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PROM: A persistent routing optimization method based on supervised learning

Journal of Network and Computer Applications 2025年 242卷

作者： Yingya Guo Zebo Huang Mingjie Ding Bin Lin Huan Luo College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China

Traditional Traffic engineering (TE) typically improves network performance by formulating and solving routing optimization problems based on measured or predicted Traffic Matrices (TMs). However, since network traffic is dynamic, routing strategies optimized for the current measured TMs may not be suitable for future traffic patterns. To adapt to dynamic traffic, one possible solution is to perform more frequent routing updates, but this may lead to service disruptions. Another approach is the two-step method of prediction followed by optimization, which could lead to degraded performance of routing strategies due to inaccuracies in traffic prediction. Additionally, existing approaches focus solely on either distributed or centralized networks, lacking flexibility to adapt to different network architectures. To address these challenges, we propose PROM, a Machine Learning (ML)-based routing optimization model that directly predicts an effective routing strategy in an end-to-end manner based on historical traffic. This routing strategy is applicable across multiple future TMs, thereby reducing the need for frequent routing updates. Furthermore, PROM can be easily extended from a centralized implementation to a distributed one, adapting to different network architectures. To enhance model performance, we have also designed a custom loss function for routing optimization scenarios to avoid overfitting. Extensive simulation results demonstrate that PROM can predict high-quality routing strategies in future unknown traffic scenarios and is adaptable to both centralized and distributed network architectures.

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knowledge Graph for China’s Genealogy

Knowledge Graph for China’s Genealogy

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IEEE International Conference on big knowledge (ICBK)

作者： Xindong Wu Tingting Jiang Yi Zhu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Information Engineering Yangzhou University Yangzhou China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical roots, and explore the origins of a family more easily. However, the multi-type, multisource dynamic changes and specialized nature of genealogical data bring challenges to the development of contemporary knowledge graph models. Applying existing methods to genealogical data can result in problems of overlooking certain specialized vocabulary and dynamic properties such as personal experiences. In this paper, we propose a genealogical knowledge graph model GKGM that combines HAO intelligence (h uman intelligence + a rtificial intelligence + o rganizational intelligence) and ontology granularity division technology to address the above problems. Furthermore, a method of applying the model to construct genealogical knowledge graphs is demonstrated, and an experiment conducted on a real-world genealogical dataset verifies the feasibility and effectiveness of the model.

关键词： Ontologies data models Blood data mining Biological system modeling History

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Draw knowledge from the Learning Process for Few-Shot Learning

SSRN

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SSRN 2022年

作者： Zhang, Yi Huang, Sheng School of Big data and Software Engineering Chongqing University No.55 Daxuecheng South Rd. Shapingba Chongqing401331 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University No.174 Shazhengjie Shapingba Chongqing400044 China

Pre-training followed by fine-tuning is an effective method for few-shot learning (FSL) and has achieved surprising performance in a more straightforward manner. However, most existing methods pay little attention to the feature learning part in the pre-training stage and neglect all the learned knowledge except the model weights in the learning process. In this work, we propose to Draw knowledge from the Learning Process (DKLP), which fully exploits the information in hierarchical and temporal dimensions within the learning process to improve the discrimination and generalization of FSL. Specifically, Self-Cycle Feature Refinement (SCFR) is designed to cyclically optimize the model through backward refinement and forward transmission of intermediate features layer by layer for exploring the hierarchical knowledge of the learning process. To gain the temporal knowledge of the learning process, a Logit Exponential Moving Average (LEMA) module is introduced to leverage soft labels generated by the output logits in the past epochs to guide model training and then improve the generalization ability of the model. LEMA updates soft labels for each sample in an exponential moving average manner, which enables improving the robustness of the model to the noise labels. Extensive experimental results on four popular FSL benchmarks show that our approach achieves a promising performance compared with recent FSL methods and is robust against the different percentages of noise labels. © 2022, The Authors. All rights reserved.

