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Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

Computational Visual Media 2021年第2期7卷 241-252页

作者： Jin Zhou Qing Zhang Jian-Hao Fan Wei Sun Wei-Shi Zheng School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China. School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education(Sun Yat-sen University)Guangzhou 510006China Peng Cheng Laboratory Shenzhen 518000China

Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same *** address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal *** achieve this in a coarse to fine manner,by joint regression and learning from pairwise ***,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image *** then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based ***,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge *** demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art ***,we show that the learned personalized image aesthetic benefits a wide variety of applications.

关键词： s personalized image aesthetic assessment deep convolutional neural networks pairwise ranking regression

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An Attention-Based Hybrid Deep learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection 46

An Attention-Based Hybrid Deep Learning Approach for Patient...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ahmad, Ijaz Wang, Xin Li, Lin Liu, Zhenzhen Huang, Jun Aboyeji, Sunday Timothy Li, Guanglin Mukhopadhyay, Subhas Chandra Liu, Zhiyuan Zhao, Guoru Guo, Yi Chen, Shixiong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Shenzhen Children's Hospital Surgery Division Epilepsy Center Shenzhen China Shenzhen Fengsheng Biotechnology Co. Ltd Shenzhen China Macquarie University Department of Engineering SydneyNSW Australia Shenzhen People's Hospital Department of Neurology Shenzhen China The Chinese University of Hong Kong Shenzhen School of Medicine Shenzhen China

ISBN: (纸本)9798350371499

Automatic detection of epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific models and have shown promising results, which is not suitable for clinical application, especially when new patient data are used for diagnosis in EEG epileptic seizure detection (ESD). Therefore, the proposed study introduces a novel hybrid deep learning approach consisting of a one-dimensional convolutional neural network (1D CNN), a Multi-Long Short-Term Memory Network (MLSTM) with a multi-attention layer (MAT) for patient-specific, cross-patient, and patient-independent seizure detection. The 1D CNN model extracts spatial features, while the MLSTM extracts temporal features from segmented EEG data. Moreover, the MAT layer conducts feature fusion and identifies relevant patterns. Experiments conducted using the CHB-MIT EEG dataset confirm our method's superiority over other sibling and state-of-the-art methods by an average of 2% in classification accuracy, recall, specificity, and *** of using patient-specific, cross-patient, and patient-independent seizure detection, demonstrating a robust and effective framework in EEG ESD. © 2024 IEEE.

关键词： Convolutional neural networks

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A Review of State-of-the-arts Sensing Techniques for the Ultra-early Screening of Cerebral Palsy

A Review of State-of-the-arts Sensing Techniques for the Ult...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Zhuang, Yunji Zhou, Hao Zhang, Huiping Chen, Yanni Wang, Lin Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China Guangdong-Hong Kong-Macau Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Xi'an Jiaotong University Children's Hospital Xi'an710002 China

ISBN: (纸本)9798350327182

Cerebral palsy (CP) is characterized by neuromotor disorders. It remains a huge challenge for ultra-early screening of CP. So far, the diagnosis of CP is mainly based on a combination of neuromotor assessment methods, magnetic resonance imaging (MRI) and clinical history. However, subjective errors are highly likely to be introduced in the process of CP screening due to disparities in clinicians' professional backgrounds, working abilities and academic levels. Cutting-edge sensing techniques have been unveiled in the field of ultra-early CP prediction which provides objective diagnosis with quantified output information by obtaining motion capture, classification, and recognition. It is necessary to contrast the strengths, weaknesses, and potential of these techniques and how to apply them in the ultra-early screening of CP. This study compares and analyzes the latest and advanced sensing methods and discusses their future applications in infantile CP screening. The work could provide technical suggestions and clinical guidance for early intervention in the field of ultra-early CP screening later on. © 2023 IEEE.

关键词： Diseases

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The measuring ZMP of self-balancing exoskeleton robot is calibrated by using the neural network

The measuring ZMP of self-balancing exoskeleton robot is cal...

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

作者： Xu, Yang Xiao, Yang Ma, Yue Zheng, Liangsheng He, Yong Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665436786

The exoskeleton robot is an auxiliary device to help the disabled people walk, and the self-balancing exoskeleton robot is one which is to keep balance without the assistance of external crutches. In order to keep the balance of the self-balancing exoskeleton robot, it is necessary to get the position of the Zero Moment Point by measuring the pressure of the footplate, and make the position of ZMP in range of supporting area. In this experiment, the footplate is used with the double-deck structure, this structure is compared with the single-deck structure, the double-dack structure will not lose the information of the collected ZMP without direct touch with the sensor, and it is lighter than another structure with dozens of sensors. But there is an inevitable structural coupling in the double-deck structure, which makes the ZMP have a large measurement error. In order to solve this problem, a novel idea is proposed, with the help of the powerful processing and learning capabilities of the neural network, four kinds of neural networks are used to calibrate measured position of ZMP so that reducing error of the measured ZMP. By comparing position of the actual ZMP before and after the calibration with the ideal position of ZMP and computing the errors to judge the effect of the calibration. Through experimental comparison, it is concluded that the different neural networks eliminate error of the measured ZMP in different extent. When the GRNN neural network is used to calibrate position of ZMP, the effect is the most ideal. © 2021 IEEE.

