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Accelerated prediction of Cu-based single-atom alloy catalysts for CO_(2) reduction by machine learning

Green Energy & Environment 2023年第3期8卷 820-830页

作者： Dashuai Wang Runfeng Cao Shaogang Hao Chen Liang Guangyong Chen Pengfei Chen Yang Li Xiaolong Zou Shenzhen Geim Graphene Center Tsinghua-Berkeley Shenzhen Institute&Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen518055China Tencent Shenzhen518054China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong Kong999077China Tsinghua-Berkeley Shenzhen Institute Tsinghua UniversityShenzhen518055China

Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of ***,a unified understanding of underlying mechanism for further optimization is still *** this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as *** method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various *** ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic *** our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying ***,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of ***,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR *** work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.

关键词： Cu-based single-atom alloy CO adsorption Machine learning First principles CO_(2)reduction reaction

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Flow-aware synthesis: A generic motion model for video frame interpolation

引用

Computational Visual Media 2021年第3期7卷 393-405页

作者： Jinbo Xing Wenbo Hu Yuechen Zhang Tien-Tsin Wong Department of Computer Science and Engineering the Chinese University of Hong KongHong Kong SARChina Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina

A popular and challenging task in video research,frame interpolation aims to increase the frame rate of *** existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping ***,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered ***,we present an adaptive flow prediction module to better approximate the complex motions in ***,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear *** enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction *** and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.

关键词： flow-aware generic motion model video frame interpolation

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Multi-resolution 2D-3D registration of vascular intervention based on Normalized mutual information and gradient difference fusion

Multi-resolution 2D-3D registration of vascular intervention...

引用

2022 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2022

作者： Wang, Jun Ning, Zhigang Liao, Xiangyun Wang, Qiong School of Electrical Engineering University of South China Hengyang421001 China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China

ISBN: (纸本)9781510654846

The registration between intraoperative 2D digital subtraction angiography and preoperative 3D computed tomography angiography can improve the visual perception of doctors during vascular interventional surgery and provide three-dimensional information of blood vessels. Therefore, improving the accuracy and robustness of 2D-3D vascular registration is the key to vascular interventional surgery. In this paper, we propose a similarity measure that fuses normalized mutual information with gradient difference and adds a multi-resolution strategy to the registration framework. Experiments show that the mTRE of the proposed method is 2.1mm, and the time of each registration iteration is 174.6s. Compared with normalized mutual information and gradient difference, the proposed method has higher accuracy and faster efficiency, and achieves better results in a shorter time. © 2022 SPIE.

关键词： Blood vessels

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

引用

12th International Conference on CYBER technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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Simulating the Dilution of Sulfuric Acid on Mixed reality Platform 8

Simulating the Dilution of Sulfuric Acid on Mixed Reality Pl...

引用

8th International Conference on virtual reality, ICVR 2022

作者： Pan, Zhipeng Miao, Jinda Cai, Ning Luo, Tianren Zhang, Mingmin Pan, Zhigeng Research Institute of Virtual Reality and Intelligent Systems Hangzhou Normal University Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Beijing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-Time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students. © 2022 IEEE.

关键词： Students

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Exploiting aliasing for manga restoration

arXiv

引用

arXiv 2021年

作者： Xie, Minshan Xia, Menghan Wong, Tien-Tsin Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology SIAT CAS

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screentone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative twostage method to restore quality bitonal manga from degraded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SENet) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Restoration

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Bidirectional projection network for cross dimension scene understanding

arXiv

引用

arXiv 2021年

作者： Hu, Wenbo Zhao, Hengshuang Jiang, Li Jia, Jiaya Wong, Tien-Tsin The Chinese University of Hong Kong Hong Kong University of Oxford United Kingdom Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology SIAT CAS China

2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two visual domains is well complementary, e.g., 2D images have fine-grained texture while 3D point clouds contain plentiful geometry information. However, most current visual recognition systems process them individually. In this paper, we present a bidirectional projection network (BPNet) for joint 2D and 3D reasoning in an end-to-end manner. It contains 2D and 3D sub-networks with symmetric architectures, that are connected by our proposed bidirectional projection module (BPM). Via the BPM, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition. Extensive quantitative and qualitative experimental evaluations show that joint reasoning over 2D and 3D visual domains can benefit both 2D and 3D scene understanding simultaneously. Our BPNet achieves top performance on the ScanNetV2 benchmark for both 2D and 3D semantic segmentation. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

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Multiscale Convolutional Transformer with Diverse-aware Feature Learning for Motor Imagery EEG Decoding

