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arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

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A stroke detection system based on cincinnati prehospital stroke scale 19

A stroke detection system based on cincinnati prehospital st...

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3rd International Conference on Medical and Health Informatics, ICMHI 2019

作者： Chien, Ting-Ying Chen, Chong-Yi Jin, Guo-Lun Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Institute of Data Science and Engineering National Chiao Tung University Hsinchu City Taiwan

ISBN: (纸本)9781450371995

Stroke, also known as cerebrovascular events, is mainly caused by the obstruction of blood flow in the brain, which leads to an inability to supply oxygen to the brain. By 2013, stroke had become the second most common cause of death in Taiwan (accounting for about 12% of all deaths). This study used image processing technology and speech recognition, following the Cincinnati Prehospital Stroke Scale, to determine whether or not the user had a stroke. The Cincinnati Prehospital Stroke Scale has three indicators, including facial droop, arm drift, and speech. Patients with 1 of these 3 findings have a 72% probability of having had an ischemic stroke. If all satisfies, the probability of the stroke is more than 85%. In addition, we developed a mobile APP based on this method to detect whether or not the user had a stroke, and hope to reduce stroke hazards. © 2019 ACM.

关键词： Signal detection

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Adaptive Surrogate Model for Failure Probability Estimation 21

Adaptive Surrogate Model for Failure Probability Estimation

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5th International Conference on Computer science and Application engineering, CSAE 2021

作者： Qin, Yi Zhang, Yue Liu, Zexin Zhu, Xueyu Wang, Peng School of Mathematical Sciences Beihang University Haidian District Beijing China School of Integrated Circuit Science and Engineering Beihang University Haidian District Beijing China Department of Mathematics University of Utah Salt Lake CityUtah United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUtah United States Department of Mathematics University of Iowa Iowa City United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Haidian District Beijing China

ISBN: (纸本)9781450389853

Estimating failure probability becomes a fundamental task in many complex engineering designs and optimizations. Yet, evaluation of failure probability via direct sampling from a given system can be computationally expensive and sometimes impossible. Although the construction of a response surface/surrogate could reduce such computational cost, reliance on its sampling alone may still yield an erroneous estimate of the failure probability. In this paper, we employ generalized polynomial chaos and develop an adaptive method whose surrogate model evolves with the additional data sampled from the underlying system as the iteration proceeds. It is more flexible by not requiring an accurate surrogate model in priori. Via three distinct numerical examples and one practical problem on a spintronic device, we demonstrate that our novel scheme provides an efficient tool to estimate system failure probability.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Iterative methods

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Universal approach to sending-or-not-sending twin field quantum key distribution

arXiv

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arXiv 2022年

作者： Hu, Xiao-Long Jiang, Cong Yu, Zong-Wen Wang, Xiang-Bin School of Physics State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-Sen University Guangzhou510275 China Jinan Institute of Quantum technology SAICT Jinan250101 China Data Communication Science and Technology Research Institute Beijing100191 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Shenzhen Institute for Quantum Science and Engineering Physics Department Southern University of Science and Technology Shenzhen518055 China

We present the method of decoy-state analysis after bit-flip error correction and using confidential observed numbers. Taking this tool we then construct a universal approach to sending-or-not-sending (SNS) protocol of twin-field quantum key distribution. In this improved protocol, the code bits are not limited to heralded events in time windows participated by pulses of intensity µz and vacuum. All kinds of heralded events can be used for code bits to distill the final keys. The number of intensities (3 or 4) and the kinds of heralded events for code bits are automatically chosen by the key rate optimization itself. Numerical simulation shows that the key rate rises drastically in typical settings, up to 80% improvement compared with the prior results. Also, larger intensity value can be used for decoy pulses. This makes the protocol more robust in practical experiments. © 2022, CC BY.

