, Wanrong Yang, Wenjing Lu, Yutong Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781450399883

Learning the causal relationships among variables from observational data has been a significant problem in statistics and data mining, and one such algorithm is the PC algorithm. However, the existing CPU-based implementations are difficult to support Causal Structure Learning(CSL) on complex observational data due to inefficiency. In this paper, we propose an efficient CPU-based parallel implementation of the PC algorithm, called PEPC. It has an extensible framework, and some optimizations are adopted to reduce the CSL computation of the PC algorithm. Moreover, our PEPC supports efficient multi-thread parallelism. The experimental results demonstrate that PEPC outperforms existing methods in serial efficiency and multi-thread parallelism. In serial tests, our method achieves the maximum speedup ratios of 577.26, 116.74, and 134.68 over three existing approaches, Stable, ***, and Tetrad. In parallel experiments, it achieves 10.66X speedup when running the PC algorithm with 12 threads. © 2023 ACM.

关键词： Learning algorithms

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Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection

arXiv

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arXiv 2024年

作者： Song, Chengyu Ma, Linru Zheng, Jianming Liao, Jinzhi Kuang, Hongyu Yang, Lin Systems Engineering Institute Academy of Military Sciences Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China

Log-based insider threat detection (ITD) detects malicious user activities by auditing log entries. Recently, Large Language Models (LLMs) with strong common sense knowledge are emerging in the domain of ITD. Nevertheless, diverse activity types and overlong log files pose a significant challenge for LLMs to directly discern malicious ones within myriads of normal activities. Furthermore, the faithfulness hallucination issue from LLMs aggravates its application difficulty in ITD, as the generated conclusion may not align with user commands and activity context. In response to these challenges, we introduce Audit-LLM, a multi-agent log-based insider threat detection framework comprising three collaborative agents: (i) the Decomposer agent, breaking down the complex ITD task into manageable sub-tasks using Chain-of-Thought (COT) reasoning;(ii) the Tool Builder agent, creating reusable tools for sub-tasks to overcome context length limitations in LLMs;and (iii) the Executor agent, generating the final detection conclusion by invoking constructed tools. To enhance conclusion accuracy, we propose a pair-wise Evidence-based Multi-agent Debate (EMAD) mechanism, where two independent Executors iteratively refine their conclusions through reasoning exchange to reach a consensus. Comprehensive experiments conducted on three publicly available ITD datasets—CERT r4.2, CERT r5.2, and PicoDomain—demonstrate the superiority of our method over existing baselines and show that the proposed EMAD significantly improves the faithfulness of explanations generated by LLMs. 1 2 Copyright © 2024, The Authors. All rights reserved.

关键词： Reusability

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Automation 5.0: The key to Systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Kun Wang, Lihui Yang, Menglong Xu, Jingwen Cheng, Xinyu Zhang, Jian Zhu, Yuemin Wei, Hongjiang Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity. © 2024 IEEE.

关键词： Image registration

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Reliability-Aware DQN-based Optimization Method of Initial Mapping Solutions in Optical Network-on- Chip (ONoC) 21

Reliability-Aware DQN-based Optimization Method of Initial M...

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21st International Conference on Optical Communications and Networks, ICOCN 2023

作者： Niu, Yuxiang Li, Hui Liu, Feiyang Xidian University State Key Laboratory of Integrated Services Network School of Telecommunications Engineering Xi'an China Hangzhou Institute of Technology Xidian University Hangzhou China Xi'an Aeronautics Computing Technique Research Institute Avic Xi'an China

ISBN: (纸本)9798350343502

Optical Network-on-Chip (ONoC) provides a promising solution for on-chip communication with higher bandwidth and lower latency, compared to traditional Electrical Network-on-Chip (ENoC). However, the reliability challenges (such as insertion loss and crosstalk) exist in ONoC. As for this issue, reliability-aware mapping can be considered. This work proposes a reliability-aware DQN-based optimization method of initial mapping solutions in ONoC, to generate better initial mapping solutions. This optimization method of initial mapping solutions can be combined with existing mapping algorithms, further improving the mapping performance. The evaluation results show that for a given core graph and network topology, our proposed method generates better solutions compared to the traditional random method. Also, the proposed method is helpful for finding the optimal mapping solution more efficiently when combined with the existing mapping algorithms. © 2023 IEEE.

