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Optimizing network resource allocation with graph pointer neural network in large-scale AI systems

Digital Communications and Networks 2024年

作者： Peiying Zhang Yilin Li Athanasios V. Vasilakos Konstantin Igorevich Kostromitin Jianyong Zhang Mohsen Guizani Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710071 China 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 250013 China Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Physics of Nanoscale Systems South Ural State University Chelyabinsk 454080 the Russian Federation Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Key Lab of All Optical Network and Advanced Telecommunication of EMC Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi 999041 United Arab Emirates

For increasingly complex communication demands of large-scale AI communication systems, the Space-Air-Ground Integrated Network (SAGIN) better caters to demands but also raises concerns about resource scarcity and diversity. This paper innovatively combines Graph Pointer Neural Networks (GPNN) and Reinforcement Learning (RL) to enhance resource allocation efficiency. The method leverages the advantages of GPNN in handling graph data and RL in optimizing decisions in dynamic environments. It also targets the optimization goal of maximizing resource allocation while minimizing deployment latency. This paper begins by modeling SAGIN and elucidating the SAGIN logical architecture based on Software-defined Networking (SDN). Subsequently, it introduces an SFC deployment algorithm aimed at joint optimization of resource allocation and latency. The algorithm leverages GPNN and RL to deploy virtual nodes and links, with the goal of optimizing resource allocation and deployment latency. Experiment findings conclusively demonstrate that the efficacy of proposed algorithm in effectively weighing limited heterogeneous resources and minimum mapping delay. Notably, when compared to three other SFC mapping algorithms MLRL, NFVdeep, and RL, the proposed algorithm consistently outperforms them, with an average improvement of 10.17% in long-term average reward/cost, 11.21% in link resource utilization ratio, 15.34% in node resource utilization ratio, and 16.38% in acceptance ratio.

关键词： Space-air-ground integrated network Topological structure Reinforcement learning Service function chain Graph pointer neural network

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Author Correction: Sub-femtonewton force sensing in solution by super-resolved photonic force microscopy

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Nature Photonics 2024年 998页

作者： Xuchen Shan Dajing Wang Jinlong Shi Hongyan Zhu Xiaolan Zhong Baolei Liu Weichang Hao Fan Wang Lei Ding Qian Peter Su Shihui Wen Dayong Jin Lei Jiang Chaohao Chen Yang Wang Zhaocun Huang Lingqian Chang Shen S. J. Wang Peter John Reece Wei Ren Xunyu Lu Jie Lu Lingdong Sun School of Physics Beihang University Beijing People’s Republic of China Science and Technology Center for Quantum Biology National Institute of Extremely-Weak Magnetic Field Infrastructure Hangzhou People’s Republic of China School of Biomedical Engineering Faculty of Engineering and Information Technology University of Technology Sydney Sydney New South Wales Australia Centre for Atomaterials and Nanomanufacturing School of Science RMIT University Melbourne Victoria Australia Institute for Biomedical Materials and Devices (IBMD) School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney Sydney New South Wales Australia Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo People’s Republic of China Key Laboratory of Bio-Inspired Materials and Interfaces Sciences Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing People’s Republic of China Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials Engineering Research School of Physics The Australian National University Canberra Australian Capital Territory Australia Key Laboratory of Biomechanics and Mechanobiology (Ministry of Education) Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing People’s Republic of China Computing and Information Systems Department Melbourne School of Engineering University of Melbourne Melbourne Victoria Australia School of Physics The University of New South Wales Sydney New South Wales Australia School of Chemistry & Chemical Engineering Shaanxi Normal University Xi’an People’s Republic of China Particles and Catalysis Research Laboratory School of Chemical Engineering The University of New South Wales Sydney New South Wales Australia Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology

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Inequality-Type Zhang Equivalency Originating from Neural Dynamics for Time-Varying Problems Solving

Inequality-Type Zhang Equivalency Originating from Neural Dy...

