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The Journal of Machine Learning Research 2024年第1期25卷 12905-12937页

作者： Shaojie Li Yong Liu Gaoling School of Artificial Intelligence Renmin University of China Beijing China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

The concentration of measure phenomenon serves an essential role in statistics and machine learning. This paper gives bounded difference-type concentration and moment inequalities for general functions of independent random variables with heavy tails. A general framework is presented, which can be used to prove inequalities for general functions once the moment inequality for sums of independent random variables is established. We illustrate the power of the framework by showing how it can be used to derive novel concentration and moment inequalities for bounded, Bernstein's moment condition, weak-exponential, and polynomial-moment random variables. Furthermore, we give potential applications of these inequalities to statistical learning theory.

关键词： concentration inequality bounded difference statistical learning theory

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Federated Learning with Common Representation Learning Criterion and Personalized Predictor

Federated Learning with Common Representation Learning Crite...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Wenzhong Xie, Zaipeng Yu, Bingzhe Qu, Zhihao Zhang, Yufeng Cao, Hongli Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China College of Computer and Information Hohai University Nanjing China Southeast University Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Nanjing China

ISBN: (纸本)9798350337020

Federated learning (FL) enables model training on decentralized devices while preserving data privacy. However, data heterogeneity poses a significant challenge to FL, and various approaches have been proposed to address it. Existing research has mainly focused on either enhancing global models or customizing personalized models for clients. This paper proposes a novel approach, FedCRC, that decouples the machine learning model into a representation extractor and predictor. This enables us to enhance both generalization and personalization, thereby addressing the challenge of data heterogeneity in FL. The approach employs a stable global predictor to unify the representation learning criterion during the training of the representation extractor. Additionally, a personalized predictor is trained for each client to achieve a personalized model tailored to the local data distribution. Our FedCRC algorithm was evaluated on multiple benchmark datasets with varying distributions, covering diverse settings. Extensive experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： data Heterogeneity Federated Learning Personalized Predictor Representation Learning

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Class-Dynamic and Hierarchy-Constrained Network for Entity Linking 28th

Class-Dynamic and Hierarchy-Constrained Network for Entity...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Kehang Liu, Qi Zhang, Kai Liu, Ye Tao, Hanqing Huang, Zhenya Chen, Enhong Auhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9783031306716

Entity Linking (EL) aims to map mentions in a text to corresponding entities in a knowledge base. Existing EL methods usually rely on sufficient labeled data to achieve the best performance. However, the massive investment in data makes EL systems viable only to a limited audience. There is ample evidence that introducing entity types can provide the model prior knowledge to maintain the model performance in low-data regimes. Unfortunately, current low-data EL methods usually employ entity types by rule constraints, which are in a shallow manner. Furthermore, they usually ignore fine-grained interaction between mention and its context, resulting in insufficient semantic information of mention representation in low-data regimes. To this end, we propose a Class-Dynamic and Hierarchy-Constrained Network (CDHCN) for entity linking. Specifically, we propose a dynamic class scheme to learn a more effective representation for each entity type. Besides, we formulate a hierarchical constraint scheme to reduce the matching difficulty of the given mention and corresponding candidate entities by utilizing entity types. In addition, we propose an auxiliary task called mention position prediction (MPP) to obtain an informative mention representation in low-data regimes. Finally, extensive in-domain and out-of-domain experiments demonstrate the effectiveness of our method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge based systems

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A Multi-Objective Artificial Physics Optimization Algorithm Based on Two-Phase Search

A Multi-Objective Artificial Physics Optimization Algorithm ...