关键词： Errors

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MMA regularization: decorrelating weights of neural networks by maximizing the minimal angles 20

MMA regularization: decorrelating weights of neural networks...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Zhennan Wang Canqun Xiang Wenbin Zou Chen Xu Shenzhen Key Laboratory of Advanced Machine Learning and Applications Guangdong Key Laboratory of Intelligent Information Processing Institute of Artificial Intelligence and Advanced Communication College of Electronics and Information Engineering Shenzhen University Institute of Artificial Intelligence and Advanced Communication College of Mathematics and Statistics Shenzhen University

ISBN: (纸本)9781713829546

The strong correlation between neurons or filters can significantly weaken the generalization ability of neural networks. Inspired by the well-known Tammes problem, we propose a novel diversity regularization method to address this issue, which makes the normalized weight vectors of neurons or filters distributed on a hypersphere as uniformly as possible, through maximizing the minimal pairwise angles (MMA). This method can easily exert its effect by plugging the MMA regularization term into the loss function with negligible computational overhead. The MMA regularization is simple, efficient, and effective. Therefore, it can be used as a basic regularization method in neural network training. Extensive experiments demonstrate that MMA regularization is able to enhance the generalization ability of various modern models and achieves considerable performance improvements on CIFAR100 and TinyImageNet datasets. In addition, experiments on face verification show that MMA regularization is also effective for feature learning.

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Feature Fusion of Gramian Angular Field Deep learning EEG-Based Epileptic Seizure Classification 17

Feature Fusion of Gramian Angular Field Deep Learning EEG-Ba...

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17th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2024

作者： Aboyeji, Sunday Timothy Wang, Xin Chen, Yan Ahmad, Ijaz Li, Lin Liu, Zhenzhen Yao, Chen Zhang, Yu Zhao, Guoru Li, Guanglin Chen, Shixiong Shenzhen Institute of Advanced Technology Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Chinese Academy of Sciences Guangdong Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China Surgery Division Epilepsy Center Shenzhen Children's Hospital Shenzhen518036 China First Affiliated Hospital of Shenzhen University Shenzhen Second People's Hospital The National Key Clinic Specialty Shenzhen Key Laboratory of Neurosurgery Department of Neurosurgery Shenzhen518035 China Naval Medical University Translational Medicine Center Shanghai200433 China The Chinese Univ of Hong Kong School of Medicine Guangdong Shenzhen518172 China

ISBN: (纸本)9798350355154

Deep learning, particularly convolutional neural networks (CNNs), is increasingly used for epileptic seizure detection, leveraging their ability to directly extract features from EEG signals. Since CNNs excel with image data, various methods have been developed to convert signals into images, including the Gramian Angular Field (GAF) transformation algorithm. The algorithm is subtyped into the Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF). Each of these transformation techniques has been employed individually in earlier studies. However, each method focuses on unique properties of the EEG signal during image construction. Therefore, we proposed a feature fusion of the two transformation methods using the CNN model to learn richer representations. The experiment was conducted on publicly available Bonn datasets. Initially, the datasets are segmented into epochs of length 0.5s and step size 0.2s. Images are generated for each of the segments, resulting in 8600 images for each set of Bonn datasets. The generated images from each of the transformations are fed into parallel CNNs, and the extracted features are fused for improved performance. We conducted binary, ternary, and multiclass (5-class) classifications. The results show that feature fusion of GASF and GADF improves the classification performance with an average accuracy increase of 0.255%, 0.295%, and 0.660% for binary, ternary, and multiclass, respectively. This feature fusion of the GAF transformation approach shows promising potential for distinguishing various EEG patterns, underscoring its potential in seizure classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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EMBANet: A Flexible Efficient Multi-branch Attention Network

arXiv

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arXiv 2024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), to process the input tensor and obtain the multi-scale feature map. The proposed MBC module brings new degrees of freedom (DoF) for the design of attention networks by allowing the type of transformation operators and the number of branches to be flexibly adjusted. Two important transformation operators, multiplex and split, are considered in this work, both of which can represent multi-scale features at a more granular level and increase the range of receptive fields. By integrating the MBC and attention module, a multi-branch attention (MBA) module is consequently developed to capture the channel-wise interaction of feature maps for establishing the long-range channel dependency. By substituting the 3x3 convolutions in the bottleneck blocks of the ResNet with the proposed MBA, a novel block namely efficient multi-branch attention (EMBA) is obtained, which can be easily plugged into the state-of-the-art backbone CNN models. Furthermore, a new backbone network called EMBANet is established by stacking the EMBA blocks. The proposed EMBANet is extensively evaluated on representative computer vision tasks including: classification, detection, and segmentation. And it demonstrates consistently superior performance over the popular backbones. Copyright © 2024, The Authors. All rights reserved.

