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GNN-based Structured Bayesian Inference for Multi-grid Localized Statistical Channel Modeling

IEEE Transactions on Wireless Communications 2025年

作者： Wang, Yiheng Xue, Ye Zhang, Shutao Chang, Tsung-Hui The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Guangdong Shenzhen518172 China The Chinese University of Hong Kong-Shenzhen Shenzhen Research Institute of Big Data School of Science and Engineering Guangdong518172 China

Localized statistical channel modeling (LSCM) is an efficient channel modeling framework recently proposed for wireless network optimization which learns the angular power spectrum (APS) of the downlink channel from the beam-wise reference signal receiving power (RSRP). However, the conventional LSCM is only based on RSRP measurements from one single geographical grid and ignores the inherent property of spatial consistency over wireless channels, resulting in suboptimal performance. To this end, we consider the LSCM in a manner of multiple geographical grids and further propose a novel graph-based approach for the multi-grid LSCM, called the accelerated Markovian variational Bayesian graph neural network (AMVB-GNN). The AMVB-GNN leverages a heterogeneous Markovian graph representation to capture the structured sparsity in the channel APSs and employs refined variational Bayesian inference (VBI) to learn the APSs of multiple grids. Notably, the design of AMVB-GNN eliminates the exact matrix inversion operations required in conventional VBI, thereby enhancing computational efficiency. Additionally, we demonstrate the partial permutation equivalence of AMVB-GNN, ensuring both interpretability and reliability. To address the issue of the demand for ground-truth APSs labels, we propose an unsupervised training loss function. Extensive simulation experiments validate the effectiveness and efficiency of the proposed AMVB-GNN model. © 2002-2012 IEEE.

关键词： Graph neural networks

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Token2vec: A Joint Self-Supervised Pre-Training Framework Using Unpaired Speech and Text 48

Token2vec: A Joint Self-Supervised Pre-Training Framework Us...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yue, Xianghu Ao, Junyi Gao, Xiaoxue Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of HongKong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

Self-supervised pre-Training has been successful in both text and speech processing. Speech and text offer different but complementary information. The question is whether we are able to perform a speech-Text joint pre-Training on unpaired speech and text. In this paper, we take the idea of self-supervised pre-Training one step further and propose token2vec, a novel joint pre-Training framework for unpaired speech and text based on discrete representations of speech. Specifically, we introduce two modality-specific tokenizers for speech and text. Based on these tokenizers, we convert speech/text sequences into discrete speech/text token sequences consisting of similar language units, thus mitigating the domain mismatch problem and length mismatch problem, which are caused by the distinct characteristics between speech and text. Finally, we feed the discrete speech and text tokens into a modality-Agnostic Transformer encoder and pre-Train with token-level masking language modeling (tMLM). Experiments show that token2vec is significantly superior to various speech-only pre-Training baselines, with up to 17.7% relative WER reduction. Token2vec model is also validated on a non-ASR task, i.e., spoken intent classification, and shows good transferability. © 2023 IEEE.

关键词： Modeling languages

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research progress of quantum artificial intelligence in smart city

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Intelligent and Converged Networks 2024年第2期5卷 116-133页

作者： Sumin Wang Ning Wang Tongyu Ji Yiyun Shi Chao Wang the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence(AI).As Moore’s Law approaches its limit,it is imperative to break the constraints that are holding back artificial *** computing and artificial intelligence have been advancing along the highway of human civilization for many years,emerging as new engines driving economic and social *** article delves into the integration of quantum computing and artificial intelligence in both research and *** introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum *** discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives:quantum supervised learning,quantum unsupervised learning,quantum reinforcement learning,and quantum deep *** an effort to address the limitations of smart cities,this article explores the formidable potential of quantum artificial intelligence in the realm of smart *** does so by examining aspects such as intelligent transportation,urban operation assurance,urban planning,and information communication,showcasing a plethora of practical achievements in the *** the foreseeable future,Quantum Artificial Intelligence(QAI)is poised to bring about revolutionary development to smart *** urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers,constructing an efficient,stable,and adaptive hybrid computing architecture that integrates quantum and classical computing,preparing quantum data as needed,and advancing controllable qubit hardware equipment to meet actual *** ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities,robustness,excellent generalization capabilities,and interpretability.

关键词： quantum artificial intelligence smart city D-Wave quantum computer

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research on the Parallel SM4-OFB Acceleration Based on Multiple Rounds of Iteration 3

Research on the Parallel SM4-OFB Acceleration Based on Multi...

