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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Genetic and phenotypic relationships between coronary atherosclerotic heart disease and electrocardiographic traits

Genetic and phenotypic relationships between coronary athero...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinfeng Wang Haoyang Zhang Xuehao Xiu Mengling Qi Yuedong Yang Huiying Zhao Key Laboratory of Machine Intelligence and Advanced Computing of MOE School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Department of Medical Research Center Sun Yat-sen Memorial Hospital Guangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Observational studies have revealed that Coronary Atherosclerotic Heart Disease (CAHD) is associated with abnormal electrocardiogram (ECG) traits. However, it remains unclear whether there are genetic correlations between ECG and CAHD. Here, we explored genetic correlations and putative causal relationships between CAHD and ECG by performing Mendelian randomization (MR) and Polygenic risk score (PRS) analyses on the summary statistics from a large-scale genome-wide association study (GWAS) for CAHD (FinnGen: Ncase 23363, Ncontrol 187840) and ECG traits (UK Biobank: Ncase=1137, Ncontrol=40823). Results showed a causal genetic relationship between CAHD and six ECG traits in the lead V6. These ECG traits combining with age and gender have predicted CAHD risk with an AUC of 0.76. Further summary data-based Mendelian randomization (SMR) analysis identified 11 risk genes associated with the causality between CAHD and ECG. Thus, the revealed putative causal effects of CAHD on ECG traits provide genetic evidence to support the importance of monitoring CAHD risk through the ECG.

关键词： Heart Correlation Genomics Atherosclerosis Electrocardiography Lead Bioinformatics

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WINDFORMER: BI-DIRECTIONAL LONG-DISTANCE SPATIO-TEMPORAL NETWORK FOR WIND SPEED PREDICTION

arXiv

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arXiv 2023年

作者： Li, Xuewei Shang, Zewen Liu, Zhiqiang Yu, Jian Xiong, Wei Yu, Mei College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China Tianjin International Engineering Institute Tianjin University Tianjin300350 China TCU School of Civil Engineering Tianjin Chengjian University Tianjin300384 China

Wind speed prediction is critical to the management of wind power generation. Due to the large range of wind speed fluctuations and wake effect, there may also be strong correlations between long-distance wind turbines. This difficult-to-extract feature has become a bottleneck for improving accuracy. History and future time information includes the trend of airflow changes, whether this dynamic information can be utilized will also affect the prediction effect. In response to the above problems, this paper proposes Windformer. First, Windformer divides the wind turbine cluster into multiple non-overlapping windows and calculates correlations inside the windows, then shifts the windows partially to provide connectivity between windows, and finally fuses multi-channel features based on detailed and global information. To dynamically model the change process of wind speed, this paper extracts time series in both history and future directions simultaneously. Compared with other current-advanced methods, the Mean Square Error (MSE) of Windformer is reduced by 0.5% to 15% on two datasets from NERL. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Toward reducing the effect of force variations on electromyography pattern recognition by Mel-frequency spectrum

Toward reducing the effect of force variations on electromyo...

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2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Liu, Yan Tian, Lan Zheng, Yue Zhou, Xiaomeng Li, Xiangxin Li, Guanglin Wuhan Research Institute of Posts and Telecommunications Wuhan430070 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Currently, the electromyography pattern recognition (EMG-PR) is considered as a promising approach to control the human-machine interaction systems such as multifunctional prostheses. However, the robustness of EMG-PR method is still not strong enough to against some issues such as different arm positions, electrode shift, muscle fatigue and force variation in the clinical application. And among these issues, the force variation is an important problem that greatly affects the performance of EMG-PR based systems. In this study, a feature of log-Mel-frequency spectrum (log-MFS) was proposed to reduce the effects of force variations on the classification performance of the EMG-PR method. Eight channels of EMG signals were recorded from the upper limbs of eight subjects when performing different hand motions at low, medium and high force levels, respectively. Then the proposed feature of log-MFS was extracted from the EMG signals and used to classify the motions. Compared with the commonly used time domain feature set, the feature of log-MFS achieved the higher classification accuracies for all the three force levels. Especially for the un-trained high and low force levels, the average classification accuracies increased by about 27% and 11%. These results demonstrated that the feature of log-MFS is effectiveness to enhance the robustness of the EMG-PR based systems to against force variations in practical application. © 2021 IEEE.

关键词： Pattern recognition

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Neighbor combinatorial attention for critical structure mining 29

Neighbor combinatorial attention for critical structure mini...

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29th International Joint Conference on Artificial intelligence, IJCAI 2020

作者： Zuo, Tanli Qiu, Yukun Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Peng Cheng Laboratory Shenzhen518005 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9780999241165

Graph convolutional networks (GCNs) have been widely used to process graph-structured data. However, existing GNN methods do not explicitly extract critical structures, which reflect the intrinsic property of a graph. In this work, we propose a novel GCN module named Neighbor Combinatorial ATtention (NCAT) to find critical structure in graph-structured data. NCAT attempts to match combinatorial neighbors with learnable patterns and assigns different weights to each combination based on the matching degree between the patterns and combinations. By stacking several NCAT modules, we can extract hierarchical structures that is helpful for down-stream tasks. Our experimental results show that NCAT achieves state-of-the-art performance on several benchmark graph classification datasets. In addition, we interpret what kind of features our model learned by visualizing the extracted critical structures. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Benchmarking

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Point Cloud Segmentation Network Based on Multi-Scale Feature Fusion

Point Cloud Segmentation Network Based on Multi-Scale Featur...