关键词： Error correction

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Multi-sensor Fusion Detection Method for Vehicle Target Based on Kalman Filter and data Association Filter 7th

Multi-sensor Fusion Detection Method for Vehicle Target Base...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Duan, Xuting Sun, Chengming Tian, Daxin Zheng, Kunxian Zhou, Gang E, Wenjuan Zhang, Yundong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Transpotation Science and Engineering Beihang University Beijing100191 China Research Institute of Highway Ministry of Transport of the People’s Republic of China Beijing100088 China School of Rail Transportation Soochow University Suzhou215000 China Suzhou Automotive Research Institute Tsinghua University Suzhou215000 China State Key Laboratory of Digital Multimedia Chip Technology Chongqing Vimicro AI Chip Technology Co. Ltd. Chongqing400722 China

ISBN: (纸本)9783030786175

Multi-sensor data fusion is an emerging technology, which has been widely used in medical diagnosis, remote sensing, inertial navigation and many other fields. What’s more, the implementation and application of automatic driving system rely heavily on target detection technology. Due to the high mobility and unpredictability of vehicle-mounted equipment, for automatic vehicles, it is arduous to achieve real-time and accurate vehicle target detection by a single sensor means, thus it is difficult to reliably guarantee the safety and stability. This paper proposes a novel object detection method based on a multi-sensor fusion mechanism, which considers the real-time sensing data from two types of sensors including radar and camera. It collects multi-vehicle speed and position information efficiently and reliably. Then, it filters and integrates data according to Extended Kalman Filter, data Association Filter and some other methods. Furthermore, vehicle-borne equipment makes intelligent decision based on the data. In addition to theoretical support, the designed simulation results also show that the multi-sensor fusion mechanism can detect target vehicles efficiently and accurately, and it has superiority in the stability and accuracy of perception than single sensor sensing method. © 2021, Springer Nature Switzerland AG.

关键词： Extended Kalman filters

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A Crystal Plasticity Model with Irradiation Effect for the Mechanical Behavior of FCC Metals

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Acta Mechanica Solida Sinica 2019年第6期32卷 675-687页

作者： Jnnfeng Nie Yunpeng Liu Pandong Lin Qihao Xie Zhanli Liu Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor Engineering and Safety of Minis try of EducationTsinghua UniversityBeijing 100084China Applied Mechanics Lab. School of Aerospace EngineeringTsinghua UniversityBeijing 100084China Data Science and Information Technology Research Center Tsinghua-Berkeley Shenzhen InstituteShenzhen 518055China

In this paper,a crystal plasticity model considering the irradiation effect based on the thermal activation theory is *** evolutions of screw dislocations,edge dislocations,and stacking fault tetrahedrals(SFTs)(induced by irradiation)are included into the *** interactions between dislocations and irradiation-induced SFTs are also *** constitutive model is numerically implemented on the ABAQUS platform through UMAT subroutine and applied to study the irradiation effect on the mechanical behavior of pure *** mechanical properties of single and polycrystalline copper are studied,and the simulation results show that the constitutive model can properly predict the mechanical behavior of irradiated pure *** for polycrystalline copper,the simulation results are in good agreement with the experimental data.

关键词： Crystal plasticity Irradiation effect Uniaxial tension Dislocation FCC crystal

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Stabilization of continuous-time Markov/semi-Markov jump linear systems via finite data-rate feedback

arXiv

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arXiv 2021年

作者： Wang, Jingyi Feng, Jianwen Xu, Chen Wu, Xiaoqun Lü, Jinhu College of Mathematics and Statistics Shenzhen University Shenzhen518060 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan China State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

This paper investigates almost sure exponential stabilization of continuous-time Markov jump linear systems (MJLSs) under communication data-rate constraints by introducing sampling and quantization into the feedback control. Different from previous works, the sampling times and the jump times are independent of each other in this paper. The quantization is recursively adjusted on the sampling time, and its updating strategy does not depend on the switching in a sampling interval. In other words, the explicit value of the switching signal in a sampling interval is not necessary. The numerically testable condition is developed to ensure almost sure exponential stabilization of MJLSs under the proposed communication and control protocols. We also drop the assumption of stabilizability of all individual modes required in previous works about the switched systems. Moreover, we extend the result to the case of continuous-time semi-Markov jump linear systems (semi-MJLSs) via the semi-Markov kernel approach. Finally, some numerical examples are presented to illustrate the effectiveness on stabilization of the proposed communication and control *** Codes 93D15 © 2021, CC BY.

