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Robust Network Optimization by Deep Generative Models and Stochastic Optimization

IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Shutao Xue, Ye Tang, Zhiwei Wang, Hao Shen, Chao Shi, Qingjiang Chang, Tsung-Hui The Chinese University of Hon g Kong School of Science and Engineering Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hon g Kong School of Data Science Shenzhen China Shenzhen Research Institute of Big Data Shenzhen International Center for Industrial and Applied Mathematics China Guangdong Provincial Engineering Technology Research Center for Intelligent Industrial Digital Twin and Optimization Shenzhen518172 China Tongji University School of Software Engineering Shanghai China

Wireless network optimization is essential for improving the network performance in mobile communications. However, due to the stochastic nature of wireless networks, existing schemes based on analytical models and deterministic optimization are less reliable. To this end, we design a framework for robust network optimization based on deep generative models and stochastic optimization. Inspired by the powerful diffusion process, we propose a deep generative simulator to capture the statistical distribution of the network performance. By sampling from the deep generative simulator, we can alleviate the inherent uncertainty related to the network performance and devise an innovative expectation-quantile-based stochastic objective function. The inner expectation is designed for the temporal statistics, while the outer quantile is developed for the spatial statistics. This designated two-tier objective function is capable of mitigating temporal fluctuations and ensuring satisfactory network performance across most geographical grids, thereby achieving robustness. To solve this stochastic optimization problem, a smooth zeroth-order approach is introduced by taking advantage of the unique structure of quantile functions. Through theoretical performance analysis and simulation experiments with real-world datasets, we demonstrate the superiority of our approach over other baseline schemes, highlighting its practical utility in robust network optimization. © 2002-2012 IEEE.

关键词： Generative diffusion model quantile function robust approach wireless network optimization

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ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognos...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yu, Xinlei Elazab, Ahmed Ge, Ruiquan Jin, Hui Jiang, Xinchen Jia, Gangyong Wu, Qing Shi, Qinglei Wang, Changmiao Hangzhou Dianzi University School of Computer Science Hangzhou China Shenzhen University School of Biomedical Engineering Shenzhen China Chinese University of Hong Kong Shenzhen School of Management and Economics Shenzhen China Chinese University of Hong Kong Shenzhen School of Medicine Shenzhen China Shenzhen Research Institute of Big Data Medical Big Data Lab Shenzhen China

ISBN: (纸本)9798350386226

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics. © 2024 IEEE.

关键词： Diagnosis

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A Novel Effectiveness Assessment Framework for Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer Based on Multi-modal Intelligence

A Novel Effectiveness Assessment Framework for Neoadjuvant C...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tian, Xiao Sui, Dong Liu, Weifeng Guo, Maozu Luo, Gongning Wang, Kuanquan Beijing Key Laboratory for Intelligent Processing Methods of Architectural Big Data Beijing China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Perceptual Computing Research Center Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350337488

Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer (LARC). With the development of artificial intelligence, an increasing number of studies have begun to explore its application in cancer treatment prediction. However, the prior methods exhibit considerable variability even with slight modifications to the input data, which could potentially undermine the reliability of the results. In this paper, we proposed RP-Net, a novel multi-modal fusion-based framework that combines feature information from magnetic resonance imaging (MRI) and whole slide images (WSI), establishing a relationship to map the therapeutic effectiveness of nCRT for LARC. We investigated the relationship of the tumour region and its periphery tissues, and demonstrated the validity of the proposed framework that involving 11 different combinations of modalities. The experimental results revealed that it has achieved higher prediction accuracy compared to the four intra-categories single-modal combinations and outperformed the two intra-categories multi-modal combinations. When compared to the other four inter-categories multi-modal combinations, the fusion features get accuracy of 2% ∼ 6% improvement respectively. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance Analysis

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China Communications 2023年第4期20卷 132-152页

作者： Xinrui Li Haiquan Lu Yong Zeng Shi Jin Rui Zhang National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China The Chinese University of Hong Kong Shenzhen Research Institute of Big DataShenzhen 518172China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced *** module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical *** accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array *** on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)*** addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are ***,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of *** simulation results are presented to validate our findings.