引用

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hang, Wenlong Wang, Junliang Liang, Shuang Lei, Baiying Wang, Qiong Li, Guanglin Chen, Badong Qin, Jing Nanjing Tech University College of Computer Nanjing211816 China Nanjing Tech University Information Engineering Nanjing211816 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210023 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China Guangdong Provincial Key Laboratory of Computer Vision Shenzhen518055 China Virtual Reality Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences Shenzhen518055 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hong Kong Polytechnic University School of Nursing Hung Hom Hong Kong

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) have significant potential in improving motor function for neurorehabilitation. Despite recent advancements, learning diversified EEG features across different frequency ranges remains a significant challenge, as the homogenization of feature representations often limits the generalization capabilities of EEG decoding models for BCIs. In this paper, we propose a novel multiscale convolutional transformer framework for EEG decoding that integrates multiscale convolution, transformer, and diverse-aware feature learning scheme (MCTD) to tackle the above challenge. Specifically, we first capture multiple frequency features using dynamic one-dimensional temporal convolution with different kernel lengths. Subsequently, we incorporate convolutional layers and transformers with a contrastive learning scheme to extract discriminative local and global EEG features within a single frequency range. To mitigate the homogenization of features extracted from different frequency ranges, we propose a novel decorrelation regularization. It enables multiscale convolutional transformers produce less correlated features with each other, thereby enhancing the overall expressiveness of EEG decoding model. The performance of MCTD is evaluated on four public MI-based EEG datasets, including the BCI competition III 3a and IV 2a, the BNCI 2015- 001, and the OpenBMI. For the average Kappa/Accuracy scores, MCTD obtains improvements of 3.58%/2.68%, 3.09%/2.20%, 2.33%/1.54%, and 4.44%/2.22%, over the state-of-the-art method on four EEG datasets, respectively. Experimental results demonstrate that our method exhibits superior performance. © 2016 IEEE.

关键词： Brain computer interface

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Difficulty-aware meta-learning for rare disease diagnosis 23rd

Difficulty-aware meta-learning for rare disease diagnosis

引用

23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Li, Xiaomeng Yu, Lequan Jin, Yueming Fu, Chi-Wing Xing, Lei Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Radiation Oncology Stanford University Stanford United States Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030597092

Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a few per-class data samples is very challenging, and so far, catches very little attention. In this paper, we present a difficulty-aware meta-learning method to address rare disease classifications and demonstrate its capability to classify dermoscopy images. Our key approach is to first train and construct a meta-learning model from data of common diseases, then adapt the model to perform rare disease classification. To achieve this, we develop the difficulty-aware meta-learning method that dynamically monitors the importance of learning tasks during the meta-optimization stage. To evaluate our method, we use the recent ISIC 2018 skin lesion classification dataset, and show that with only five samples per class, our model can quickly adapt to classify unseen classes by a high AUC of 83.3%. Also, we evaluated several rare disease classification results in the public Dermofit Image Library to demonstrate the potential of our method for real clinical practice. © Springer Nature Switzerland AG 2020.

关键词： Learning systems

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Kine-Appendage: Enhancing Freehand VR interaction through Transformations of virtual Appendages

TechRxiv

引用

TechRxiv 2021年

作者： Bai, Hualong Tian, Yang Zhao, Shengdong Fu, Chi-Wing Wang, Qiong Heng, Pheng-Ann School of Computers Electronics and Information Guangxi University China NUS-HCI Lab National University of Singapore Singapore The Chinese University of Hong Kong China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology SIAT CAS Shenzhen China

Kinesthetic feedback, the feeling of restriction or resistance when hands contact objects, is essential for natural freehand interaction in VR. However, inducing kinesthetic feedback using mechanical hardware can be cumbersome and difficult in commodity VR systems. We propose a novel approach for providing visual kinesthetic feedback, kine-appendage. It adds a virtual appendage to the user’s avatar hand. During freehand interaction with virtual objects, the appendage exhibits visual transformations, including rotation and deformation, as it makes contact with the surfaces of objects, providing additional feedback to compensate for the loss of kinesthetic feedback in the virtual environment. A proof-of-concept kine-appendage technique, BrittleStylus, was designed to enhance freehand typing. Our empirical evaluation demonstrated that BrittleStylus significantly reduced the uncorrected error rate of naive freehand typing from 6.53% to 1.92% without compromising the typing speed. BrittleStylus also helps to induce a sense of kinesthetic feedback and significantly reduce the possibility of hand penetrating objects. Inspired by the success of BrittleStylus, we further discussed the potential of other kine-appendage techniques. © 2021, CC BY-NC-SA.

关键词： virtual reality

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