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Neural computing and Applications 2018年第10期32卷 6415-6415页

作者： Ren, Fuji Dong, Yindong Wang, Wei School of Computer and Information Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Graduate School of Advanced Technology and Science University of Tokushima Tokushima Japan

The authors quite agree that the AsI model is first developed by Panagiotis Petrantonakis from references [11] and [18] in their thesis.

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Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition

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arXiv 2020年

作者： Wu, Xun Zheng, Wei-Long Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Clinical Data Animation Center Department of Neurology Massachusetts General Hospital Harvard Medical School 55 Fruit Street BostonMA United States

Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP. The strength feature achieves the best classification performance and outperforms the state-of-the-art differential entropy feature based on single-channel analysis for the EEG signals. The experimental results reveal that distinct functional connectivity patterns are exhibited for the five emotions of disgust, fear, sadness, happiness, and neutrality. Furthermore, we construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals using deep canonical correlation analysis. The classification accuracies of multimodal emotion recognition are 95.08 ± 6.42% on the SEED dataset, 84.51 ± 5.11% on the SEED-V dataset, and 85.34 ± 2.90% and 86.61 ± 3.76% for arousal and valence on the DEAP dataset, respectively. The results demonstrate the complementary representation properties of the EEG functional connectivity network features with eye movement data. In addition, we find that the brain networks constructed with fewer channels, i.e., 18 channels, achieve comparable performance with that of the 62-channel network with respect to multimodal emotion recognition and enable easier setups for BCI systems in real scenarios. Cop

关键词： Electrophysiology

Evolutionary dynamics in stochastic nonlinear public goods games

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arXiv 2024年

作者： Zhu, Wenqiang Wang, Xin Wang, Chaoqian Liu, Longzhao Hu, Jiaxin Zheng, Zhiming Tang, Shaoting Zheng, Hongwei Dong, Jin School of Artificial Intelligence Beihang University Beijing100191 China Key laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100094 China Beijing Academy of Blockchain and Edge Computing Beijing100085 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China State Key Lab of Software Development Environment Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China School of Mathematical Sciences Dalian University of Technology Dalian116024 China Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Faculty of Business The Hong Kong Polytechnic University Hung Hom Kowloon999077 Hong Kong

Understanding the evolution of cooperation in multiplayer games is of vital significance for natural and social systems. An important challenge is that group interactions often leads to nonlinear synergistic effects. However, previous models mainly focus on deterministic nonlinearity where the arise of synergy or discounting effect is determined by certain conditions, ignoring uncertainty and stochasticity in real-world systems. Here, we develop a probabilistic framework to study the cooperative behavior in stochastic nonlinear public goods games. Through both analytical treatment and Monte Carlo simulations, we provide comprehensive understanding of social dilemmas with stochastic nonlinearity in both well-mixed and structured populations. We find that increasing the degree of nonlinearity makes synergy more advantageous when competing with discounting, thereby promoting cooperation. Interestingly, we show that network reciprocity loses effectiveness when the probability of synergy is small. Moreover, group size exhibits nonlinear effects on group cooperation regardless of the underlying structure. Our findings thus provide novel insights into how stochastic nonlinearity influences the emergence of prosocial behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear simulations

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations

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arXiv 2024年

作者： Lee, Jae Yong Schotthöfer, Steffen Xiao, Tianbai Krumscheid, Sebastian Frank, Martin Center for Artificial Intelligence and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States State Key Laboratory of High Temperature Gas Dynamics Centre for Interdisciplinary Research in Fluids Institute of Mechanics Chinese Academy of Sciences Beijing China School of Engineering Science University of Chinese Academy of Sciences Beijing China Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany

This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e.g., in radiation transport. We investigate the capabilities of the Deep Operator network (DeepONet) approach to modelling the high dimensional collision operator of the linear kinetic equation. This integral operator has crucial analytical structures that a surrogate model, e.g., a DeepONet, needs to preserve to enable meaningful physical simulation. We propose several DeepONet modifications to encapsulate essential structural properties of this integral operator in a DeepONet model. To be precise, we adapt the architecture of the trunk-net so the DeepONet has the same collision invariants as the theoretical kinetic collision operator, thus preserving conserved quantities, e.g., mass, of the modeled many-particle system. Further, we propose an entropy-inspired data-sampling method tailored to train the modified DeepONet surrogates without requiring an excessive expensive simulation-based data generation. © 2024, CC BY-NC-SA.

