检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Sun, Jing Chen, Mingtao Sun, Jingxuan Rao, Shuying Zhang, Yaoyun Zhao, Sha Pan, Gang Jiang, Haiteng Li, Tao Affiliated Mental Health Center Hangzhou Seventh People's Hospital School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou310058 China College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine Integration State Key Laboratory of Brain-machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou311121 China School of Biomedical Informatics University of Texas at Dallas 800 W Campbell Rd RichardsonTX75080 United States College of Computer Science and Technology Zhejiang University Hangzhou310013 China NHC CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou310058 China

Background: In today's high-pressure society, anxiety disorders have come to the forefront of public attention. Distinguishing anxiety disorders accurately, conveniently, and effectively is of immense social value and scientific ***: Thirty-one generalized anxiety disorder (GAD) patients and twenty healthy controls (HC) were recruited in this study, and skin potential (SP) signals were collected during the resting state and task state using a single-channel portable device. Time-domain, spectral-domain, and nonlinear features were extracted from these two states, and feature coefficients were obtained by examining the relationship between individual features and clinical scales. Furthermore, single-mode features (resting state or task state), bimodal features (both two states with direct feature combination) and fused bimodal features (both two states with weighted feature coefficients) were fed into multiple machine learning algorithms including support vector machine (SVM), decision trees (DT), random forest (RF), and cat boost (CB) to distinguish between these two groups at the individual level. Model performances were systematically evaluated and compared using 10 fold ***: The weighted feature fusion approach achieved the best performance in individual GAD prediction (88.31%), comparing to single-mode features (resting state: 72.68%;task state: 79.07%) and direct feature combinations (84.57%). Conclusion: Our proposed weighted feature fusions from resting state and task state provides a promising tool to aid in early screening and clinical support in the GAD diagnosis. © 2024, The Authors. All rights reserved.

关键词： Decision trees

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Active Pointly-Supervised Instance Segmentation

arXiv

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arXiv 2022年

作者： Tang, Chufeng Xie, Lingxi Zhang, Gang Zhang, Xiaopeng Tian, Qi Hu, Xiaolin Department of Computer Science and Technology Institute for AI BNRist State Key Laboratory of Intelligent Technology and Systems Tsinghua University China Huawei Inc. China China IDG McGovern Institute for Brain Research Tsinghua University China

The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting, named active pointly-supervised instance segmentation (APIS), which starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object. The key of APIS is to find the most desirable points to maximize the segmentation accuracy with limited annotation budgets. We formulate this setting and propose several uncertainty-based sampling strategies. The model developed with these strategies yields consistent performance gain on the challenging MS-COCO dataset, compared against other learning strategies. The results suggest that APIS, integrating the advantages of active learning and point-based supervision, is an effective learning paradigm for label-efficient instance segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Budget control

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Toward multi-target self-organizing pursuit in a partially observable Markov game

arXiv

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arXiv 2022年

作者： Sun, Lijun Chang, Yu-Cheng Lyu, Chao Shi, Ye Shi, Yuhui Lin, Chin-Teng Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology China Centre for Artificial Intelligence CIBCI Lab Faculty of Engineering and Information Technology University of Technology Sydney Australia College of Computer and Information Science Southwest University China School of Information Science and Technology ShanghaiTech University China

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve the implicit coordination capabilities in search and pursuit. We model a self-organizing system as a partially observable Markov game (POMG) featured by large-scale, decentralization, partial observation, and noncommunication. The proposed distributed algorithm-fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning (MARL) method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that by decomposing the SOP task, FSC2 achieves superior performance compared with other implicit coordination policies fully trained by general MARL algorithms. The scalability of FSC2 is proved that up to 2048 FSC2 agents perform efficient multi-target SOP with almost 100% capture rates. Empirical analyses and ablation studies verify the interpretability, rationality, and effectiveness of component algorithms in FSC2. Copyright © 2022, The Authors. All rights reserved.

关键词： Intelligent agents

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Negatively Correlated Search for Constrained Optimization

Negatively Correlated Search for Constrained Optimization

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Congress on Evolutionary Computation

作者： Yuan Li Xiaofen Lu Xin Yao Department of Computer Science and Engineering The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA) School of Computer Science University of Birmingham Edgbaston Birmingham U.K.

Evolutionary algorithms (EAs) combined with constraint handling techniques (CHTs) are very effective in solving constrained optimization problems (COPs). Recently, negatively correlated search (NCS) has been shown to be powerful for real-world multimodal optimization problems (MMOP). However, there has been few works on utilizing NCS to solve COPs. In this paper, we present a novel constrained optimization evolutionary algorithm (COEA) named NCS-E which combined NCS with a new CHT ensemble method to deal with COPs. It integrates three complementary CHTs as voters, and individuals are first voted by each voter, then the individual with more weighted votes is considered better. In addition, the negative correlation information of the voted individuals is utilized to adaptively adjust the voting weights. To demonstrate the performance of NCS-E, NCS-E is compared with four NCS-based COEAs and two state-of-the-art methods, ∊MAgES and MAgES-VMCH, on 57 real-world COPs. The statistical results show that NCS-E exhibits the best performance among the compared methods. Moreover, NCS-E was applied to solve the Deep Neural Network pruning problem and compared with the state-of-the-art pruning method OLMP on three reference models. Empirical results show that the proposed method improves the performance of OLMP on all three DNN pruning tasks.

关键词：

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Transparent Registration-Based Encryption through Blockchain

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Distributed Ledger Technologies: Research and Practice 2023年第1期2卷 1-14页

作者： Qin Wang Rujia Li Qi Wang David Galindo Shiping Chen Yang Xiang CSIRO Data61 Sydney Australia Southern University of Science and Technology and University of Birmingham Nanshan District Shenzhen UK Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Nanshan District Shenzhen China University of Birmingham Edgbaston Birmingham UK Swinburne University of Technology Hawthorn Melbourne Australia

Garg et al. (TCC 2018) defined the notion of registration-based encryption (RBE) where the private key generator (PKG) is decoupled from key management and replaced by a key curator (KC). KC does not possess any cryptographic secrets and only plays the role of aggregating the public keys of all the registered users and updating the public parameters whenever a new user joins the system, which solves the key escrow issue. Notwithstanding, RBE still places a significant amount of trust in KC, whose actions are not accountable, e.g., it could secretly register multiple keys for already registered users. In this article, we propose a blockchain-based RBE framework, which provides total transparency and decentralization of KC by leveraging smart contracts. Our framework transfers the right of key management from KC to individual participants and keeps publicly upgradable parameters on-chain. We provide a basic construction that calculates the public parameter on-chain and an extended construction with better efficiency, which merely calculates the roots of trees on-chain. Our basic version is theoretically feasible, while the extended version is practically feasible. In particular, the enhanced scheme reduces computing complexity to a constant level. Our prototype implementation and evaluation results demonstrate that our extended construction is satisfactorily efficient.

关键词： Registration-based encryption smart contract transparency

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Constrained Reinforcement Learning for Dynamic Material Handling

arXiv

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arXiv 2023年

作者： Hu, Chengpeng Wang, Ziming Liu, Jialin Wen, Junyi Mao, Bifei Yao, Xin Southern University of Science and Technology Shenzhen China Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Trustworthiness Theory Research Center Huawei Technologies Co. Ltd Shenzhen China

As one of the core parts of flexible manufacturing systems, material handling involves storage and transportation of materials between workstations with automated vehicles. The improvement in material handling can impulse the overall efficiency of the manufacturing system. However, the occurrence of dynamic events during the optimisation of task arrangements poses a challenge that requires adaptability and effectiveness. In this paper, we aim at the scheduling of automated guided vehicles for dynamic material handling. Motivated by some real-world scenarios, unknown new tasks and unexpected vehicle breakdowns are regarded as dynamic events in our problem. We formulate the problem as a constrained Markov decision process which takes into account tardiness and available vehicles as cumulative and instantaneous constraints, respectively. An adaptive constrained reinforcement learning algorithm that combines Lagrangian relaxation and invalid action masking, named RCPOM, is proposed to address the problem with two hybrid constraints. Moreover, a gym-like dynamic material handling simulator, named DMH-GYM, is developed and equipped with diverse problem instances, which can be used as benchmarks for dynamic material handling. Experimental results on the problem instances demonstrate the outstanding performance of our proposed approach compared with eight state-of-the-art constrained and non-constrained reinforcement learning algorithms, and widely used dispatching rules for material handling. Copyright © 2023, The Authors. All rights reserved.

关键词： Materials handling

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An ant colony optimization algorithm for learning brain effective connectivity network from fMRI data

An ant colony optimization algorithm for learning brain effe...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinduo Liu Junzhong Ji Aidong Zhang Peipeng Liang Beijing Municipal Key Laboratory of Multimedia and Intelligent Software the College of Computer Science and Technology Beijing University of Technology Beijing China Department of Computer Science and Engineering University at Buffalo The State University of New York Buffalo America Beijing Key Lab of MRI and Brain Informatics Department of Radiology Capital Medical University Beijing China

ISBN: (纸本)9781509016129

Identifying brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is an important advanced subject in neuroinformatics in recent years, where the learning method based on bayesian networks (BN) has become a new hot topic in the field. This paper proposes a new method to learn the brain effective connectivity network structure by combining ant colony optimization (ACO) with BN method, named as ACOEC. In the proposed algorithm, a brain effective connectivity network is first mapped onto an ant, and then the ant colony optimization by simulating real ants looking for food is employed to construct network structures and finally an ant with the highest score is obtained as the optimal solution. The experimental results on simulated and real fMRI data sets show that the new method can not only accurately identify the connections and directions of the brain networks, but also quantitatively describe the connection strength of the brain networks, which has a good clinical application prospects.

关键词： Ant colony optimization Bayes methods Measurement Mathematical model brain modeling Optimization Electronic mail

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Surface Modification of (001) Facets Dominated TiO2 with Ozone for Adsorption and Photocatalytic Degradation of Gaseous Toluene

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Chinese Journal of Chemical Physics 2019年第5期32卷 611-619页

作者： Yue Yang Zhi-yu Wang Fan Zhang Yi Fan Jing-jing Dong Song Sun Chen Gao Jun Bao National Synchrotron Radiation Laboratory Collaborative Innovation Center of Chemistry for Energy Materials Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei 230029 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Chemistry and Chemical Engineering Anhui University Hefei 230601 China Beijing Advanced Sciences and Innovation center Chinese Academy of Sciences Beijing 101407 China

This study investigated the positive effect of surface modification with ozone on the photocatalytic performance of anatase TiO2 with dominated（001） facets for toluene *** performance of photocatalyst was tested on a home-made volatile organic compounds degradation system. The ozone modification, toluene adsorption and degradation mechanism were established by a combination of various characterization methods, in situ diffuse reflectance infrared fourier transform spectroscopy, and density functional theory *** surface modification with ozone can significantly enhance the photocatalytic degradation performance for toluene. The abundant unsaturated coordinated 5 c-Ti sites on（001）facets act as the adsorption sites for ozone. The formed Ti–O bonds reacted with H2O to generate a large amount of isolated Ti5 c-OH which act as the adsorption sites for toluene,and thus significantly increase the adsorption capacity for toluene. The outstanding photocatalytic performance of ozone-modified TiO2 is due to its high adsorption ability for toluene and the abundant surface hydroxyl groups, which produce very reactive OH·radicals under irradiation. Furthermore, the O2 generated via ozone dissociation could combine with the photogenerated electrons to form superoxide radicals which are also conductive to the toluene degradation.

关键词： Ozone modification (001) Facets Toluene degradation TiO2 In situ diffuse reflectance infrared fourier transform spectroscopy

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Emotion Recognition under Sleep Deprivation Using a Multimodal Residual LSTM Network

Emotion Recognition under Sleep Deprivation Using a Multimod...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Le-Yan Tao Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-like Computing and Machine Intelligence Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Brain Science and Technology Research Center Shanghai China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Emotion recognition under sleep deprivation is instructive for the study of mental disorders such as major depressive disorder. Previous studies on emotion recognition under sleep deprivation have been mainly based on psychological research techniques. In this paper, we introduce a multilayer weight-sharing multimodal residual LSTM network for emotion recognition under sleep deprivation. The advantage of our proposed method is that it allows for the combination of three different features: the electroencephalography (EEG) single-channel differential entropy (DE) features, EEG functional strength features with topological correlation connectivity, and eye movement features. The experiments under the conditions of sleep deprivation, sleep recovery and baseline are designed and conducted. The experimental results demonstrate that the proposed method significantly enhances the performance compared with the simple concatenation of the features of different modalities, and the best mean accuracies of 86.86% and 82.03% are achieved for four emotions (happiness, sadness, fear, and neutral) in subject-dependent and cross-subject emotion recognition tasks under 30 hours of sleep deprivation, respectively. The classification accuracy of the happiness emotion is obviously impaired under sleep deprivation, indicating that sleep deprivation impairs the stimulation of the happiness emotion, and one night of sleep recovery can reactivate the elicitation of the happiness emotion to the baseline level. Furthermore, we study the brain neural patterns of the four emotional states. The prefrontal area becomes less activated for the happiness emotion and sadness emotion in the gamma band under sleep deprivation, while the neural pattern of the fear emotion is highly robust with respect to sleep deprivation.

关键词： Feature extraction Electroencephalography Sleep Emotion recognition Correlation Physiology Task analysis

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Let's Play Music: Audio-Driven Performance Video Generation

Let's Play Music: Audio-Driven Performance Video Generation

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International Conference on Pattern Recognition

作者： Hao Zhu Yi Li Feixia Zhu Aihua Zheng Ran He Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap provides more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and interframe temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

关键词： Instruments Transforms Synchronization Data mining Task analysis Space heating

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