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arXiv 2024年

作者： Wang, Chaoqian Su, Qi Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Understanding the emergence of cooperation in spatially structured populations has advanced significantly in the context of pairwise games, but the fundamental theory of group-based public goods games (PGGs) remains less explored. Here, we provide theoretical conditions under which cooperation thrive in spatial PGGs on any population structure, which are accurate under weak selection. We find that PGGs can support cooperation across all kinds of model details and on almost all network structures in contrast to pairwise games. For example, a class of networks that would otherwise fail to produce cooperation, such as star graphs, are particularly conducive to cooperation in spatial PGGs. This fundamental advantage of spatial PGGs derives from reciprocity through second-order interactions, allowing local structures such as the clustering coefficient to play positive roles. We also verify the robustness of spatial PGGs on empirical networks where pairwise games cannot support cooperation, which implies that PGGs could be a universal interaction mode in real-world systems. © 2024, CC BY.

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Balanced Audiovisual dataset for Imbalance Analysis

arXiv

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arXiv 2023年

作者： Xia, Wenke Zhao, Xu Pang, Xincheng Zhang, Changqing Hu, Di Gaoling School of Artificial Intelligence Renmin University of China Beijing China School of Computer Science and Engineering Northeastern University Shenyang China College of Intelligence and Computing Tianjin University Tianjin China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

The imbalance problem is widespread in the field of machine learning, whichalso exists in multimodal learning areas caused by the intrinsic discrepancybetween modalities of samples. Recent works have attempted to solve themodality imbalance problem from algorithm perspective, however, they do notfully analyze the influence of modality bias in datasets. Concretely, existingmultimodal datasets are usually collected under specific tasks, where onemodality tends to perform better than other ones in most conditions. In thiswork, to comprehensively explore the influence of modality bias, we first splitexisting datasets into different subsets by estimating sample-wise modalitydiscrepancy. We surprisingly find that: the multimodal models with existingimbalance algorithms consistently perform worse than the unimodal one onspecific subsets, in accordance with the modality bias. To further explore theinfluence of modality bias and analyze the effectiveness of existing imbalancealgorithms, we build a balanced audiovisual dataset, with uniformly distributedmodality discrepancy over the whole dataset. We then conduct extensiveexperiments to re-evaluate existing imbalance algorithms and draw someinteresting findings: existing algorithms only provide a compromise betweenmodalities and suffer from the large modality discrepancy of samples. We hopethat these findings could facilitate future research on the modality imbalanceproblem. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach

The State of Charge Estimation of Lithium-ion Batteries Usin...

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Chinese Control and Decision Conference, CCDC

作者： Wei He Hongyan Ma Yingda Zhang Shuai Wang Jiaming Dou Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China National Virtual Simulation Experimental Center for Smart City Education Beijing China

ISBN: (纸本)9781665478977

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. In order to improve the estimation accuracy of SOC, this paper develops a SOC estimation model for a lithium-ion battery using a Particle Swarm Optimization-Extreme Learning Machine(PSO-ELM) algorithm. The PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM model. The inputs to the PSO-ELM model are the battery voltage, current, and temperature, and the output is the actual SOC values. The performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion batteries Temperature measurement Temperature distribution Neurons Estimation Particle measurements Battery charge measurement

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Dual Progressive Transformations for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2022年

作者： Huo, Dongjian Su, Yukun Wu, Qingyao School of Software and Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Nanyang Technological University Singapore Pazhou Lab Guangzhou China

Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current state-of-the-art CNN-based methods usually adopt Class-Activation-Maps (CAMs) to highlight the potential areas of the object, however, they may suffer from the part-activated issues. To this end, we try an early attempt to explore the global feature attention mechanism of vision transformer in WSSS task. However, since the transformer lacks the inductive bias as in CNN models, it can not boost the performance directly and may yield the over-activated problems. To tackle these drawbacks, we propose a Convolutional Neural Networks Refined Transformer (CRT) to mine a globally complete and locally accurate class activation maps in this paper. To validate the effectiveness of our proposed method, extensive experiments are conducted on PASCAL VOC 2012 and CUB-200-2011 datasets. Experimental evaluations show that our proposed CRT achieves the new state-of-the-art performance on both the weakly supervised semantic segmentation task the weakly supervised object localization task, which outperform others by a large margin. Code is available at https://***/huodongjian0603/crt Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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CRNN-Refined Spatiotemporal Transformer for Dynamic MRI reconstruction

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Computers in Biology and Medicine 2024年 182卷 109133-109133页

作者： Wang, Bin Lian, Yusheng Xiong, Xingchuang Han, Hongbin Liu, Zilong Center for Metrology Scientific Data National Institute of Metrology Beijing100029 China Key Laboratory of Metrology Digitalization and Digital Metrology State Administration for Market Regulation Beijing100029 China School of Printing and Packaging Engineering Beijing Institute of Graphic Communication Beijing102600 China Department of Radiology Peking University Third Hospital. Institute of Medical Technology Peking University Health Science Center. Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology Peking University Third Hospital Beijing100191 China