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IEEE International Conference on Mechatronics and Automation

作者： Yunong Zhang Liangjie Ming Min Yang Binbin Qiu Zhonghua Li School of Data and Computer Science Sun Yat-sen University Guangzhou P. R. China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen P. R. China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou P. R. China School of Electronics and Information Technology Sun Yat-sen University Guangzhou P. R. China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou P. R. China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a concept of Zhang equivalency (ZE) is presented, and its type of inequality to inequality (simply termed, inequality type) is further discussed. By applying Zhang neural dynamics method, we thus propose the order-1, order2, and order-3 ZE general formulas of such an inequality type, respectively. Then, the inequalities in four different situations are investigated, i.e., the robot situation, matrix situation (including matrix-vector situation), state-control situation, and state situation. We propose the corresponding order-1, order-2, and order-3 ZE formulas to prepare for solving corresponding time-varying inequality-related problems.

关键词： Mechatronics Automation Conferences Linear matrix inequalities Problem-solving Robots

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Linear operators in information retrieval

Linear operators in information retrieval

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6th Symposium on Languages, Applications and Technologies, SLATE 2017

作者： Hattab, Hawete Mbarek, Rabeb Umm Al-qura University Al-Jumum University College Department of Mathematics Makkah Saudi Arabia Sfax University Multimedia Information Systems and Advanced Computing Laboratory Sfax Tunisia

ISBN: (纸本)9783959770569

In this paper, we propose a pseudo-relevance feedback approach based on linear operators: vector space basis change and cross product. The aim of pseudo-relevance feedback methods based on vector space basis change IBM (Ideal Basis Method) is to optimally separate relevant and irrelevant documents. Whereas the aim of pseudo-relevance feedback method based on cross product AI (Absorption of irrelevance) is to e ectively exploit irrelevant documents. We show how to combine IBM methods with AI methods. The combination methods IBM+AI are evaluated experimentally on two TREC collections (TREC-7 ad hoc and TREC-8 ad hoc). The experiments show that these methods improve previous works. © Hawete Hattab and Rabeb Mbarek

关键词： Vector spaces

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Skin Lesion Segmentation Using Multiple Density Clustering Algorithm MDCUT And Region Growing

Skin Lesion Segmentation Using Multiple Density Clustering A...

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International Conference on Computer and information Science (ACIS)

作者： Soumaya Louhichi Mariem Gzara Hanêne Ben Abdallah MIRACL: Multimedia InforRmation systems and Advanced Computing Laboratory Sfax Tunisia High School of Computer Science and Mathematics at the University of Monastir Monastir Tunisia King Abdulaziz University Jeddah KSA

Skin lesion segmentation is a key step in a diagnosis system based on dermoscopic images. This paper proposes a method to detect the skin lesion accurately. The images are first cleansed to remove noise. Then, pertinent features are extracted from RGB, HSV and XYZ color spaces. Cluster analysis is used for segmentation. We take advantage of the multiple density clustering algorithm MDCUT [1] to solve the problem of image segmentation using region growing. We demonstrate how MDCUT algorithm is used to automatically determine the needed parameters for region growing image segmentation. Experiments on medical skin lesion image and comparison with the ground truth segmentation results demonstrate the validity of our method.

关键词： Image segmentation Clustering algorithms Lesions Image color analysis Skin Feature extraction Shape

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GPU PaaS computation model in aneka cloud computing environments

arXiv

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arXiv 2018年

作者： Ilager, Shashikant Wankar, Rajeev Kune, Raghavendra Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia School of Computer and Information Sciences University of Hyderabad Hyderabad India Advanced Data Processing Research Institute Department of Space Secunderabad India

Due to the surge in the volume of data generated and rapid advancement in Artificial Intelligence (AI) techniques like machine learning and deep learning, the existing traditional computing models have become inadequate to process an enormous volume of data and the complex application logic for extracting intrinsic information. computing accelerators such as Graphics processing units (GPUs) have become de facto SIMD computing system for many big data and machine learning applications. On the other hand, the traditional computing model has gradually switched from conventional ownership-based computing to subscription-based cloud computing model. However, the lack of programming models and frameworks to develop cloud-native applications in a seamless manner to utilize both CPU and GPU resources in the cloud has become a bottleneck for rapid application development. To support this application demand for simultaneous heterogeneous resource usage, programming models and new frameworks are needed to manage the underlying resources effectively. Aneka is emerged as a popular PaaS computing model for the development of Cloud applications using multiple programming models like Thread, Task, and MapReduce in a single *** platform. Since, Aneka addresses MIMD application development that uses CPU based resources and GPU programming like CUDA is designed for SIMD application development, here, the chapter discusses GPU PaaS computing model for Aneka Clouds for rapid cloud application development *** platforms. The popular opensource GPU libraries are utilized and integrated it into the existing Aneka task programming model. The scheduling policies are extended that automatically identify GPU machines and schedule respective tasks accordingly. A case study on image processing is discussed to demonstrate the system, which has been built using PaaS Aneka SDKs and CUDA library. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphics processing unit

