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arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Extremely Large-Scale Dynamic Metasurface Antennas (XL-DMAs): Near-Field Modeling and Channel Estimation

arXiv

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arXiv 2024年

作者： Yang, Songjie Lyu, Wanting Ning, Boyu Xiu, Yue Xiong, Youzhi Chen, Hua Assi, Chadi Yuen, Chau The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China College of Air Traffic Management Civil Aviation Flight University of China Sichuan618311 China College of Physics and Electronic Engineering Sichuan Normal University Chengdu610101 China The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo315211 China Concordia University MontrealQCH3G 1M8 Canada The School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Dynamic metasurface antennas (DMAs) represent a novel transceiver array architecture for extremely large-scale (XL) communications, offering the advantages of reduced power consumption and lower hardware costs compared to conventional arrays. This paper focuses on near-field channel estimation for XL-DMAs. We begin by analyzing the near-field characteristics of uniform planar arrays (UPAs) and introducing the Oblong Approx. model. This model decouples elevation-azimuth (EL-AZ) parameters for XL-DMAs, providing an effective means to characterize the near-field effect. It offers simpler mathematical expressions than the second-order Taylor expansion model, all while maintaining negligible model errors for oblong-shaped arrays. Building on the Oblong Approx. model, we propose an EL-AZ-decoupled estimation framework that involves near- and far-field parameter estimation for AZ/EL and EL/AZ directions, respectively. The former is formulated as a distributed compressive sensing problem, addressed using the proposed off-grid distributed orthogonal least squares algorithm, while the latter involves a straightforward parallelizable search. Crucially, we illustrate the viability of decoupled EL-AZ estimation for near-field UPAs, exhibiting commendable performance and linear complexity correlated with the number of metasurface elements. Moreover, we design an measurement matrix optimization method with the Lorentzian constraint on DMAs and highlight the estimation performance degradation resulting from this constraint. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

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Adaptive Index Modulated OFDM Spread Spectrum for Underwater Acoustic Integrated Sensing and Communication Networks

Adaptive Index Modulated OFDM Spread Spectrum for Underwater...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Junfeng Wang Yue Cui Mingzhang Zhou Haixin Sun Lanjun Liu Ailing Zhang School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application MNR China College of Computer and Information Engineering Tianjin Normal University Tianjin China School of Information Science and Engineering Xiamen University Xiamen China College of Engineering Ocean University of China Qingdao China

It is predicted that underwater acoustic integrated sensing and communication networks (UAISACNs) would be one of the main enablers for supporting a variety of emerging applications due to benefits of communication-assisted sensing, sensing-assisted communication and their integration. Mean-while, link adaptation and index modulated orthogonal frequency division multiplexing spread spectrum (IM-OFDM-SS) are a kind of powerful transmission technique for underwater acoustic communications networks, which can exploit the time-varying behavior of underwater communication channels to transmit much higher speeds. Thus, this paper aims to propose the adaptive IM-OFDM-SS for the UAISACNs, where the adaptive technique is integrated with the IM-OFDM-SS. The presented scheme is capable of adaptive transmitting the downlink information to the communication node in the UAISACNs by employing the estimating sensing parameters of its echo, which can reduce large amount of the signaling overhead and the long latency resulted from the conventional feedback-based protocol. Our suggested framework can choose the best possible transmission mode to maximize the achievable throughput, which can be verified through computer simulations, where we show the benefits of our proposed adaptive transmission scheme.

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Characteristic Analysis and Similarity Calculation of Molecular Compounds of Traditional Chinese Medicine

Characteristic Analysis and Similarity Calculation of Molecu...

