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arXiv 2024年

作者： Wu, Hongjia Zeng, Hui Xiong, Zehui Kang, Jiawen Cai, Zhiping Chan, Tse-Tin Niyato, Dusit Han, Zhu Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Automation Guangdong University of Technology China College of Computer National University of Defense Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Updates of extensive Internet of Things (IoT) data are critical to the immersion of vehicular metaverse services. However, providing high-quality and sustainable data in unstable and resource-constrained vehicular networks remains a significant challenge. To address this problem, we put forth a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models trained by their latest local data for augmented reality (AR) services in the vehicular metaverse, while preserving their privacy through federated learning. To comprehensively evaluate the contribution of locally trained learning models provided by MUs to AR services, we design a new immersion metric that captures service immersion by considering the freshness and accuracy of learning models, as well as the amount and potential value of raw data used for training. We model the trading interactions between metaverse service providers (MSPs) and MUs as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains. Moreover, considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Then, a fully distributed dynamic reward method based on deep reinforcement learning is presented, which operates without any private information about MUs and other MSPs. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets compared to benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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Stochastic Qubit Resource Allocation for Quantum Cloud Computing

arXiv

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arXiv 2022年

作者： Kaewpuang, Rakpong Xu, Minrui Niyato, Dusit Yu, Han Xiong, Zehui Kang, Jiawen School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Automation Guangdong University of Technology China

Quantum cloud computing is a promising paradigm for efficiently provisioning quantum resources (i.e., qubits) to users. In quantum cloud computing, quantum cloud providers provision quantum resources in reservation and on-demand plans for users. Literally, the cost of quantum resources in the reservation plan is expected to be cheaper than the cost of quantum resources in the on-demand plan. However, quantum resources in the reservation plan have to be reserved in advance without information about the requirement of quantum circuits beforehand, and consequently, the resources are insufficient, i.e., under-reservation. Hence, quantum resources in the on-demand plan can be used to compensate for the unsatisfied quantum resources required. To end this, we propose a quantum resource allocation for the quantum cloud computing system in which quantum resources and the minimum waiting time of quantum circuits are jointly optimized. Particularly, the objective is to minimize the total costs of quantum circuits under uncertainties regarding qubit requirement and minimum waiting time of quantum circuits. In experiments, practical circuits of quantum Fourier transform are applied to evaluate the proposed qubit resource allocation. The results illustrate that the proposed qubit resource allocation can achieve the optimal total costs. © 2022, CC0.

关键词： Resource allocation

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Improvement of computer Recognition of Foam Layer Criteria

Improvement of Computer Recognition of Foam Layer Criteria

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2022 International Conference "Quality Management, Transport and information Security, information Technologies", IT and QM and IS 2022

作者： Zatonskiy, Andrey V. Bazhenov, Ruslan I. Orozobekova, Aida K. Beknazarova, Saida S. Gorbunova, Tatiana N. Ledovskikh, Irina A. Perm National Research Polytechnic University Automation of Technological Processes Perm Russia Sholom-Aleichem Priamursky State University Information Systems and Mathematics Birobidzhan Russia Kyrgyz State University of Construction Transport and Architecture Name of N.Isanov Applied Computer Science Bishkek Kyrgyzstan Tashkent University of Information Technologies Named by Muhammad Al-Khwarizmi Audiovisual Technologies Tashkent Uzbekistan Moscow State University of Civil Engineering Applied Mathematics Moscow Russia Pacific National University Mathematics and Information Technology Khabarovsk Russia

ISBN: (数字)9781665488709

ISBN: (纸本)9781665488709

Foam bed flotation processes are common in the poly metallic, potash and food industries. A worker who visually assesses the condition of the foam bed controls the process. This reduces process control and product quality because of the human factor. Using computer vision is based on bubble boundary recognition to control due to poor lighting, low foam contrast, and splash back. Recognition is possible by analyzing the glare from the foam surface. We attempt to improve recognition by accounting for the dark antiglare that takes place due to the bubble deformation. It improved recognition and reduced the noise of the method to a few percents. © 2022 IEEE.

关键词： Process control

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Performance Analysis of Free-Space information Sharing in Full-Duplex Semantic Communications

Performance Analysis of Free-Space Information Sharing in Fu...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Hongyang Du Jiacheng Wang Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim Boon Hee Soong School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University South Korea Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to design an efficient information sharing scheme among users to replace the heavy and repetitive computation. In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications. Specifically, the view images of MR users in the same real-world scenario may be analogous. Therefore, when one user (i.e., a device) completes some computation tasks, the user can send his own calculation results and the semantic features extracted from the user's own view image to nearby users (i.e., other devices). On this basis, other users can use the received semantic features to obtain the spatial matching of the computational results under their own view images without repeating the computation. Using generalized small-scale fading models, we analyze the key performance indicators of full-duplex D2D communications, including channel capacity and bit error probability, which directly affect the transmission of semantic information. Finally, the numerical analysis experiment proves the effectiveness of our proposed methods.