关键词： Markov processes

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Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction

arXiv

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arXiv 2020年

作者： Pian, Weiguo Wu, Yingbo Qu, Xiangmou Cai, Junpeng Kou, Ziyi Automotive Collaborative Innovation Center Chongqing University China School of Big Data & Software Engineering Chongqing University China Department of Computer Science and Engineering University of Notre Dame United States

Ride-hailing demand prediction is an essential task in spatial-temporal data mining. Accurate Ride-hailing demand prediction can help to pre-allocate resources, improve vehicle utilization and user experiences. Graph Convolutional Networks (GCN) is commonly used to model the complicated irregular non-Euclidean spatial correlations. However, existing GCN-based ride-hailing demand prediction methods only assign the same importance to different neighbor regions, and maintain a fixed graph structure with static spatial relationships throughout the timeline when extracting the irregular non-Euclidean spatial correlations. In this paper, we propose the Spatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailing demand prediction method. Based on the attention mechanism of GAT, STDGAT extracts different pair-wise correlations to achieve the adaptive importance allocation for different neighbor regions. Moreover, in STDGAT, we design a novel time-specific commuting-based graph attention mode to construct a dynamic graph structure for capturing the dynamic time-specific spatial relationships throughout the timeline. Extensive experiments are conducted on a real-world ride-hailing demand dataset, and the experimental results demonstrate the significant improvement of our method on three evaluation metrics RMSE, MAPE and MAE over state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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SADDEL: Joint speech separation and denoising model based on multitask learning

arXiv

引用

arXiv 2020年

作者： Wu, Yuan-Kuei Tuan, Chao-I Lee, Hung-Yi Tsao, Yu Graduate Institute of Communication Engineering National Taiwan University Data Science Degree Program National Taiwan University Research Center for Information Technology Innovation Academia Sinica

Speech data collected in real-world scenarios often encounters two issues. First, multiple sources may exist simultaneously, and the number of sources may vary with time. Second, the existence of background noise in recording is inevitable. To handle the first issue, we refer to speech separation approaches, that separate speech from an unknown number of speakers. To address the second issue, we refer to speech denoising approaches, which remove noise components and retrieve pure speech signals. Numerous deep learning based methods for speech separation and denoising have been proposed that show promising results. However, few works attempt to address the issues simultaneously, despite speech separation and denoising tasks having similar nature. In this study, we propose a joint speech separation and denoising framework based on the multitask learning criterion to tackle the two issues simultaneously. The experimental results show that the proposed framework not only performs well on both speech separation and denoising tasks, but also outperforms related methods in most conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Source separation

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Convolutional Neural Network Pruning: A Survey

Convolutional Neural Network Pruning: A Survey

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第三十九届中国控制会议

作者： Sheng Xu Anran Huang Lei Chen Baochang Zhang School of Automatic Science and Eletrical Engineering Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing

Deep convolutional neural networks have enabled remarkable progress over the last years on a variety of visual tasks,such as image recognition, speech recognition, and machine translation. These tasks contribute many to machine ***, developments of deep convolutional neural networks to a machine terminal remains challenging due to massive number of parameters and float operations that a typical model contains. Therefore, there is growing interest in convolutional neural network pruning. Existing work in this field of research can be categorized according to three dimensions: pruning method,training strategy, estimation criterion.

关键词： convolutional neural networks machine intelligence pruning method training strategy estimation criterion

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