Magnetic Resonance Imaging (MRI) plays a pivotal role in modern clinical practice, providing detailed anatomical visualization with exceptional spatial resolution and soft tissue contrast. Dynamic MRI, aiming to capture both spatial and temporal characteristics, faces challenges related to prolonged acquisition times and susceptibility to motion artifacts. Balancing spatial and temporal resolutions becomes crucial in real-world clinical scenarios. In the realm of dynamic MRI reconstruction, while Convolutional Recurrent Neural Networks (CRNNs) struggle with long-range dependencies, CRNNs require extensive iterations, impacting efficiency. Transformers, known for their effectiveness in high-dimensional imaging, are underexplored in dynamic MRI reconstruction. Additionally, prevailing algorithms fall short of achieving superior results in demanding generative reconstructions at high acceleration rates. This research proposes a novel approach for dynamic MRI reconstruction, named CRNN-Refined Spatiotemporal Transformer Network (CST-Net). The spatiotemporal Transformer initiates reconstruction, modeling temporal and spatial correlations, followed by refinement using the CRNN. This integration mitigates inaccuracies caused by damaged frames and reduces CRNN iterations, enhancing computational efficiency without compromising reconstruction quality. Our study compares the performance of the proposed CST-Net at 6 × and 12 × undersampling rates, showcasing its superiority over existing algorithms. Particularly, in challenging 25× generative reconstructions, the CST-Net outperforms current methods. The comparison includes experiments under both radial and Cartesian undersampling patterns. In conclusion, CST-Net successfully addresses the limitations inherent in existing generative reconstruction algorithms, thereby paving the way for further exploration and optimization of Transformer-based approaches in dynamic MRI reconstruction. Code and datasets can be available: https://

关键词： Recurrent neural networks

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Efficient meta neural heuristic for multi-objective combinatorial optimization 23

Efficient meta neural heuristic for multi-objective combinat...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jinbiao Chen Jiahai Wang Zizhen Zhang Zhiguang Cao Te Ye Siyuan Chen School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.

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A Unified Channel Estimation Framework for Stationary and Non-Stationary Fading Environments

A Unified Channel Estimation Framework for Stationary and No...

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作者： Shi, Qi Liu, Yangyu Zhang, Shunqing Xu, Shugong Lau, Vincent K. N. Key Laboratory of Specialty Fiber Optics and Optical Access Networks School of Information and Communication Engineering Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China The Hong Kong University of Science and Technology Hong Kong Hong Kong

Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future. © 1972-2012 IEEE.

关键词： Channel estimation

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Speaker Extraction with Co-Speech Gestures Cue

arXiv

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arXiv 2022年

作者： Pan, Zexu Qian, Xinyuan Li, Haizhou The Integrative Sciences and Engineering Programme The Institute of Data Science National University of Singapore 119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China The School of Data Science The Chinese University of Hong Kong Shenzhen518172 China The University of Bremen 28359 Germany

Speaker extraction seeks to extract the clean speech of a target speaker from a multi-talker mixture speech. There have been studies to use a pre-recorded speech sample or face image of the target speaker as the speaker cue. In human communication, co-speech gestures that are naturally timed with speech also contribute to speech perception. In this work, we explore the use of co-speech gestures sequence, e.g. hand and body movements, as the speaker cue for speaker extraction, which could be easily obtained from low-resolution video recordings, thus more available than face recordings. We propose two networks using the co-speech gestures cue to perform attentive listening on the target speaker, one that implicitly fuses the co-speech gestures cue in the speaker extraction process, the other performs speech separation first, followed by explicitly using the co-speech gestures cue to associate a separated speech to the target speaker. The experimental results show that the co-speech gestures cue is informative in associating with the target speaker. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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Design of Offline Analysis System for Remote Sensing data Service Based on Hive

Design of Offline Analysis System for Remote Sensing Data Se...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Hongkuo Zhang Jiaxiang Zhang Meng Tian Baojun Qiao School of Computer and Information Engineering Heanan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9781665406314

With the wide application of remote sensing data service platform, the platform accumulates more and more data, and the remote sensing users' demand for remote sensing data service becomes more and more diverse. Therefore, how to deal with remote sensing service data, make statistics and analyze its potential value, and meet the diverse needs of remote sensing users becomes particularly important. Based on big data technology, this paper firstly collects and stores remote sensing user behavior data and remote sensing service platform business data, then uses Hive to build offline data warehouse, performs dimensional modeling and hierarchical design of the data, and finally obtains relevant index data values by statistical analysis. Through the statistical analysis of remote sensing service data can not only provide data support for active service mode, but also provide support for the decision of remote sensing application department managers.

关键词： Process design Analytical models Statistical analysis data warehouses data models Indexes Intelligent systems

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Neural Adaptive Dynamic Surface Control of PMSMs with Input Saturation and output constraint

Neural Adaptive Dynamic Surface Control of PMSMs with Input ...

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Chinese Automation Congress (CAC)

作者： Dongchao Lv Shaobo Li Tao Zhang Fengbin Wu Menghan Li Chaojie Zheng School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory Of Public Big Data Guizhou University Guiyang China School of Computer Science and Technology Guizhou University Guiyang China

This paper discusses an adaptive neural tracking control of permanent magnet synchronous motors subject to input saturation and output constraint. The difficulty is to consider output constraints and input saturation. Firstly, the adaptive dynamic surface control design process is systematized by embedding many existing tools into the classical backstepping framework. Then, a nonlinear transformation function is proposed to transform the output constrained system into an unconstrained system. Furthermore, using radial basis function neural networks to Process the unknown terms, the Gaussian error function is utilized to describe the continuously differentiable asymmetric saturation nonlinearity. It turns out that all signals in the proposed scheme are bounded. The simulation results are provided to further show the feasibility of the proposed method.

关键词： Control design Simulation Process control Radial basis function networks Transforms Permanent magnet motors Stability analysis

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