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Context Convolution Dehazing Network With Channel Attention 5

Context Convolution Dehazing Network With Channel Attention

引用

5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Li, Yuanyuan Meng, Jun Zhu, Zhiqin Huang, Xinghua Qi, Guanqiu Luo, Yaqin Chongqing University of Posts and Telecommunications Chongqin Key Laboratory of Image Cognition Chongqing400065 China Chongqing University College of Automation Chongqing400044 China Buffalo State College Computer Information Systems Department BuffaloNY14222 United States

ISBN: (纸本)9781665426305

Fog and haze weather conditions lead to deterioration of image visual quality. Under these conditions, advanced image processing tasks such as object detection and image segmentation are difficult to perform. To solve related problems, in this paper we propose an end-to-end context dilated convolution dehazing network with channel attention to return to a clear image from a haze image. This model uses context dilated module extracts the multi-scale scene information in the haze image, which can better maintain the original color of the image while removing the haze. The channel attention enables the model to separate the importance of features and boosts the model's power to adapt to different input scenarios. In the training phase, the model uses contrastive learning to distinguish the potential difference between the haze picture and the clear picture, helping the model to better renewal the clear image. The results of contrastive tests with existing methods indicate the proposed method has excellent dehazing performance. © 2021 IEEE.

关键词： Convolution

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Sparse Dynamic Volume Transunet with Multi-Level Edge Fusion for Brain Tumor Segmentation

SSRN

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SSRN 2023年

作者： Zhu, Zhiqin Sun, Mengwei Qi, Guanqiu Li, Yuanyuan Gao, Xinbo Liu, Yu College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Computer Information Systems Department State University of New York at Buffalo State BuffaloNY14222 United States Department of Biomedical Engineering Hefei University of Technology Hefei230009 China

3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and treatment. Accurate segmentation results are critical for localization and spatial distribution of brain tumors using 3D MRI. However, most existing methods mainly focus on extracting global semantic features from the spatial and depth dimensions of a 3D volume, while ignoring voxel information, inter-layer connections, and detailed features. A 3D brain tumor segmentation network SDV-TUNet (Sparse Dynamic Volume TransUNet) based on an encoder-decoder architecture is proposed to achieve accurate segmentation by effectively combining voxel information, inter-layer feature connections, and intra-axis information. Volumetric data is fed into a 3D network consisting of extended depth modeling for dense prediction by using two modules: sparse dynamic (SD) encoder-decoder module and multi-level edge feature fusion (MEFF) module. The SD encoder-decoder module is utilized to extract global spatial semantic features for brain tumor segmentation, which employs multi-head self-attention and sparse dynamic adaptive fusion in a 3D extended shifted window strategy. In the encoding stage, dynamic perception of regional connections and multi-axis information interactions are realized through local tight correlations and long-range sparse correlations. The MEFF module achieves the fusion of multi-level local edge information in a layer-by-layer incremental manner and connects the fusion to the decoder module through skip connections to enhance the propagation ability of spatial edge information. The proposed method is applied to the BraTS2020 and BraTS2021 benchmarks, and the experimental results show its superior performance compared with state-of-the-art brain tumor segmentation methods. The source code of the proposed method is available at https://***/SunMengw/SDV-TUNet. © 2023, The Authors. All rights reserved.

关键词： Tumors

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GeneCompass:deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model

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细胞研究（英文版） 2024年第12期34卷 830-845页