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International Symposium on Computer Science and Intelligent Controls (ISCSIC)

作者： Huihua Zhang Liping Xie Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

Aiming at the problems of insufficient utilization of information about elite particles in archive and instability of particle motion in the population in the multi-objective artificial physics optimization algorithm (MOAPO) in solving multiobjective optimization problems, A multi-objective artificial physics optimization algorithm based on two-phase search (TPMOAPO) is proposed. To begin with, the algorithm improves the calculation of the mass of particles, so that the strength and weakness of the particles can be accurately transformed into the corresponding masses while improving the efficiency of particle mass calculation. Next, a two-phase search strategy is proposed, which makes the algorithm have strong exploration ability in the first phase, and the second phase gradually enhances the exploitation capability with iterations, which solves the problem of instability motion of particles in the search process. Finally, the simulated binary crossover (SBX) and polynomial-based mutation (PM) operators are adopted in the archive to further enhance the search capability of the algorithm. For verifying the performance of TP-MOAPO, 21 benchmark functions were selected to compare with the classical multi-objective particle swarm optimization algorithms: MOPSO, dMOPSO, SMPSO, MMOPSO, and NMPSO, and the experimental results show the superiority of TP-MOAPO in these functions.

关键词： Measurement Computer science Benchmark testing Search problems Particle swarm optimization Optimization Physics Intelligent control Convergence

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Dynamical relaxation of a long-range Kitaev chain

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Physical Review B 2024年第9期109卷 094309-094309页

作者： Yu-Huan Huang Yin-Tao Zou Chengxiang Ding School of Microelectronics and Data Science Anhui University of Technology Maanshan 243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan 243002 China

We study the universal real-time relaxation behaviors of a long-range Kitaev chain following a quench, and the results are compared to a short-range quantum XY chain. Our research includes both noncritical and critical quenches. In the case of noncritical quench, i.e., neither the initial state nor the postquench Hamiltonian is at a critical point of the equilibrium phase transition, a quench to the commensurate phase or incommensurate phase gives a scaling of t−3/2 or t−1/2, respectively, which is the same as the counterpart of the short-range quantum XY model. However, for a quench to the boundary line between the commensurate and incommensurate phases, the scaling law t−μ may be different from the t−3/4 law of the counterpart of the short-range quantum XY model. More interestingly, the decaying exponent μ may depend on the choice of the parameters of the postquench Hamiltonian because of the different asymptotic behaviors of the energy spectrum. Furthermore, in certain cases, the scaling behavior may be outside the range of predictions made by the stationary phase approximation, because an inflection point emerges in the energy spectrum. For the critical quench, i.e., the initial state or the postquench Hamiltonian is at a critical point of equilibrium phase transition, the aforementioned scaling law t−μ may be changed because of the gap-closing property of the energy spectrum of the critical point.

关键词： Quantum quench Spin relaxation Exact solutions for many-body systems Integrable systems

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A Survey of Object Tracking Methods Based on Deep Learning

A Survey of Object Tracking Methods Based on Deep Learning

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Yi, Yang Meng, Zijian Tian, Guixiong School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Information Science Xinhua College of Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9789811669620

The object tracking problem is usually represented as estimating the state of an arbitrary target in a video only given its state in the initial frame. Many recent object tracking methods based on deep learning have achieved advanced performance. In general, most object tracking methods can be divided into two types. One family of trackers such as DiMP, ATOM learn a discriminative classifier, which adopt online updating strategy, to distinguish the target object from the background. In the process of tracking, these methods need to update the model parameters using reference frames and states. The other family of trackers represented by Siamese network use the reference state and the current frame as the input of the model without changing or updating the parameters. These trackers calculate the cross-corrlation between the reference frame and the current frame to complete the object tracking task. Both methods usually divide object tracking task into two subtasks: classification and estimation. The target location is maintained by target classification, and the accurate target boundary box is obtained by target estimation. In this paper, we will investigate some state-of-the-art object tracking methods using deep learning in recent years. These methods will be introduced in three different emphases: classification, estimation, both. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Indoor Navigation Mechanism based on 5G network for Large Parking Garage 26

Indoor Navigation Mechanism based on 5G network for Large Pa...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Xiaolong Ding, Qun Lin, Licheng Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350331684