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作者： Juxing Hong Lu Wang Shoude Zhang Fenfen Xie Computer Technology and Application Qinghai University State Key Laboratory Qinghai University

To be able to extract corresponding features of molecular compounds of traditional Chinese medicine, and to use the extracted features to analyze physical and chemical properties and similarities. In this study, Python technology is used to process the TCM molecular library file into an effective TCM molecular compound file, and the chemical drug molecular compound analysis method is used to extract the characteristics between TCM molecular compounds and analyze the physical and chemical properties contained in it. Besides, the four similarity calculation methods such as the Tanimoto coefficient and the Dice coefficient in data mining techniques combined with molecular fingerprint to analyze the similarity and physicochemical properties between traditional Chinese medicine molecular compounds. The experimental results of this study show that the analysis method of chemical drug molecular compounds can better extract the characteristics of traditional Chinese medicine molecular compounds. The combination of data mining techniques and the analysis method to analyze the similarity and characteristics of traditional Chinese medicine molecular compounds can play an important role in the field of drug discovery and biological activity research of traditional Chinese medicine. It can play an important role in the screening of drugs for the clinical treatment of diseases.

关键词： Chinese medicine molecular compound Features Similarity Data mining

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Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding

arXiv

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arXiv 2023年

作者： Xu, Lixiang Cui, Qingzhe Hong, Richang Xu, Wei Chen, Enhong Yuan, Xin Li, Chenglong Tang, Yuanyan The College of Artificial Intelligence and Big Data Hefei University Hefei230027 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia The School of Artificial Intelligence Anhui University Hefei230601 China The Zhuhai UM Science and Technology Research Institute FST University of Macau China

In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address this problem, we introduce a compression method based on knowledge distillation for this field, which largely reduces the number of parameters while preserving model performance as much as possible. Specifically, to enhance the capabilities of smaller models, we design a high-performing large model called Group Multi-view Vision Transformer (GMViT). In GMViT, the view-level ViT first establishes relationships between view-level features. Additionally, to capture deeper features, we employ the grouping module to enhance view-level features into group-level features. Finally, the group-level ViT aggregates group-level features into complete, well-formed 3D shape descriptors. Notably, in both ViTs, we introduce spatial encoding of camera coordinates as innovative position embeddings. Furthermore, we propose two compressed versions based on GMViT, namely GMViT-simple and GMViT-mini. To enhance the training effectiveness of the small models, we introduce a knowledge distillation method throughout the GMViT process, where the key outputs of each GMViT component serve as distillation targets. Extensive experiments demonstrate the efficacy of the proposed method. The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB. The smaller models, GMViT-simple and GMViT-mini, reduce the parameter size by 8 and 17.6 times, respectively, and improve shape recognition speed by 1.5 times on average, while preserving at least 90% of the classification and retrieval performance. The code is available at https://***/bigdatagraph/*** Codes 68 Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Mitigation of allergic asthma in mice: A compound mixture comprising luteolin, arbutin, and marmesin from Gerbera Piloselloides Herba by suppression of PI3K/Akt pathway

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Heliyon 2024年第19期10卷 e37632页

作者： Liu, Chunhua He, Yu Zhou, Kun Wang, Hong Zhou, Meng Sun, Jia Lu, Yuan Huang, Yong Wang, Yonglin Liu, Ting Li, Yongjun State Key Laboratory of Functions and Applications of Medicinal Plants Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education) Guizhou Medical University Guiyang 550004 China Guizhou Provincial Key Laboratory of Pharmaceutics Guizhou Medical University Guiyang 550004 China School of Pharmacy Guizhou Medical University Guiyang 561113 China Department of Pediatrics Guizhou Provincial People's Hospital Guiyang 550002 China Computer Education and Information Technology Center Guizhou Medical University Guiyang 561113 China