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Chinese Automation Congress (CAC)

作者： Hao Deng Peng Cheng Shengmei Cheng Cheng Liu Shaoyi Du Lin Wang State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Service School of Information Science and Technology Northwest University Xi'an China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at improving the local feature extraction for point cloud learning, we introduce a point cloud segmentation network that enhances the PointNet++ framework with a local feature transformation module and a multi-scale feature fusion module. The local feature transformation module employs operations akin to convolution to restructure the point cloud's local feature dimensions, thus boosting the network's ability to extract detailed local features. Additionally, the multi-scale feature fusion module integrates semantic features of point clouds across various scales using a layered network design, aiming to heighten segmentation precision. Experimental results indicate that the proposed network can achieve a robust point cloud segmentation accuracy, reaching an accuracy rate of 85.8%.

关键词： Point cloud compression Accuracy Automation Convolution Semantics Feature extraction Boosting

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Latency Localization of Auditory Brainstem Response by Different Deep Learning Methods

Latency Localization of Auditory Brainstem Response by Diffe...

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

作者： Junyu Ji Xin Wang Desheng Jia Chunrui Zhao Yuchao He Ijaz Ahmad Mingxing Zhu Hongguang Pan Shixiong Chen Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Otolaryngology Shenzhen Children's Hospital Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

Auditory brainstem response (ABR) is an evoked potential generated by the auditory brainstem nuclei in response to auditory stimuli. Hearing loss or impairment in the auditory pathway can be objectively reflected by extracting the latencies of the peaks of characteristic waves (especially wave V) in the clinic. The commonly used clinical method of peak localization is the manual labeling approach by subjective visualization. However, this method is time-consuming and highly dependent on the experience of the clinician. In this study, three different types of neural networks were proposed to realize the automatic intelligent recognition of the wave V position. Firstly, during the data preprocessing, the ABR data of humans was clipped and normalized. Then three types of neural networks, namely DNN, CNN, and RNN, were proposed for the latency localization of wave V. The performances of these three networks were further evaluated under different sound pressure levels and different error scales. The results showed that DNN was the best network for localizing the wave V position. The highest overall accuracy rate was 91.70% and the error scale was 0.1ms. This study showed that the proposed automatic method with neural networks can greatly reduce the workload of medical personnel in finding the ABR latencies and can assist doctors in further diagnosis.

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A motion planning method based on HRL for autonomous exoskeleton

A motion planning method based on HRL for autonomous exoskel...

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2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Dong, Yao He, Yong Su, Zhilong Ni, Jiangpeng Feng, Wei Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

With the increase of the world's population and the serious aging phenomenon, more and more people with mobility impairments need technology to assist them in their daily lives. Autonomous Exoskeleton are one of the important technologies. The existing gait generation algorithm are most based on simplified mathematical model calculations, which have the problem of poor accuracy and unfriendliness to the wearer. This research aims to propose a path planning and gait generation policies (HPPO) based on hierarchical reinforcement learning (HRL), so that the wearer can safely avoid obstacles and maintain dynamic balance when using the autonomous exoskeleton controlled by this policy to walk. The high-level controller (HL) is responsible for path planning, it can quickly plan a reasonable exoskeleton footholds in the indoor environment. The low-level controller (LL) can imitate a given reference trajectory, learn a walking policy suitable for the exoskeleton, and it can follow the planned footholds, so as to achieve the purpose of avoiding obstacles for the wearer. The algorithm is verified in simulation environment. © 2021 IEEE.

关键词： Motion planning

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Preserving earlier knowledge in continual learning with the help of all previous feature extractors

arXiv

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arXiv 2021年

作者： Li, Zhuoyun Zhong, Changhong Liu, Sijia Wang, Ruixuan Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou510000 China

Continual learning of new knowledge over time is one desirable capability for intelligent systems to recognize more and more classes of objects. Without or with very limited amount of old data stored, an intelligent system often catastrophically forgets previously learned old knowledge when learning new knowledge. Recently, various approaches have been proposed to alleviate the catastrophic forgetting issue. However, old knowledge learned earlier is commonly less preserved than that learned more recently. In order to reduce the forgetting of particularly earlier learned old knowledge and improve the overall continual learning performance, we propose a simple yet effective fusion mechanism by including all the previously learned feature extractors into the intelligent model. In addition, a new feature extractor is included to the model when learning a new set of classes each time, and a feature extractor pruning is also applied to prevent the whole model size from growing rapidly. Experiments on multiple classification tasks show that the proposed approach can effectively reduce the forgetting of old knowledge, achieving state-of-the-art continual learning performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Intelligent systems

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Embanet: A Flexible Efficient Multi-Branch Attention Network

SSRN

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SSRN 2023年

作者： Zu, Keke Zhang, Hu Lu, Jian Zhang, Lei Xu, Chen Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Shenzhen China University of Electronic Science and Technology of China Zhejiang Quzhou China

This work presents a novel module, namely multi-branch concat (MBC), toprocess the input tensor and obtain the multi-scale feature map. The proposedMBC module brings new degrees of freedom (DoF) for the design of attentionnetworks by allowing the type of transformation operators and the numberof branches to be flexibly adjusted. Two important transformation operators,multiplex and split, are considered in this work, both of which can representmulti-scale features at a more granular level and increase the range of receptivefields. By integrating the MBC and attention module, a multi-branch attention(MBA) module is consequently developed to capture the channel-wise interactionof feature maps for establishing the long-range channel dependency. By substituting the 3x3 convolutions in the bottleneck blocks of the ResNet with theproposed MBA, a novel block namely efficient multi-branch attention (EMBA)is obtained, which can be easily plugged into the state-of-the-art backbone CNNmodels. Furthermore, a new backbone network called EMBANet is establishedby stacking the EMBA blocks. The proposed EMBANet is extensively evaluatedon representative computer vision tasks including: classification, detection, andsegmentation. And it demonstrates consistently superior performance over thepopular backbones. © 2023, The Authors. All rights reserved.

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