关键词： Distillation

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Leveraging Herpangina data to Enhance Hospital-level Prediction of Hand-Foot-and-Mouth Disease Admissions Using UPTST

arXiv

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arXiv 2023年

作者： Yu, Guoqi Yao, Hailun Zheng, Huan Xu, Ximing The Big Data Center for Children’s Medical Care Children’s Hospital Chongqing Medical University Chongqing401122 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China The National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders Chongqing401122 China The Department of Orthopedics Ministry of Education Key Laboratory of Child Development and Disorders National Clinical Research Center for Child Health and Disorders China International Science and Technology Cooperation Base of Child Development and Critical Disorders Chongqing Key Laboratory of Pediatrics Children’s Hospital Chongqing Medical University Chongqing400014 China The School of Finance Nankai University Tianjin300350 China The Big Data Center for Children’s Medical Care Children’s Hospital Chongqing Medical University Chongqing401122 China

Outbreaks of hand-foot-and-mouth disease (HFMD) have been associated with significant morbidity and, in severe cases, mortality. Accurate forecasting of daily admissions of pediatric HFMD patients i1s therefore crucial for aiding the hospital in preparing for potential outbreaks and mitigating nosocomial transmissions. To address this pressing need, we propose a novel transformer-based model with a U-net shape, utilizing the patching strategy and the joint prediction strategy that capitalizes on insights from herpangina, a disease closely correlated with HFMD. This model also integrates representation learning by introducing reconstruction loss as an auxiliary loss. The results show that our U-net Patching Time Series Transformer (UPTST) model outperforms existing approaches in both long- and short-term prediction accuracy of HFMD at hospital-level. Furthermore, the exploratory extension experiments show that the model's capabilities extend beyond prediction of infectious disease, suggesting broader applicability in various domains. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial Network with Explainable AI System for NIR-II Fluorescence Molecular Imaging

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

引用

IEEE International Symposium on Biomedical Imaging

作者： Lidan Fu Binchun Lu Lingbing Li Xiaojing Shi Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000–1300 nm). NIR-IIb (1500–1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice.

关键词： Training Image resolution Explainable AI Statistical analysis Noise Imaging Fluorescence Generative adversarial networks Real-time systems Probes

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UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Liu, Saichao Sun, Geng Li, Jiahui Liang, Shuang Wu, Qingqing Wang, Pengfei Niyato, Dusit The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Information Science and Technology Northeast Normal University Changchun130117 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Dalian University of Technology Dalian116024 China

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Regeneration and functional recovery of the completely transected optic nerve in adult rats by CNTF-chitosan

引用

Signal Transduction and Targeted Therapy 2023年第3期8卷 912-915页

作者： Xiao Liu Fei Hao Peng Hao Jingxue Zhang Liqiang Wang Si-Wei You Ningli Wang Zhaoyang Yang Kwok-Fai So Xiaoguang Li Department of Neurobiology School of Basic Medical SciencesCapital Medical University100069 BeijingChina School of Engineering Medicine Beijing Key Laboratory for Biomaterials and Neural RegenerationBeihang University100083 BeijingChina Beijing Tongren Eye Center Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key Laboratory100005 BeijingChina Department of Ophthalmology The Third Medical CenterChinese PLA General Hospital100089 BeijingChina Department of Ophthalmology Xijing HospitalThe Fourth Military Medical University710032 Xi’anShanxi ProvinceChina Guangdong-Hongkong-Macao Institute of CNS Regeneration Ministry of Education CNS Regeneration Collaborative Joint LaboratoryJinan University510632 GuangzhouGuangdong ProvinceChina Bioland Laboratory(Guangzhou Regenerative Medicine and Health Guangdong Laboratory) 510530 GuangzhouGuangdong ProvinceChina Department of Ophthalmology and State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong999077 Hong KongChina Center for Brain Science and Brain-Inspired Intelligence Guangdong-Hong Kong-Macao Greater Bay Area510515 GuangzhouGuangdong ProvinceChina Co-innovation Center of Neuroregeneration Nantong University226001 NantongJiangsu ProvinceChina Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration Beijing Advanced Innovation Center for Big Data-Based Precision MedicineBeihang University100083 BeijingChina

Dear Editor,The optic nerve,which belongs to the central nervous system(CNS),cannot regenerate when injured in adult mammals.1 Up to now,no readily translatable measures are available for repairing a severely injured optic *** we demonstrated that ciliary neurotrophic factor(CNTF)-chitosan enabled the reconstruction and functional recovery of the adult rat visual system,thus shedding light on the clinical potential for repairing the severely injured optic nerve.