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3rd International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Han, Ya Wang, Mingsheng Li, Yongqiang Ren, Lifeng Institute of Information Engineering CAS Key Laboratory of Cyberspace Security Defense Beijing China Chongqing Research Institute of Big Data Peking University Chongqing China

ISBN: (纸本)9798350365443

In OFB mode, the output of the cryptographic algorithm is fed back to the input of the cryptographic algorithm. The OFB mode does not directly encrypt plaintext through cryptographic algorithms, but generates ciphertext blocks by XOR combining plaintext blocks with the output of cryptographic algorithms. In OFB encryption mode, messages are treated as bitstreams, and the output of block encryption is added to the encrypted messages, making it difficult for bit errors to propagate. The SM4 algorithm is a symmetric cryptographic technique developed by the Chinese National Cryptographic Administration and standardized as part of the State Encryption Standard in China. SM4 is widely adopted in various applications, including wireless communication systems and secure data transmissions. In this paper, a fast implementation method of SM4 in OFB mode is proposed. We calculate the possible parallelization operations in OFB mode using the operational relationship between the SM4 algorithm round functions, and ensure the correctness of SM4-OFB encryption through a feedback compensation method. © 2024 IEEE.

关键词： Encryption algorithms

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Unveiling the planar deformation mechanisms for improved formability in pre-twinned AZ31 Mg alloy sheet at warm temperature

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Journal of Magnesium and Alloys 2023年第12期11卷 4659-4678页

作者： Xiaohuan Pan Lifei Wang Pengbin Lu Hua Zhang Guangsheng Huang Liuwei Zheng Bin Xing Weili Cheng Hongxia Wang Wei Liang Kwang Seon Shin Shanxi Key Laboratory of Advanced Magnesium-Based Materials College of Materials Science and EngineeringTaiyuan University of TechnologyTaiyuan 030024China National Engineering Laboratory for Industrial Big-data Application Technology Chongqing Innovation Center of Industrial Big-Data Co.Ltd.Chongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute for Advanced Studies in Precision Materials Yantai UniversityYantai 264005China National Engineering Research Center for Magnesium Alloys College of Materials Science&EngineeringChongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National University1 Gwanak-roGwanak-guSeoul 08826South Korea

To investigate the role of pre-twins in Mg alloy sheets during warm planar deformation,the stretch forming is conducted at 200℃.Results suggest the formability of the pre-twinned AZ31 Mg alloy sheet is enhanced to 11.30 *** mechanisms for the improved formability and the deformation behaviors during the planar stretch forming are systematically investigated based on the planar stress *** Schmid factor for deformation mechanisms are calculated,the results reveal that planar stress states extremely affect the Schmid factor for{10-12}*** detwinning is activated and the prismatic slip is enhanced in the pre-twinned sheet,especially under the planar extension stress state in the outer ***,the thickness-direction strain is accommodated *** dynamic recrystallization(DRX)type is continuous DRX(CDRX)regardless of the planar stress ***,the CDRX degree is greater under the planar extension stress *** twin lattices deviate from the perfect{10-12}twinning relation due to the planar compression stress state and the *** basal texture is weakened when the planar stress state tends to change the texture components.

关键词： AZ31 Mg alloy sheet Planar stretch forming Planar stress states Continuous dynamic recrystallization {10-12}tensile twinning

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Towards Pervasive Sensing: A multimodal approach via CSI and RGB image modalities fusion 3

Towards Pervasive Sensing: A multimodal approach via CSI and...

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3rd ACM MobiCom Workshop on Integrated Sensing and Communication Systems, ISACom 2023

作者： Zhou, Fujia Zhu, Guangxu Li, Xiaoyang Li, Hang Shi, Qingjiang School of Software Engineering Tongji University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798400703645

Channel state information (CSI) and RGB (Red, Green, Blue) image are two fundamental modalities for accurate human activity recognition (HAR). CSI data is privacy preserving, independent of viewing angle and lighting, while RGB image data can provide useful information after processed by deep learning (DL) methods. However, both modalities have inherent disadvantages. CSI modality is with high environmental dependence, causing unstable fluctuations in wireless signals. RGB image modality is heavily restricted by camera facing angle and lighting condition. Hence, we propose a general multimodal fusion framework for HAR tasks based on both CSI and RGB image modalities. Distinct from other literatures on multimodal fusion mainly based on vision modalities, we utilized CSI modality from wireless domain along with RGB images to realize more comprehensive human activity sensing. Both early fusion and late fusion strategies are formulated and developed, with multiple models constructed from two modern unimodal frameworks to constitute corresponding fusion models. Experiments are conducted on a synchronous CSI and RGB image human activity dataset and validate the advantage of the proposed multimodal fusion design comparing with unimodal approaches. © 2023 ACM.