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Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency

arXiv

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arXiv 2019年

作者： Li, Jia Fu, Kui Zhao, Shengwei Ge, Shiming State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security at University of Chinese Academy of Sciences Beijing100095 China

—The performance of video saliency estimation techniques has achieved significant advances along with the rapid development of Convolutional Neural Networks (CNNs). However, devices like cameras and drones may have limited computational capability and storage space so that the direct deployment of complex deep saliency models becomes infeasible. To address this problem, this paper proposes a dynamic saliency estimation approach for aerial videos via spatiotemporal knowledge distillation. In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model. The knowledge of spatial and temporal saliency is first separately transferred from the two complex and redundant teachers to their simple and compact students, and the input scenes are also degraded from high-resolution to low-resolution to remove the probable data redundancy so as to greatly speed up the feature extraction process. After that, the desired spatiotemporal model is further trained by distilling and encoding the spatial and temporal saliency knowledge of two students into a unified network. In this manner, the inter-model redundancy can be further removed for the effective estimation of dynamic saliency on aerial videos. Experimental results show that the proposed approach outperforms ten state-of-the-art models in estimating visual saliency on aerial videos, while its speed reaches up to 28,738 FPS on the GPU platform. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

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A New Method for the Construction of Evolving Embedded Representations of Words

A New Method for the Construction of Evolving Embedded Repre...

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ACS/IEEE International Conference on Computer systems and Applications

作者： Amal Bouraoui Salma Jamoussi Abdelmajid Ben Hamadou Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL Sfax-Tunisia Technopole of Sfax Sfax Tunisia

There are several natural language processing tasks such as natural language understanding, information retrieval and social network analysis which require dynamic update of the word embeddings representation so as to keep pace with the words meanings evolving. Thanks to their ability to capture both syntactic and semantic information within language, the continuous bag-of-words, a learning word representation model, has gained a lot of attention from researchers and has been successfully applied to different NLP tasks. However, this model does not adequately capture the change of word semantics over time. This paper provides a new perspective to determine the semantic similarity of words over time by constructing several representations of words that reflect the semantic change of words based on latest embeddings. We examine the performance of our improved CBOW on an evolved data base collected from English Wikipedia pages. Our learned embeddings illustrate the semantic evolution of words better than the original CBOW.

关键词： Semantics Task analysis Vocabulary Computer architecture Natural language processing Encyclopedias

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Cylindrical vector beam-excited frequency-tunable second harmonic generation in a plasmonic octamer

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Photonics Research 2018年第3期6卷 157-161页

作者： FAJUN XIAO WUYUN SHANG WEIREN ZHU LEI HAN MALIN PREMARATNE TING MEI JIANLIN ZHAO MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of ScienceNorthwestern Polytechnical UniversityXi'an 710129China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems Monash UniversityClaytonVlC 3800Australia

We report a method to tune the second harmonic generation(SHG) frequency of a metallic octamer by employing cylindrical vector beams as the excitation. Our method exploits the ability to spatially match the polarization state of excitations with the fundamental target plasmonic modes, enabling flexible control of the SHG resonant *** is found that SHG of the octamer is enhanced over a broad band(400 nm) by changing the excitation from the linearly polarized Gaussian beam to radially and azimuthally polarized beams. More strikingly, when subjected to an azimuthally polarized beam, the SHG intensity of the octamer becomes 30 times stronger than that for the linearly polarized beam even in the presence of Fano resonance.

关键词： SHG Cylindrical vector beam-excited frequency-tunable second harmonic generation in a plasmonic octamer

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