关键词： Reliability

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Biomedical polymers: synthesis, properties, and applications

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Science China Chemistry 2022年第6期65卷 1010-1075页

作者： Wei-Hai Chen Qi-Wen Chen Qian Chen Chunyan Cui Shun Duan Yongyuan Kang Yang Liu Yun Liu Wali Muhammad Shiqun Shao Chengqiang Tang Jinqiang Wang Lei Wang Meng-Hua Xiong Lichen Yin Kuo Zhang Zhanzhan Zhang Xu Zhen Jun Feng Changyou Gao Zhen Gu Chaoliang He Jian Ji Xiqun Jiang Wenguang Liu Zhuang Liu Huisheng Peng Youqing Shen Linqi Shi Xuemei Sun Hao Wang Jun Wang Haihua Xiao Fu-Jian Xu Zhiyuan Zhong Xian-Zheng Zhang Xuesi Chen Key Laboratory of Biomedical Polymers of Ministry of Education&Department of Chemistry Wuhan UniversityWuhan430072China Institute of Functional Nano&Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&DevicesSoochow UniversitySuzhou215123China School of Materials Science and Engineering Tianjin Key Laboratory of Composite and Functional MaterialsTianjin UniversityTianjin300350China Key Laboratory of Biomedical Materials of Natural Macromolecules(Beijing University of Chemical Technology) Ministry of EducationBeijing Laboratory of Biomedical MaterialsCollege of Materials Science and EngineeringBeijing University of Chemical TechnologyBeijing100029China MOE Key Laboratory of Macromolecular Synthesis and Functionalization Department of Polymer Science and EngineeringZhejiang UniversityHangzhou310027China State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Functional Polymer Materials of Ministry of EducationCollege of ChemistryNankai UniversityTianjin300071China Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province College of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China Jinhua Institute of Zhejiang University Jinhua321299China Zhejiang Key Laboratory of Smart BioMaterials and Center for Bionanoengineering College of Chemical and Biological EngineeringZhejiang UniversityHangzhou310027China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou311215China State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular Science and Laboratory of Advanced MaterialsFudan UniversityShanghai200438China CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety CAS Center for Excellence in Nano-scienceNational Center for Nanoscience and Technology(NCNST)Beijing100190China School of Biomedical Sciences and Engineering Guangzhou International CampusSouth China University of TechnologyGuangzhou510006China Institute of Functional Nano and So

Biomedical polymers have been extensively developed for promising applications in a lot of biomedical fields, such as therapeutic medicine delivery, disease detection and diagnosis, biosensing, regenerative medicine, and disease treatment. In this review, we summarize the most recent advances in the synthesis and application of biomedical polymers, and discuss the comprehensive understanding of their property-function relationship for corresponding biomedical applications. In particular, a few burgeoning bioactive polymers, such as peptide/biomembrane/microorganism/cell-based biomedical polymers, are also introduced and highlighted as the emerging biomaterials for cancer precision therapy. Furthermore, the foreseeable challenges and outlook of the development of more efficient, healthier and safer biomedical polymers are discussed. We wish this systemic and comprehensive review on highlighting frontier progress of biomedical polymers could inspire and promote new breakthrough in fundamental research and clinical translation.

关键词： biomedical polymer nanomedicine cancer therapy drug delivery bioimaging and biosensing tissue engineering regenerative medicine cytotoxicity biocompatibility

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Application of heat-activated peroxydisulfate process for the chemical cleaning of fouled ultrafiltration membranes

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Chinese Chemical Letters 2023年第10期34卷 116-120页

作者： Jiaqi Ding Haoliang Xiao Xiaolong Huang Yujia Zou Zhimin Ye Songlin Wang Pengchao Xie Yongsheng Chen Jun Ma Yangtze River Basin Ecological Environment Monitoring and Scientific Research Center Yangtze River Basin Ecological Environment Supervision and Administration BureauMinistry of Ecological EnvironmentWuhan 430010China School of Environmental Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Water&Wastewater Treatment(HUST) MOHURDWuhan 430074China School of Civil and Environmental Engineering Georgia Institute of TechnologyAtlantaGA 30332United States State Key Laboratory of Urban Water Resource and Environment Harbin Institute of TechnologyHarbin 150090China