作者： Xiaodong Yang Guole Liu Guihai Feng Dechao Bu Pengfei Wang Jie Jiang Shubai Chen Qinmeng Yang Hefan Miao Yiyang Zhang Zhenpeng Man Zhongming Liang Zichen Wang Yaning Li Zheng Li Yana Liu Yao Tian Wenhao Liu Cong Li Ao Li Jingxi Dong Zhilong Hu Chen Fang Lina Cui Zixu Deng Haiping Jiang Wentao Cui Jiahao Zhang Zhaohui Yang Handong Li Xingjian He Liqun Zhong Jiaheng Zhou Zijian Wang Qingqing Long Ping Xu The X-Compass Consortiudm Hongmei Wang Zhen Meng Xuezhi Wang Yangang Wang Yong Wang Shihua Zhang Jingtao Guo Yi Zhao Yuanchun Zhou Fei Li Jing Liu Yiqiang Chen Ge Yang Xin Li Baoyang Hu Wei Li Fei Gao Leqian Yu Qi Gu Weiwei Zhai Zhengting Zou Jingqi Yu Wenhui Wu Xinxin Lin Yu Zou Yongshun Ren Fan Li Yixiao Zhao Yike Xin Longfei Han Shuyang Jiang Kai Ma Qicheng Chen Haoyuan Wang Huanhuan Wu Chaofan He Yilong Hu Shuyu Guo Yiyun Li Zaitian Wang Huimin He Shan Zong Jiajia Wang Yan Chen Chunyang Zhang Chengrui Wang Ran Zhang Meng Xiao Yining Wang Xin Qin Jiaxin Qin Chenhao Li Zhufeng Xu Zeyuan Zhang Xiaoning Qi Wuliang Huang Yaoru Luo Qinxuan Luo Ziwen Liu Teng Wang Yiming Huang Shirui Li Kangning Dong Qunlun Shen State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Institute for Stem Cell and Regenerative Medicine Chinese Academy of SciencesBeijingChina Beijing Institute for Stem Cell and Regenerative Medicine BeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Research Center for Ubiquitous Computing Systems Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Computer Network Information Center Chinese Academy of SciencesBeijingChina Institute of Automation Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Computer Network Information Center Chinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina University of Chinese Academy of Sciences BeijingChina CEMS NCMISHCMSMDISRCSDSAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology

Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical ***,the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across *** breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this *** this study,we built an extensive dataset of over 120 million human and mouse single-cell *** data preprocessing,we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model,named *** pre-training,GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised *** fine-tuning for multiple downstream tasks,GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological *** also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal ***,GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.

关键词： knowledge model with cross-species deciphering foundation genecompass informed mechanisms regulatory

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Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation

arXiv

引用

arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Cao, Yuan Gao, Yulan Ren, Chao Yu, Han The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore The School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China

Quantum computing networks enable scalable collaboration and secure information exchange among multiple classical and quantum computing nodes while executing large-scale generative AI computation tasks and advanced quantum algorithms. Quantum computing networks overcome limitations such as the number of qubits and coherence time of entangled pairs and offer advantages for generative AI infrastructure, including enhanced noise reduction through distributed processing and improved scalability by connecting multiple quantum devices. However, efficient resource allocation in quantum computing networks is a critical challenge due to factors including qubit variability and network complexity. In this article, we propose an intelligent resource allocation framework for quantum computing networks to improve network scalability with minimized resource costs. To achieve scalability in quantum computing networks, we formulate the resource allocation problem as stochastic programming, accounting for the uncertain fidelities of qubits and entangled pairs. Furthermore, we introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation to resolve the proposed stochastic resource allocation problem efficiently. Finally, we optimize the resource allocation in heterogeneous quantum computing networks supporting quantum generative learning applications and propose a multi-agent RL-based algorithm to learn the optimal resource allocation policies without prior knowledge. © 2024, CC BY.

关键词： Quantum cryptography

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Simple and Accurate Method to Evaluate Type a Standard and Expanded Uncertainties of Measurement for the Laplace Distributed Observations

SSRN

引用

SSRN 2023年

作者： Dorozhovets, Mykhaylo Department of Metrology and Diagnostic Systems Faculty of Electrical and Computer Engineering Rzeszow University of Technology ul. Wincentego Pola 2A Rzeszów35-959 Poland Department of Measuring Information Technologies Institute of Computer Technologies Automation and Metrology Lviv Polytechnic National University 12 Bandera street Lviv79013 Ukraine

The article proposes simple and accurate evaluation of the standard and expanded uncertainties of the Laplace population median. The uncertainty evaluation based on the approximation of the population median distribution for n observations by the distribution of the sample median for n - 2 observations. Comparisons of the results obtained by proposed approximation with exact results shown that when 6 ≤ n ≤ 10 the differences between approximated and exact standard uncertainties are lees then 1% (0.56%), i.e. negligibly small and for a confidence level p = 0.90 and 0.95 the approximated value of expanded uncertainty differ from exact value less than 1%, and for p= 0.99 difference does not exceed ≈ 5%. When n ≥ 11 the Monte Carlo simulation results showed that the proposed method for determining both the standard and the expanded uncertainty of population median do not exceed ±1%, i.e. is very much precise. © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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Proceedings of the 37th International Conference on Neural information Processing systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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