关键词： Kinetic energy

Learning with Group Noise

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arXiv 2021年

作者： Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia

machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object (cf. Figure 1) w.r.t. the target, meanwhile considering the Non-IID characteristics among objects in the group. Only the most confident object is considered to learn the model, so that the fine-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

Performance of Flexible Non-contact Electrodes in Bioelectrical Signal Measurements

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Performance of Flexible Non-contact Electrodes in Bioelectri...

IEEE International Conference on Real-time computing and Robotics (RCAR)

作者： Xin Wang Shuting Liu Mingxing Zhu Xiaochen Wang Zhenzhen Liu Yanbing Jiang Dan Wang Peng Li Oluwarotimi Williams Samuel Wanqing Wu Shixiong Chen Guanglin Li Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China School of Graduate School at Shenzhen Tsinghua University Shenzhen Guangdong China School of Pharmacy and Bioengineering Chongqing University of Technology Chongqing China School of Electrical and Information Engineering Tianjin University Tianjin China Third Affiliated Hospital of Sun Yat-Sen University Guangzhou Guangdong China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Electrocardiography (ECG) and electroencephalograph (EEG) are two physiological signals closely related with human health, and their measurements can help to detect various diseases in the clinic. Long-term physiological signal monitoring is usually needed to catch any possible diseases which may be missed in conventional short-term monitoring. Currently, wet electrodes are widely used in the clinic to obtain physiological signals. However, wet electrodes require adhesives or conductive gels to improve the quality of recorded signals, and may introduce skin irritation. Based on the Capacitive coupling principle, this study proposed a flexible non-contact electrode that can collect physiological electrical signals without direct contacts with the skin. Moreover, it can attach firmly than common conductive electrodes because of its flexibility. The results showed that the proposed non-contact electrode could reliably obtain high quality physiological signals, and the performance could be comparable to the conventional wet electrodes. Alpha waves could be clearly detected when the subjects closed their eyes, with the non-contact electrodes placed over the hair. This study might provide a reliable solution for long-term monitoring of physiological signals for those whose disorders could not be diagnosed over a short period of time.

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Deep high-order supervised hashing

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Optik 2019年 180卷 847-857页

作者： Jing Dong Cheng Qiu Le Sun Jian Xin Zhang Christian Desrosiers Bin Liu Jian Lu Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian 116622 China School of Information and Communication Engineering Dalian University of Technology Dalian 116024 China Laboratory for Imagery Vision and Artificial Intelligence (LIVAI) École de technologie supérieure (ETS) Montreal H3C1K3 Canada International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China

Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised hashing (DHoSH) architectures based on point-wise labels (DHoSH-PO) and pair-wise labels (DHoSH-PA), respectively. The core of DHoSH is a trainable layer of bilinear features incorporated into existing deep convolutional neural network (CNN) for end-to-end learning. This layer captures the interaction of local features by bilinear pooling, using correlation to model dependencies of features within the same layer or cross-correlation for features across different layers of the CNN. Furthermore, DHoSH systematically employs the high-order statistics of features of multiple layers. Extensive experiments on commonly used image retrieval benchmarks show that our DHoSH-PO and DHoSH-PA models yield improved accuracy over its first-order counterparts and achieve effective performance for these benchmarks.

关键词： High-order statistics Supervised hashing Bilinear pooling CNN Image retrieval

The Effects of Adaptive Kalman Filtering on the Morphology of Auditory Brainstem Response in Real Active Behavior: A Pilot Study

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The Effects of Adaptive Kalman Filtering on the Morphology o...

IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Xin Wang Haoshi Zhang Yangiie Xu Jingqian Tan Yuchao He Yuting Qiu Ziming Huang Yuan Tao Mingiiang Wang Mingxing Zhu Shixiong Chen Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and the Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg 1855 Luxembourg Luxembourg Department of Otorhinolaryngology Head and Neck Surgery The Eighth Affiliated Hospital of Sun Yat-sen University Shenzhen Guangdong China School of Environment and Energy Peking University Shenzhen Graduate School Shenzhen Guangdong China Department of Cardiothoracic Surgery Wuhan Asia Heart Hospital Affiliated to Wuhan University of Science and Technology Wuhan Hubei China Department of Otorhinolaryngology Peking University Shenzhen Hospital Shenzhen Guangdong China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen Guangdong China

Auditory, which served as one of the five sense systems, play a vital role in human beings’ daily life. Among the many auditory detection techniques, Auditory Brainstem Response (ABR) is widely chosen and studied for its convenience and objectivity. The averaging (Ave) technique is the currently applied method to extract ABR from the EEG signals and is regarded as the gold standard in the clinic. However, the Ave technique is not suitable for noisy condition, like active behavioral condition, which requires the subjects to keep stay during the whole ABR test and is therefore not suitable for newborn. To extract ABR signals from the real active behavioral condition, an adaptive kalman filter (AKF) technique was proposed and systematically investigated from the morphology aspect in two conditions, namely rest and active behavioral (chewing) conditions. The results showed that in rest condition, the ABR signal obtained by the AKF method was highly similar to that of the gold standard method, and the latencies and amplitudes of characteristics waves were also alike. Moreover, we analyzed the latencies and amplitudes of the characteristics waves and CC between the standard ABR and the different method-based ABR. The analyses showed that the AKF had the potential on the extraction of ABR in active behavioral condition. The AKF method provides a new way to robust denoise, and opens a window for ABR acquisition in active behavioral condition, making ABR acquisition in daily life more possible.

关键词： Pediatrics Filtering Morphology Brainstem Electroencephalography Behavioral sciences Kalman filters

Asynchronous Functional Brain Network Construction with Spatiotemporal Transformer for MCI Classification

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TechRxiv

TechRxiv 2023年

作者： Tang, Xiang Zhang, Jianjia Wu, Xiaotong Zhou, Luping Wang, Lei Wu, Weiwen Shen, Dinggang The School of Biomedical Engineering Sun Yat-sen University China School of Electrical and Information Engineering University of Sydney Australia School of Computing and Information Technology University of Wollongong Australia The School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Company Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Construction and analysis of functional brain networks (FBNs) with rs-fMRI is a promising method to diagnose functional brain diseases. Nevertheless, the existing methods suffer from several limitations. First, the functional connectivities (FCs) of the FBN are usually measured by the temporal co-activation level between rs-fMRI time series from regions of interest (ROIs). While enjoying simplicity, the existing approach implicitly assumes simultaneous co-activation of all the ROIs, and models only their synchronous dependencies. However, the functional coactivation is not necessarily always synchronous due to the time lag of information flow and cross-time interactions between ROIs. Therefore, it is desirable to model the asynchronous functional interactions. Second, the traditional methods usually construct FBNs at individual level for feature extraction and classification, leading to large variability and degraded diagnosis accuracy when modeling asynchronous FBN. Third, the FBN construction and analysis are conducted in two independent steps without joint alignment for the target diagnosis task. To address the first limitation, this paper proposes an effective sliding-windowbased method to model spatiotemporal FCs in Transformer. Regarding the second limitation, we propose to learn common and individual FBNs adaptively with the common FBN as prior knowledge, thus alleviating the variability and enabling the network to focus on the individual diseasespecific asynchronous FCs. To address the third limitation, the common and individual asynchronous FBNs are built and analyzed by an integrated network, enabling end-to-end training and improving the flexibility and discriminativity. The effectiveness of the proposed method is consistently demonstrated on three data sets for MCI diagnosis. © 2023, CC BY-NC-SA.

关键词： Computer aided diagnosis

Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

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arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection