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K-Means Clustering-based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10250-10254页

作者： Luefeng Chen Kuanlin Wang Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada Tokyo Institute of Technology Yokohama 226-8502 Japan

Emotion is an important part of human interaction. Emotional recognition can greatly promote human-centered interaction techniques. On this basis, multimodal feature fusion can effectively improve the emotion recognition rate. However, in the multimodal feature fusion at the feature level, most of the methods do not consider the intrinsic relationship between different modes. Only the fusion of analysis and transformation of the feature matrices of different modes does not make better use of modal differences to improve the recognition rate. This problem led us to propose feature fusion method based on K-Means clustering and kernel canonical correlation analysis (KCCA). Clustering makes the classification of features not classified by mode, but by the degree of influence on emotional labels, thus positively affecting the results of KCCA. The experimental results obtained on the Savee database show that the proposed K-Means based KCCA improves overall classification performance and produces higher recognition rate than that of the state of art methods, such as the Informed Segmentation and Labeling Approach.

关键词： Emotion Recognition K-Means Clustering Kernel Canonical Correlation Analysis Feature Fusion

RRNet: Relational reasoning network with parallel multi-scale attention for salient object detection in optical remote sensing images

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arXiv 2021年

作者： Cong, Runmin Zhang, Yumo Fang, Leyuan Li, Jun Zhang, Chunjie Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong College of Electrical and Information Engineering Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518000 China Guangdong Provincial Key Laboratory of Urbanization and Geosimulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China Key Laboratory of Information Perception and Systems for Public Security of MIIT Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively. https://***/proj_***. © 2021, CC BY-NC-SA.

关键词： Object detection

Optimizing network resource allocation with graph pointer neural network in large-scale AI systems

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Digital Communications and Networks 2024年

作者： Peiying Zhang Yilin Li Athanasios V. Vasilakos Konstantin Igorevich Kostromitin Jianyong Zhang Mohsen Guizani Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710071 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250013 China Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Physics of Nanoscale Systems South Ural State University Chelyabinsk 454080 the Russian Federation Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Key Lab of All Optical Network and Advanced Telecommunication of EMC Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi 999041 United Arab Emirates

For increasingly complex communication demands of large-scale AI communication systems, the Space-Air-Ground Integrated Network (SAGIN) better caters to demands but also raises concerns about resource scarcity and diversity. This paper innovatively combines Graph Pointer Neural Networks (GPNN) and Reinforcement Learning (RL) to enhance resource allocation efficiency. The method leverages the advantages of GPNN in handling graph data and RL in optimizing decisions in dynamic environments. It also targets the optimization goal of maximizing resource allocation while minimizing deployment latency. This paper begins by modeling SAGIN and elucidating the SAGIN logical architecture based on Software-defined Networking (SDN). Subsequently, it introduces an SFC deployment algorithm aimed at joint optimization of resource allocation and latency. The algorithm leverages GPNN and RL to deploy virtual nodes and links, with the goal of optimizing resource allocation and deployment latency. Experiment findings conclusively demonstrate that the efficacy of proposed algorithm in effectively weighing limited heterogeneous resources and minimum mapping delay. Notably, when compared to three other SFC mapping algorithms MLRL, NFVdeep, and RL, the proposed algorithm consistently outperforms them, with an average improvement of 10.17% in long-term average reward/cost, 11.21% in link resource utilization ratio, 15.34% in node resource utilization ratio, and 16.38% in acceptance ratio.

关键词： Space-air-ground integrated network Topological structure Reinforcement learning Service function chain Graph pointer neural network

Thermoelectric properties of layered ternary telluride Nb3SiTe6

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Physical Review Materials 2020年第9期4卷 094205-094205页

作者： Yahui Pang Emad Rezaei Dongyun Chen Si Li Yu Jian Qinsheng Wang Zhiwei Wang Junxi Duan Mona Zebarjadi Yugui Yao Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Micronano Centre Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing 100081 China Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia 22932 USA School of Physics and Electronics Hunan Normal University Changsha Hunan 410081 China Department of Materials Science and Engineering University of Virginia Charlottesville Virginia 22932 USA

We report the study of the thermoelectric properties of layered ternary telluride Nb3SiTe6. The temperature dependence of the thermoelectric power (TEP) evolves from nonlinear to linear when the thickness of the devices is reduced, consistent with the suppression of electron-phonon interaction caused by quantum confinement. The magnitude of TEP strongly depends on the hole density. It increases with decreasing hole density when the hole density is low, as observed in ionic-liquid-gated thin flakes. However, the device with the largest hole density possesses the highest TEP. Theoretical analysis suggests that the high TEP in the device with the largest hole density can be ascribed to the phonon-mediated intervalley scatterings. The highest TEP reaches ∼230μV/K at 370 K while the electrical resistivity of the device is maintained below 1.5mΩcm. Therefore, a large power factor PF ∼36μWcm−1K−2 comparable to the record values reported in p-type materials is obtained.

关键词： Seebeck effect Thermoelectric effects Topological materials Transport phenomena

Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

Finite-size analysis of continuous variable source-independent quantum random number generation

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arXiv 2020年

作者： Zhang, Junyu Zhang, Yichen Zheng, Ziyong Chen, Ziyang Xu, Bingjie Yu, Song State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China

We study the impact of finite-size effect on continuous variable source-independent quantum random number generation. The central-limit theorem and maximum likelihood estimation theorem are used to derive the formula which could output the statistical fluctuations and determine upper bound of parameters of practical quantum random number generation. With these results, we can see the check data length and confidence probability has intense relevance to the final randomness, which can be adjusted according to the demand in implementation. Besides, other key parameters, such as sampling range size and sampling resolution, have also been considered in detail. It is found that the distribution of quantified output related with sampling range size has significant effects on the loss of final randomness due to finite-size effect. The overall results indicate that the finite-size effect should be taken into consideration for implementing the continuous variable source-independent quantum random number generation in practical. Copyright © 2020, The Authors. All rights reserved.

关键词： Random number generation

Age-Friendly City and Walkability: Data from Observations Towards Simulations 8th

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Age-Friendly City and Walkability: Data from Observations To...

8th Italian Forum on Ambient Assisted Living, ForitAAL 2017

作者： Gorrini, Andrea Crociani, Luca Vizzari, Giuseppe Bandini, Stefania Department of Computer Science Complex Systems and Artificial Intelligence Research Center University of Milano-Bicocca Viale Sarca 336 Building U14 Milan20126 Italy Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-kuTokyo153-8904 Japan

ISBN: (纸本)9783030046712

The use of agent-based simulation systems can provide an innovative framework to support the design of age-friendly cities, focusing on walkability assessment. This is aimed at managing the complex interaction between elderly pedestrians and vehicles at zebra crossings, in which the compliance to traffic norms plays a fundamental role. The data of an observation performed at a non-signalized intersection are presented to provide useful insights for supporting the future development of agent-based models. Results focus on drivers’ compliance to crossing pedestrians, describing potentially conflictual interactions among heterogeneous agents. The discussion closes with the potential applications of the collected data set for modelling the phenomenon. © 2019, Springer Nature Switzerland AG.

关键词： Autonomous agents

Transition Metal-Vacancy Point Defects in Zinc Oxide as Deep-Level Spin Qubits

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arXiv 2025年

作者： Zhang, Shimin Park, Taejoon Perez, Erik Li, Kejun Wang, Xingyi Wang, Yanyong Vega Bazantes, Jorge D. Zhang, Ruiqi Sun, Jianwei Fu, Kai-Mei C. Seo, Hosung Ping, Yuan Department of Materials Science and Engineering University of Wisconsin-Madison 53706 United States SKKU Advanced Institute of Nanotechnology Sungkyunkwan University Gyeonggi Suwon16419 Korea Republic of Department of Energy Systems Research Department of Physics Ajou University Gyeonggi Suwon16499 Korea Republic of Department of Physics University of California Santa CruzCA95064 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States School of Science & Engineering Tulane University 6823 St Charles Ave New OrleansLA70118 United States Department of Physics University of Washington SeattleWA98195 United States Physical Sciences Division Pacific Northwest National Laboratory RichlandWA99352 United States Department of Quantum Information Engineering Sungkyunkwan University Gyeonggi Suwon16419 Korea Republic of Center for Quantum Information Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics University of Wisconsin-Madison 53706 United States Department of Chemistry University of Wisconsin-Madison 53706 United States

Wide band gap oxides are promising host materials for spin defect qubits, offering unique advantages such as a dilute nuclear spin environment. Zinc oxide (ZnO), in particular, can achieve exceptional high purity, which enables long spin coherence time. In this work, we theoretically search for deep-level point defects in ZnO with optimal physical properties for optically-addressable spin qubits. Using first-principles calculations, we predict the Molybdenum-vacancy complex defect (MoZnvO)2+ in ZnO to own promising spin and optical properties, including spin-triplet ground state, optical transition in the visible to near-infrared range with high quantum yield, allowed intersystem crossings with a sizable optically-detected magnetic resonance contrast, and long spin T2 and T∗2. Notably, we find the Huang-Rhys factor of the defect to be around 5, which is significantly smaller than the typical range of 10-30 for most known defects in ZnO. Furthermore, we compare the spin decoherence driven by the nuclear spin bath and paramagnetic impurity baths. We find that the paramagnetic impurities are very effective in causing spin decoherence even with very low concentrations, implying that they can likely dominate the spin decoherence in ZnO even after isotopic purification. Using the computed excited-state energies and kinetic rates as inputs, we predict the ODMR contrast and propose a new protocol for spin qubit initialization and readout, which could be generalized to other systems with forbidden axial intersystem crossings. © 2025, CC BY.

关键词： Zinc oxide