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2023 ACM/SIGMOD International Conference on Management of data, SIGMOD 2023

作者： Wang, Jue Chen, Ke Shou, Lidan Jiang, Dawei Chen, Gang Key Lab of Intelligent Computing Based Big Data of Zhejiang Province Zhejiang University Hangzhou China

ISBN: (纸本)9781450395076

Deep learning models, particularly pre-trained language models (PLMs), have become increasingly important for a variety of applications that require text/language processing. However, these models are resource-intensive and often require costly hardware such as dedicated GPU servers. In response to this issue, we present SMILE, a novel prototype system for efficient deployment and management of such models in the cloud. Our goal is to build a cloud platform from which tenants can easily derive their own custom models, and rent PLM processors to run inference services on these models at reduced costs. To facilitate this, we present a co-design of cost-effective storage and computation scheme for managing massive customized PLMs with constrained hardware resources via effective resource sharing and multiplexing. Our system consists of four core components: vPLM creator, vPLM storage appliance, vPLM trainer, and vPLM processor, which allow tenants to easily create, store, train, and use their customized PLM in the cloud without the need for dedicated hardware or maintenance. In particular, vPLM processors are virtualized from a physical machine, and are designed to have a multi-tenant nature, enabling efficient utilization of resources by precomputing the intermediate representation of PLMs and using adapters to provide customization instead of training the entire model. This allows tenants to host their PLMs in the cloud at minor costs. In our demonstration, we show that over 10,000 models can be hosted on one single machine without compromising the inference speed and accuracy. Overall, our system provides a convenient and cost-effective solution for tenants to host and manage PLMs in the cloud for their customized tasks. © 2023 ACM.

关键词： Cost effectiveness

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 131-141页

作者： Mingsheng Shang Xin Luo Zhigang Liu Jia Chen Ye Yuan MengChu Zhou the Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences the School of Computer Science and Engineering Beihang University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix *** results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

关键词： big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile computing 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Deciphering the role of liver metabolic enzymes in dementia pathogenesis

Cell Investigation

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Cell Investigation 2025年

作者： Kaicheng Li Jingyun Zhang Hui Hong Yeerfan Jiaerken Yinuo Tu Xucheng Wu Bonan Ding Zhenqing Yang Qingze Zeng Xiao Luo Hui Zhang Zuyun Liu Chao Wang Department of Radiology Center for Clinical Big Data and Analytics the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou Zhejiang 310009 China Center for Clinical Big Data and Analytics of the Second Affiliated Hospital Department of Big Data in Health Science School of Public Health Zhejiang Key Laboratory of Intelligent Preventive Medicine Zhejiang University School of Medicine Hangzhou Zhejiang 310058 China Human Phenome Institute Fudan International Innovation Centre Fudan University Shanghai 200438 China

The relationship between liver metabolic enzymes and the risk of dementia, as well as whether genetic factors can explain these potential associations, remains unclear. Based on data from 347,605 dementia-free British adults in the UK Biobank, we categorized participants into normal and abnormal liver metabolic enzymes groups according to serum liver metabolic enzyme levels and assessed the longitudinal risk of incident dementia and changes in brain structure across these groups. Subsequently, we employed a two-sample Mendelian randomization (MR) analysis to explore the influence of genetic factors and further investigated the potential role of confounding factors such as lifestyle. The results showed that abnormal liver metabolic enzymes were associated with a higher risk of dementia. However, MR analysis did not reveal a statistically significant association between liver metabolic enzymes and dementia, indicating that genetic factors may not primarily drive the association. Additionally, abnormal liver metabolic enzymes were associated with reduced gray matter (GM) volume, indicating that changes in GM volume may be a potential mechanism through which abnormal liver metabolic enzymes influences dementia risk. In summary, this study indicates that individuals with abnormal liver metabolic enzymes have a higher risk of dementia, and genetic susceptibility does not explain the observed higher risk of dementia among participants with abnormal liver metabolic enzymes.