关键词： modular extremely large-scale array practical deployment projected apertures non-uniform spherical wave near-field modelling

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Reliable Throughput of Generalized Collision Channel without Synchronization

Reliable Throughput of Generalized Collision Channel without...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Fan, Yijun Liu, Yanxiao Chen, Yi Yang, Shenghao Yeung, Raymond W. The Chinese University of Hong Kong The Department of Information Engineering Hong Kong The Chinese University of Hong Kong Shenzhen The School of Science and Engineering Shenzhen China The Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9781665475549

We consider a generalized collision channel model for general multi-user communication systems, an extension of Massey and Mathys' collision channel without feedback for multiple access communications. In our model, there are multiple transmitters and receivers sharing the same communication channel. The transmitters are not synchronized and arbitrary time offsets between transmitters and receivers are assumed. A "collision"occurs if two or more packets from different transmitters partially or completely overlap at a receiver. Our model includes the original collision channel as a special *** paper focuses on reliable throughputs that are approachable for arbitrary time offsets. We consider both slot-synchronized and non-synchronized cases and characterize their reliable throughput regions for the generalized collision channel model. These two regions are proven to coincide. Moreover, it is shown that the protocol sequences constructed for multiple access communication remain "throughput optimal"in the generalized collision channel model. We also identify the protocol sequences that can approach the outer boundary of the reliable throughput region. © 2023 IEEE.

关键词： Transmitters

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Analysis of Labanotation generation based on motion capture data 5

Analysis of Labanotation generation based on motion capture ...

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5th International Conference on Artificial Intelligence and Computer science, AICS 2023

作者： Chen, Boran Wang, Jiaji Miao, Zhenjiang Li, Jianpo Yang, Shengchun Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center School of Computing Northeast Electric Power University Jilin Jilin132012 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510668621

Labanotation is an approach to record human movement with different distributions in space that have different representations of symbols. Motion capture is a technique that records the trajectory of an object in the time domain by tracking the motion of key points, observed using different sensors, and recorded in files. This paper is to manually mark the Labanotation based on 3D human motion capture data, to divide the 3D human motion into gaits and poses, and to record and represent different human movements and positions in space in the notation. This is an exploration of motion capture data to generate Labanotation, while also contributing to the preservation of folk-art forms. © 2023 SPIE.

关键词： Time domain analysis

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Glacial Lake Area Changes in High Mountain Asia during 1990-2020 Using Satellite Remote Sensing

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research 2023年第2期2022卷 1-23页

作者： Meimei Zhang Fang Chen Huadong Guo Lu Yi Jiangyuan Zeng Bin Li International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesNo.9 Dengzhuang South RoadBeijing 100094China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Coastal Environment and Resources Research of Zhejiang Province School of EngineeringWestlake UniversityHangzhou 310024China State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of Sciences Beijing 100094China

Changes in a large-scale glacial lake area directly reflect the regional glacier status and climate ***,long time series of glacial lake dataset and comprehensive investigation of the spatiotemporal changes in the gla... 详细信息

关键词： glacial glacier lake

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A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive data

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China Communications 2024年第1期21卷 310-322页

作者： Zhiwei Wang Nianhua Yang Qingqing Chen Wei Shen Zhiying Zhang School of Computer Sciences Nanjing University of Posts and TelecommunicationsNanjing 210023China Dept.of Intelligent Science and Information Law East China University of Political Science and LawShanghai 201620China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal UniversityHaikou 571158China State Key Laboratory of Public Big Data Guizhou UniversityGuizhou 550025China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Research Institute of Beijing University of Aeronautics and AstronauticsKunming 650233

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be *** owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the *** prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file *** also provide security analysis in the context of four typical attacks.

关键词： blind batch encryption data sharing onetime adaptive access public ledger security and privacy

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Identification of Key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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How Graph Neural Networks Learn: Lessons from Training Dynamics 41

How Graph Neural Networks Learn: Lessons from Training Dynam...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Chenxiao Wu, Qitian Wipf, David Sun, Ruoyu Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University China Amazon Web Services United States School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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