关键词： Channel state information

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Alignment at Pre-training! Towards Native Alignment for Arabic LLMs 38

Alignment at Pre-training! Towards Native Alignment for Arab...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liang, Juhao Cai, Zhenyang Zhu, Jianqing Huang, Huang Zong, Kewei An, Bang Alharthi, Abdulmohsen He, Juncai Zhang, Lian Li, Haizhou Wang, Benyou Xu, Jinchao Shenzhen Research Institue of Big Data Shenzhen China The Chinese University of Hong Kong Shenzhen China King Abdullah University of Science and Technology Thuwal Saudi Arabia Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

The alignment of large language models (LLMs) is critical for developing effective and safe language models. Traditional approaches focus on aligning models during the instruction tuning or reinforcement learning stages, referred to in this paper as 'post alignment'. We argue that alignment during the pre-training phase, which we term 'native alignment', warrants investigation. Native alignment aims to prevent unaligned content from the beginning, rather than relying on posthoc processing. This approach leverages extensively aligned pre-training data to enhance the effectiveness and usability of pre-trained models. Our study specifically explores the application of native alignment in the context of Arabic LLMs. We conduct comprehensive experiments and ablation studies to evaluate the impact of native alignment on model performance and alignment stability. Additionally, we release open-source Arabic LLMs that demonstrate state-of-the-art performance on various benchmarks, providing significant benefits to the Arabic LLM community. © 2024 Neural information processing systems foundation. All rights reserved.

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Time Series data Cleaning under Expressive Constraints on Both Rows and Columns 40

Time Series Data Cleaning under Expressive Constraints on Bo...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Genglong Wang, Hongzhi Wang, Chen Song, Yichen School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Laboratory for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Time series data generated by thousands of sensors are suffering data quality problems. Traditional constraint-based techniques have greatly contributed to data cleaning applications. However, cleaning methods that support expressive constraints on time series data remain insufficient. Given the notable characteristics of time series data, existing cleaning approaches are challenged to provide good repair solutions. To address the challenges, we propose a novel data cleaning method for time series which incorporates expressive constraints that support arithmetic operations between attributes and time context. In the violation detection phase, we introduce specialized violation degree quantification functions and design a violation cell discovery algorithm to identify errors hidden in time series data. In the data repairing phase, we formalize the cleaning task as a constrained optimization problem and develop a novel repair objective function that considers both modification costs and conformance degrees of constraints. We effectively reduce the repair search space through the evaluation of time-context constraints and propose a bidirectional repairing algorithm. We also provide theoretical analysis of the proposed repairing method. Experimental results on three real-world IoT datasets across five metrics demonstrate that our proposed method outperforms seven state-of-the-art cleaning techniques specialized for time series data. Specifically, we achieve a 60% improvement in repairing effectiveness and a 70% reduction in time costs with our designed cleaning strategy. © 2024 IEEE.

关键词： Time series

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Designing Group-Level Multi-User Precoding in Multi-Subcarrier Networks: Is EESM the Solution? 24

Designing Group-Level Multi-User Precoding in Multi-Subcarri...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Guan, Xin Shi, Qingjiang Tongji University School of Software Engineering Shanghai China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781665496261

Existing researches on multi-subcarrie multi-antenna networks usually apply the subcarrier-level precoding, by which each tone is allocated with a dedicated precoder. Such kind of approaches are far from practical implementation due to unbearably high design complexity. By recognizing the fact that multiple subcarriers in a practical cellular network are assigned into one group and share one common precoder, we investigate a more realistic group-level precoding approach. Inspired by the commonly used link adaptation technique namely EESM (Exponential Effective SNR Mapping), a novel spectral efficiency (SE) evaluation scheme is proposed, from which a sumrate maximization of multiple users with group-level precoding is formulated. Via leveraging successive convex approximation (SCA) framework, we tackle the non-convex problem by solving a sequence of convex problems. Furthermore, we also develop a low-complexity solution using gradient projection which are free of any numerical solvers. Simulation results verify the superiority of our proposed novel SE model as well as the efficiency of the proposed algorithms. © 2023 IEEE.

关键词： Spectrum efficiency

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