关键词： Liver metabolic enzymes Dementia Genetics Brain structure

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Knowledge Circuits in Pretrained Transformers 38

Knowledge Circuits in Pretrained Transformers

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yao, Yunzhi Zhang, Ningyu Xi, Zekun Wang, Mengru Xu, Ziwen Deng, Shumin Chen, Huajun Zhejiang University China National University of Singapore NUS-NCS Joint Lab Singapore Zhejiang Key Laboratory of Big Data Intelligent Computing China

The remarkable capabilities of modern large language models are rooted in their vast repositories of knowledge encoded within their parameters, enabling them to perceive the world and engage in reasoning. The inner workings of how these models store knowledge have long been a subject of intense interest and investigation among researchers. To date, most studies have concentrated on isolated components within these models, such as the Multilayer Perceptrons and attention head. In this paper, we delve into the computation graph of the language model to uncover the knowledge circuits that are instrumental in articulating specific knowledge. The experiments, conducted with GPT2 and TinyLLAMA, have allowed us to observe how certain information heads, relation heads, and Multilayer Perceptrons collaboratively encode knowledge within the model. Moreover, we evaluate the impact of current knowledge editing techniques on these knowledge circuits, providing deeper insights into the functioning and constraints of these editing methodologies. Finally, we utilize knowledge circuits to analyze and interpret language model behaviors such as hallucinations and in-context learning. We believe the knowledge circuits hold potential for advancing our understanding of Transformers and guiding the improved design of knowledge editing. © 2024 Neural information processing systems foundation. All rights reserved.

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DRLO: Optimizing edge server placement in dynamic MEC scenarios using deep reinforcement learning

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Computer Networks 2025年 268卷

作者： Yingya Guo Cen Chen College of Computer and Data Science Fuzhou University PR China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University PR China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian PR China

As an emerging computing paradigm, Mobile Edge computing (MEC) significantly enhances user experience and alleviates network congestion by strategically deploying edge servers in close proximity to mobile users. However, the effectiveness of MEC hinges on the precise placement of these edge servers, a critical factor in determining the Quality of Experience (QoE) for mobile users. While existing studies predominantly focus on optimizing edge server placement in static scenarios, they often fall short when faced with user mobility, resulting in a degradation of QoE. To address this challenge, we propose an adaptive edge server placement approach that leverages Deep Reinforcement Learning (DRL) to select the base stations for placing edge servers in a dynamic MEC environment. Our objective is to minimize access delay by optimizing edge server placement for adapting to dynamic environment. To tackle the vast action space associated with edge server placement, we introduce a novel activation function in the actor neural network for efficient exploration. Furthermore, to enhance the adaptability of the derived edge server placement strategy, we meticulously design a new reward function, which takes into account the minimization of total access delay within dynamic MEC scenarios. Finally, to validate the effectiveness of our proposed method, extensive experiments were conducted using the Shanghai Telecom dataset. The results demonstrate that our approach outperforms baseline methods in minimizing access delay for users in dynamic MEC scenarios.

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Comparison of 2-acetylfuran,2-ethylfuran,and 2-methylfuran spherically expanding flame intrinsic instabilities

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Science China(Technological Sciences) 2022年第10期65卷 2388-2398页

作者： XU CangSu WANG QianWen LI XiaoLu OPPONG Francis LIU WeiNan College of Energy Engineering State Key Laboratory of Clean Energy UtilizationZhejiang UniversityHangzhou 310027China College of Mechanical and Electrical Engineering Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang ProvinceChina Jiliang UniversityHangzhou 310018China

The intrinsic instability of three furan derivatives, namely, 2-acetylfuran(AF), 2-ethylfuran(EF), and 2-methylfuran(MF), is experimentally and theoretically investigated and compared. A deep-learning-based end-to-end system is used to segment the cells and measure the crack length on the cellular flame front of the spherically expanding flame to quantitively investigate the flame morphology. Moreover, the flame front radius profile is quantitively investigated to shed light on the correlation between disturbance amplitude and flame radius. With the use of the linear stability model, the theoretical critical flame radius and Peclet number are obtained and compared to their corresponding experimental results to characterize the outset of cellularization and evaluate the flame instability propensity. Our analysis shows that compared with AF and EF, MF is prone to cellularization at a smaller flame radius and has a faster cell proliferation rate and crack length growth rate before reaching the hydrodynamic saturation limit. Meanwhile, the average area of saturated cells tends to be similar for the three furan derivatives. Furthermore,AF premixed flame does not exhibit cellularization before the flame radius is less than 45 mm in low initial pressure.

关键词： furan derivatives spherical expanding flame flame intrinsic instability linear stability theory

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CQUPT-FL: Cross-Domain Sharing and Security Awareness and Early Warning Platform of Health Science big data 1

CQUPT-FL: Cross-Domain Sharing and Security Awareness and Ea...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xiao, Yunpeng Zhang, Qunqing Zhao, Wanjing Li, Xufeng Peng, Jinhua Mo, Haonan Zhu, Haipeng Tang, Fei Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350358780

Federated learning (FL) is a rapidly growing research area in machine learning, but it is problematic. It has been questioned whether or not existing FL libraries are practical in the area of medical privacy. To address these issues, we developed the CQUPT-FL system. The system focuses on resolving the conflict between data integrity and medical data privacy protection in cross-domain and cross-institution collaborative analysis. CQUPT-FL supports distributed computing and stand-alone simulation computing methods. To deal with the problems of heterogeneity, data domain diversity, and effective data scarcity, we adopted key technologies such as multi-party secure computing and holistic information representation and studied user identification, privacy protection, and heterogeneous user alignment to achieve sustainable Cross-domain and cross-platform data fusion of letters. The goal of introducing the CQUPT-FL system is to improve the level of data privacy protection and enhance the data privacy protection mechanism, solve the machine learning dilemma in the field of medical privacy, and provide a reliable solution for cross-domain collaborative analysis. © 2023 IEEE.

关键词： Machine learning

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Multi-Granular Differential Evolution Algorithm for Multi-Agent Task Allocation 9

Multi-Granular Differential Evolution Algorithm for Multi-Ag...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Gao, Peng Liu, Qun Jiang, Haihuan Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350330076

The multi-agent task allocation presents a fundamental challenge in the field of multi-agent systems, especially in uncertain environments. Although extensive research has been conducted on the multi-agent task allocation in deterministic environments, discussions around the multi-agent task allocation in uncertain environments are relatively scarce. In reality, uncertain data is more common in practical decision-making processes. To address the multi-agent task allocation problem in uncertain environments, this study frames it as a noisy optimization problem and proposes a novel Multi-Granular Differential Evolution (MGDE) algorithm to solve it. MGDE combines the powerful differential evolution (DE) with the granular-ball computing which has high robustness in noise. The proposed MGDE is compared with other three state-of-the-art algorithms on 12 scenarios encompassing 6 agent and task quantity combinations and 2 uncertainty levels. Experimental results demonstrate the superior performance of MGDE. © 2023 IEEE.

关键词： Multi agent systems

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A Multi-granularity Graph-based Representation for Image Recognition 4

A Multi-granularity Graph-based Representation for Image Rec...

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4th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Lan, Danfeng Dai, Dawei Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350359589

Graph neural networks (GNNs) are specially designed to process graph data because of their ability to effectively capture complex structures and relationships within graphs. However, due to the pixel-based nature of images, representing them as graphs and then using graph networks poses challenges. Inspired by granular computing, we propose a method to compute multi-granularity structured features from image tiles, which facilitates efficient and direct graph-based representation learning. Specifically: (1) We design a method to reorganize all regional features into multiple tiles and construct a multi-granularity graph representation, which includes nodes and edges. (2) We use gray-level co-occurrence matrices to extract the image texture features to compensate for the information lost during graph representation. (3) We design a graph neural network classifier that combines the above graph features and image texture features. Experimental evaluations on two benchmark image classification datasets demonstrate the efficiency of our proposed method and its potential to establish a new paradigm for image processing. Codes are available at https://***/ddw2AIGROUP2CQUPT/MGRIR. © 2024 IEEE.

关键词： GNNs graph-based representation image classification multi-granularity

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