Na Cl O has been widely used to restore membrane flux in practical membrane cleaning processes,which would induce the formation of toxic halogenated *** this study,we proposed a novel heatactivated peroxydisulfate(heat/PDS)process to clean the membrane fouling derived from humic acid(HA).The results show that the combination of heat and PDS can achieve almost 100%recovery of permeate flux after soaking the HA-fouled membrane in 1 mmol/L PDS solution at 50℃ for 2 h,which is attributed to the changes of HA structure and enhanced detachment of foulants from *** properties of different treated membranes are characterized by scanning electron microscopy(SEM),atomic force microscope(AFM),attenuated total reflection Fourier transform infrared spectroscopy(ATRFTIR),and X-ray photoelectron spectroscopy(XPS),demonstrating that the reversible and irreversible foulants could be effectively removed by heat/PDS *** filtration process and fouling mechanism of the cleaned membrane were close to that of the virgin membrane,illustrating the good reusability of the cleaned ***,heat/PDS which can avoid the generation of halogenated byproducts shows comparable performance to Na Cl O on membrane cleaning and high performance for the removal of fouling caused by sodium alginate(SA),HA-bovine serum albumin(BSA)-SA mixture and algae,further suggesting that heat/PDS would be a potential alternative for membrane cleaning in practical application.

关键词： Chemical cleaning Peroxydisulfate Heat Membrane fouling Ultrafiltration

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Efficient Certificateless Authenticated key Agreement for Blockchain-Enabled Internet of Medical Things

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Computers, Materials & Continua 2023年第4期75卷 2043-2059页

作者： Chaoyang Li Yanbu Guo Mianxiong Dong Gang Xu Xiu-Bo Chen Jian Li Kaoru Ota College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450002China Department of Sciences and Informatics Muroran Institution of TechnologyMuroran050-8585Japan School of Computing Science and Technology North China University of TechnologyBeijing100144China Information Security Centre State Key Laboratory of Networking and Switching TechnologyBeijing University of Post and TelecommunicationsBeijing100876China

Internet of Medical Things(IoMT)plays an essential role in collecting and managing personal medical *** recent years,blockchain technology has put power in traditional IoMT systems for data sharing between different medical institutions and improved the utilization of medical ***,some problems in the information transfer process between wireless medical devices and mobile medical apps,such as information leakage and privacy *** paper first designs a cross-device key agreement model for blockchain-enabled *** model can establish a key agreement mechanism for secure medical data ***,a certificateless authenticated key agreement(KA)protocol has been proposed to strengthen the information transfer security in the cross-device key agreement *** proposed KA protocol only requires one exchange of messages between the two parties,which can improve the protocol execution ***,any unauthorized tampering of the transmitted signed message sent by the sender can be detected by the receiver,so this can guarantee the success of the establishment of a session key between the strange *** blockchain ledger can ensure that the medical data cannot be tampered with,and the certificateless mechanism can weaken the key escrow ***,the security proof and performance analysis are given,which show that the proposed model and KA protocol are more secure and efficient than other schemes in similar literature.

关键词： Certificateless key agreement authentication blockchain internet of medical things

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Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge computing 19

Energy-Efficient and Reliable Federated Learning in Heteroge...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xu, Yan Li, Liying Cong, Peijin Zhou, Junlong Nanjing University of Science and Technology School of Computer Science and Engineering China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture China

ISBN: (纸本)9798350346558

As a decentralized machine learning paradigm, Federated Learning (FL) is an emerging technique to protect user data privacy in Mobile Edge computing (MEC). FL adopts the idea of distributed privacy computing, enabling the terminal devices to train machine learning models required by servers locally and upload model parameters to servers for aggregation. However, in the process of coupling FL with MEC systems, device heterogeneity, malicious use of poisoned data by users and limited system resources will seriously affect FL training accuracy, system reliability and energy efficiency. In this paper, a highly reliable and energy-efficient FL scheme is designed to solve these issues. Specifically, we first propose a calculation method for estimating the reliability of heterogeneous devices. Then, we design a highly reliable device selection scheme based on devices' reliability, computing power, and participation times. Finally, we utilize the slack time in FL training to dynamically adjust the voltage and frequency of devices for reducing the energy consumption of training. Experimental results show that our proposed scheme improves the accuracy by up to 36.45% on average and saves energy by up to 48.63% when compared to the baseline and benchmarking methods. © 2022 IEEE.

关键词： Reliability

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