Background: Gerberae Piloselloidis Herba (GPH) exhibits notable efficacy in alleviating allergic asthma. Previous studies in our research have identified a mixture of luteolin, arbutin, and marmesin as effective components of GPH in treating allergic asthma. However, the underlying mechanism remains unclear. This study aims to elucidate the molecular mechanism of these active components. Method: Using an ovalbumin (OVA)-induced allergic asthma mouse model, various treatment groups were administered, including GPH, the active component mixture (termed “Mixture”) containing luteolin, arbutin, and marmesin, and a positive drug (dexamethasone, DEX). Relevant indices were assessed, including behavioral characteristics, inflammatory cell counts, cytokine levels, histopathological examination of lung tissue, apoptosis, and expression of key proteins such as Caspase-3, Bax, Bcl-2, PI3K, p-PI3K, Akt, and p-Akt. The effect of the Mixture on the PI3K/Akt signaling pathway was further verified using the PI3K inhibitor LY294002. Results: The Mixture significantly alleviated asthma symptoms, decreased IgE levels, cytokine levels (IL-4, IL-5, IL-13 and TNF-α), and the number of inflammatory cells in serum or bronchoalveolar lavage fluid (BALF), leading to the alleviation of lung pathological lesions. Additionally, the Mixture reduced the expression of Bax and Caspase-3 while increasing Bcl-2 expression, resulting in mitigated apoptosis in lung tissue. Furthermore, there appeared a decrease in the levels of PI3K and p-PI3K, as well as the ratio of p-Akt to Akt in the Mixture group, indicating the suppression of PI3K and Akt phosphorylation. Interestingly, the effects of the Mixture were comparable to those of GPH, LY294002, or the combination of LY294002 with the Mixture. Conclusion: The study confirms that the Mixture containing luteolin, arbutin, and marmesin indeed alleviates allergic asthma induced by OVA in mice by suppressing the PI3K/Akt signaling pathway. These findings hig

关键词： Active compounds Allergic asthma Apoptosis Gerbera Piloselloides Herba Inflammation PI3K/Akt pathway

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How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

TechRxiv

引用

TechRxiv 2022年

作者： Lin, Hao Wang, Chun-Dong Gao, Hao-Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Ministry of Education Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Cybersecurity researchers always ignore the "people" behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— "How to solve cyberspace security issues via social engineering?" We briefly review existing frameworks for social engineering attacks and propose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four "guidelines" for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field. © 2022, CC BY.

关键词： Cybersecurity

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Personalized Heart Disease Detection via ECG Digital Twin Generation

arXiv

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arXiv 2024年

作者： Hu, Yaojun Chen, Jintai Hu, Lianting Li, Dantong Yan, Jiahuan Ying, Haochao Liang, Huiying Wu, Jian College of Computer Science and Technology Zhejiang University China Computer Science Department University of Illinois at Urbana-Champaign United States Medical Big Data Center Guangdong Provincial People’s Hospital Southern Medical University China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China School of Public Health Zhejiang University China State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital Zhejiang University School of Medicine China

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals’ anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. The code can be found at https://***/huyjj/LAVQ-Editor. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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Diagrammatic Sentence Pattern Structure System and Its Treebank Construction 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Peng, Weiming Zhao, Min Song, Yuchen Hu, Jiajia Song, Tianbao Sui, Zhifang Song, Jihua Laboratory for Chinese Character and Language Research and Social Application Beijing Normal University Beijing100875 China Linguistic Data Consortium University of Pennsylvania PhiladelphiaPA19104-2653 United States Beijing Han-sky Education Technology Co. Ltd Beijing100084 China Department of Linguistics University of Michigan Ann ArborMI48109-1220 United States School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Key Laboratory of Computational Linguistics Ministry of Education Peking University Beijing100871 China

ISBN: (纸本)9780000000002

The Sentence Pattern Structure (SPS) is a formalized syntactic structure based on sentence-based grammar, which presents the structure of sentences in a customized diagram form. This paper proposes an SPS system covering three aspects: clause structure, lexical structure, and inter-sentence structure, elucidating its design philosophy and parsing principles of sentence-based grammar. Finally, an overview of the engineering progress of building a Chinese treebank based on this system is provided. © 2024 China National Conference on Computational Linguistics.

关键词： Trees (mathematics)

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CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

arXiv

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arXiv 2023年

作者： Guo, Di Li, Sijin Liu, Jun Tu, Zhangren Qiu, Tianyu Xu, Jingjing Feng, Liubin Lin, Donghai Hong, Qing Lin, Meijin Lin, Yanqin Qu, Xiaobo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China The Key Laboratory for Chemical Biology of Fujian Province MOE Key Laboratory of Spectrochemical Analysis & Instrumentation College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China The China Mobile Group Xiamen361005 China

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://***/***. © 2023, CC BY.

关键词： Deep learning

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