关键词： Regeneration functional CNTF

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Semi-supervised Long-tail Endoscopic Image Classification

引用

Chinese Medical Sciences Journal 2022年第3期37卷 171-180,I0002页

作者： Runnan Cao Mengjie Fang Hailing Li Jie Tian Di Dong School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Advaneed Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'an 710126China

Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited *** We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse ***-supervised learning algorithm FixMatch was applied based on consistency regularization and *** splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited *** The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall *** algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,*** a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average *** analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail *** SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume ***,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.

关键词： endoscopic image artificial intelligence semi-supervised learning long-tail distribution image classification

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Effect of hydrothermal conditions on crystal structure, morphology and visible-light driven photocatalysis of WO3 nanostructures 20th

Effect of hydrothermal conditions on crystal structure, morp...

引用

20th Chinese Materials Conference, CMC 2019

作者： Liu, Yangsi Xi, Xiaoli Nie, Zuoren Zhao, Linyan Fan, Youshu College of Materials Science and Engineering Key Laboratory of Advanced Functional Materials Education Ministry of China Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783035715668

Tungsten trioxide (WO3) nanostructures were synthesized by a hydrothermal method, and the influence of essential hydrothermal conditions, temperature and time, on their crystal structure, morphology and visible-light driven photocatalysis was studied. The hydrothermal temperature was varied from 120 °C to 200 °C, and the hydrothermal time changed from 12 h to 32 h. The crystal structure, morphology and photocatalytic performance of WO3 nanostructures were characterized by XRD, SEM and UV-Vis. The crystal structure of WO3 nanostructure was triclinic phase and their morphology was mainly one dimensional nanorods. Methylene blue was used as the target to evaluate their photocatalytic performance under visible light (λ>420 nm). The photocatalytic results suggest the suitable hydrothermal conditions to synthesize WO3 nanostructures for the wastewater treatment application. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Crystal structure

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Effect of Protonation and Deprotonation on Oxygen-Containing Groups Functionalized Graphene for Boron Adsorption Removal

SSRN

引用

SSRN 2024年

作者： Yao, Jingjing Zhang, Shidong Li, Dong-Sheng Wang, Ye An, Wengang Yang, Hui Ying Pillar of Engineering Product Development Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Institute of Advanced Optoelectronic Materials and Technology College of Big Data and Information Engineering Guizhou University Guiyang550025 China Singapore University of Technology and Design [SUTD Singapore Central South University China Key Laboratory of Material Physics Ministry of Education School of Physics and Microelectronics Zhengzhou University Zhengzhou450052 China College of New Energy and Environment Jilin University Changchun130021 China

This study investigated the adsorption mechanisms of B(OH)3 (B3) and B(OH)4- (B4) on protonated and deprotonated graphene with oxygen-containing functional groups. Protonation or deprotonation conditions were found to significantly influence boron adsorption. Deprotonation of hydroxyl-modified graphene with 20 carbon rings (G20-OH) and carboxyl-modified graphene with 20 carbon rings (G20-COOH) led to decreased adsorption capacities for both B3 and B4. Protonation of G20-OH enhanced B3 adsorption. The adsorption interface, mainly around hydrogen atoms associated with protonated groups, exhibited a sharp gradient, likely due to increased electron density from O-H bond formation. B3 showed stronger electron sharing, emphasizing robust interactions. Hydrogen bonding dominated in G20-OH2-B3, while G20-O-B4 exhibited more substantial hydrogen bond interactions. In summary, hydrogen bond strength played a crucial role in B3 adsorption, while electrostatic repulsion hindered B4 adsorption on deprotonated graphene. This highlights the importance of graphene protonated and deprotonated states in effectively removing various pKa contaminants. © 2024, The Authors. All rights reserved.

关键词： Density functional theory

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