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Embanet: A Flexible Efficient Multi-Branch Attention Network

SSRN

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SSRN 2023年

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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Activating La−O−Ni Bridge in Ordered Macroporous Interface for Electrochemical Urea Wastewater Purification

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Angewandte Chemie 2025年

作者： Xiaoyue Qiang Yaxiong Yao Jie Yin Pengfei Da Zhaori Mu Kaier Shen Yuanmiao Sun Yunxiang Zhang Peiqiong Li Zhenglong Li Pinxian Xi Chun-Hua Yan State Key Laboratory of Applied Organic Chemistry Key Laboratory of Nonferrous Metal Chemistry and Resources Utilization of Gansu Province College of Chemistry and Chemical Engineering Frontiers Science Center for Rare Isotopes Lanzhou University Lanzhou 730000 China School of Materials Science and Engineering Peking University Beijing 100871 China Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences 1068 Xueyuan Avenue Shenzhen 518055 China Beijing National Laboratory for Molecular Sciences State Key Laboratory of Rare Earth Materials Chemistry and Applications Peking University. The University of Hong Kong Joint Laboratory in Rare Earth Materials and Bioinorganic Chemistry College of Chemistry and Molecular Engineering Peking University Beijing 100871 China

Electrochemical treatment of urea wastewater purification significantly aids in environmental protection, but it remains a considerable challenge in designing high performance anode urea oxidation electrocatalysts. Herein, we report a La-induced three-dimensional ordered macroporous (3DOM) NiO heterostructure to improve Ni sites electron density for urea electrooxidation by activating the La−O−Ni bridge. This material demonstrated exceptional performance in a membrane electrode assembly (MEA) device, characterized by a low cell voltage (1.49 V @ 80 °C) and 280 h stability test at 1 A cm −2 current density (25 °C) and displayed promising efficiency in urea wastewater purification. Permeation experiments revealed the crucial role of 3DOM morphological in facilitating mass transfer processes. A high valence nickel mechanism (HNM) on the La−O−Ni bridge during catalysis was proposed, based on various in situ characterizations and theoretical calculations. Experimentally, in situ Raman and UV-vis spectra demonstrated that Ni active species Ni δ+ (δ≥3) promote urea oxidation kinetics, while in situ ATR-IR proved strong adsorption of C=O with Ni sites and the enhancement of urea N−H bonds cleavage, supporting the HNM. This work enables us to underscore the critical importance of La−O−Ni electron bridge with 3DOM architectures and promising contributions to urea wastewater purification.

关键词： Three-dimensional ordered macroporous materials Urea oxidation reaction Rare earth oxide heterojunction interface Electron transfer bridge New class of Ni-based catalysts

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Evoking and Evaluation of Non-invasive Sensory Feedback for applications in Neural Rehabilitation: A Pilot Study

Evoking and Evaluation of Non-invasive Sensory Feedback for ...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Dong, Yuanzhe Tang, Xi Jiang, Naifu Xie, Jun Liang, Wenyuan Shang, Peng Li, Guanglin Fang, Peng Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems The Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China National Research Center for Rehabilitation Technical Aids Beijing100176 China

ISBN: (纸本)9798350327182

Protheses are very useful for limb amputees to restore their lost motor functions. However, most commercially available prostheses lack an intuitive and natural sensory feedback function, which may strongly limit the performance of prosthetic control and lowers the acceptance of amputees. As a non-invasive stimulation method, Transcutaneous Electrical Nerve Stimulation (TENS) has been proven to effectively evoke intuitive and natural sensations, which is practical for upper-limb amputees in clinical applications. In this paper, we explored the relationship between TENS modalities and induced sensory information based on able-bodied subjects by using electroencephalogram (EEG). We computed the Event-related Potential (ERP) and Brain Electrical Activity Mapping (BEAM) to evaluate the performance of sensation evoking. The results demonstrated that the TENS applied on wrist areas with proper parameters could stably induce multiple types of intuitive sensations of different fingers, including touching, vibrating, and flapping, and the mapping relationship between stimulation modality and sensory feedback were objectively verified and evaluated with EEG. This pilot study might provide a support for future researches on restoration of sensory paths and feedback for upper-limb amputees. © 2023 IEEE.

关键词： Electroencephalography

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