With the increase of car ownership in China, the problem of difficult parking in cities has become more and more serious. In large parking garage, finding an ideal parking space has become a daily problem for people;especially during the peak usage period of parking garage, a large number of vehicles will be driven into the parking garage, which will bring congestion, and at the same time, the demand of a large number of users in the area will lead to network congestion under 4G network, and users cannot access the network to check and find parking spaces. Therefore, this paper proposes a global optimal navigation mechanism with 5G network based on the above mentioned problems by analyzing the congestion situation during the peak usage of the parking garage. This mechanism uses an optimal parking space selection model and a global optimal scheduling model that can avoid congestion. Based on the model proposed in this paper, we construct a navigation planning system based on mobile, front-end and back-end. With the ultra-low latency, ultrahigh transmission efficiency and reliability of 5G network, the mobile port can select the optimal parking space according to the user's preference, and the back-end can show the parking space and congestion in the parking garage in real time according to the actual situation of the parking garage. Finally, the performance of the model and system proposed in this paper is verified through experiments. © 2023 IEEE.

关键词： 5G mobile communication systems

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GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal Attention Graph Convolution Networks

GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lu, Jin Xie, Zaipeng Chen, Jiayu Li, Maohua Xu, Chenghong Cao, Hongli Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China College of Computer and Information Hohai University Nanjing China Southeast University Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Nanjing China

ISBN: (纸本)9798350337020

Runoff prediction is essential for flood forecasting, irrigation planning, and sustainable water resource management. However, accurate predictions can be challenging due to the involvement of multiple variables. This paper presents a novel Graph Convolution-based Spatial-temporal Attention LSTM Multi-Task learning (GC-SALM) model for accurate runoff predictions. Our approach combines a multilayer neural network and an attention mechanism for enhanced generalization performance. The GC-SALM model employs spatial attention and graph convolutional networks to discern local and global spatial patterns, while temporal attention and LSTM are utilized to capture temporal characteristics within extended sequences. Experimental results reveal that the proposed model outperforms six state-of-the-art methods in runoff prediction and flow calibration, emphasizing its potential for real-world hydrological applications. © 2023 IEEE.

关键词： Graph convolutional networks Multi-Task Learning Runoff prediction Spatial and temporal modeling

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The identification for time-varying parameter and noise of tuberculosis with seasonal changes based on data-behavior-driven system

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The European Physical Journal Special Topics 2024年 1-17页

作者： Wei, Kexin Ma, Shaojuan School of Mathematics and Information Science North Minzu University Yinchuan China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan China

Time-varying and seasonal parameter inversion in the mathematical model of infectious diseases and uncertainty quantization based on actual data have great significance for real quantitative transmission process. In this study, the behavior-driven mathematical model of infectious diseases and the data-driven parameter identification method are combined to quantify the transmission law of tuberculosis (TB). To begin with, according to the characteristics of TB transmission, the TS-SID model with time-varying is established. Then, the improved identification algorithm is proposed to track the fluctuation of disease infection rate and mortality rate considering the seasonal influence. Meanwhile, focusing on the influence of noise on the spread of diseases, noise reduction and uncertain quantization are carried out on the data to identify the noise distribution. In addition, predict the denoised sequence and superimpose the noise distribution, which can improve the rationality of prediction. Finally, the numerical comparison shows that seasonal time-varying tracking is good for grasping and predicting the disease evolution.

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Unified Uncertainty Estimation for Cognitive Diagnosis Models 24

Unified Uncertainty Estimation for Cognitive Diagnosis Model...

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33rd ACM Web Conference, WWW 2024

作者： Wang, Fei Liu, Qi Chen, Enhong Liu, Chuanren Huang, Zhenya Wu, Jinze Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Business Analytics and Statistics The University of Tennessee Knoxville United States IFLYTEK Research State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users' proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis. © 2024 ACM.

关键词： Large datasets

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