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

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

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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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The Interactive Network Visualization of the interactions Between Topologically Associating Domains in the Genome of Fruit Fly

The Interactive Network Visualization of the Interactions Be...

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17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Mali, Samira Annavarapu, Swetha Laboratory for Theoretical and Computational Molecular Biophysics Virginia Tech Blacksburg VA United States The Distributed Virtual Environments Lab Center for Human-Computer Interaction Virginia Tech BlacksburgVA United States 3ADP Inc AlpharettaGA United States

In this work, we created a network visualization to help you understand how Topologically Associating Do mains (TADs) interact with each other across the genome based on where the TAD is located, whether it is near the center of the nucleus or near the edge of the nucleus. This visualization can reveal how the dense regions and sparse regions of chromosome interactions are distributed in one view. The pilot study demonstrates how network visualization of TAD-TAD interactions can quickly answer numerous major questions in 3D genome and epigenetics field without requiring the development of Machine Learning methods or Algorithms to un lock Heatmap structures. The questions include but are not limited to, determining many-way interactions and interactions between TADs belonging to various epigenetic classes. © 2024 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Visualization

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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...

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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

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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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Federated semi-supervised medical image classification via inter-client relation matching

arXiv

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arXiv 2021年

作者： Liu, Quande Yang, Hongzheng Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Beihang University China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training setting, most hospitals in realistic usually cannot afford the intricate data labeling due to absence of budget or expertise. This paper studies a practical yet challenging FL problem, named Federated Semi-supervised Learning (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals). We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme. The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, thereby mitigating the deficiency of task knowledge at unlabeled clients and promoting discriminative information from unlabeled samples. We validate our method on two large-scale medical image classification datasets. The effectiveness of our method has been demonstrated with the clear improvements over state-of-the-arts as well as the thorough ablation analysis on both tasks4 © 2021, CC BY-NC-ND.

关键词： Hospitals

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Mixed reality based respiratory liver tumor puncture navigation

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Computational Visual Media 2019年第4期5卷 363-374页

作者： Ruotong Li Weixin Si Xiangyun Liao Qiong Wang Reinhard Klein Pheng-Ann Heng Institute of Computer Science II University of Bonn53115 BonnGermany Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina Department of Computer Science and Engineering Chinese University of Hong KongHong KongChina

This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation(RFA).Our system contains an optical see-through head-mounted display device(OST-HMD),Microsoft Holo Lens for perfectly overlaying the virtual information on the patient,and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical *** with traditional navigation method with CT,our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking *** addition,to alleviate the difficulty during needle placement induced by respiratory motion,we reconstruct the patientspecific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver *** proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE,which has the potential to be applied in clinical RFA guidance.

关键词： mixed reality human computer interaction statistical motion model

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

Difficulty-aware meta-learning for rare disease diagnosis

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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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Evoking Stress Reactivity in a virtual Dance Competition

Evoking Stress Reactivity in a Virtual Dance Competition

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AHFE virtual Conference on human Factors and Simulation and the International Conference on Digital human Modeling and Applied Optimization, 2020

作者： van Dammen, Lotte Barnett, Neil Conrady, Roselynn Wright, Lucas Thymes, Bradon Shirtcliff, Elizabeth A. Department of Human Development and Family Studies Iowa State University AmesIA United States Virtual Reality Application Center Iowa State University AmesIA United States Department of Mechanical Engineering San Diego State University San DiegoCA United States Hamilton College Physics Department Hamilton ClintonNY United States Computer Science Department Hamilton ClintonNY United States Electrical Engineering and Computer Science Department Howard University WashingtonDC United States

ISBN: (纸本)9783030510633

Stress reactivity involves a physiological response to a stressful task, as a biological mechanism related to mental and physical health outcomes. Traditional stressors evoke social evaluative threat, which is fear of judgment and a threat to social status, typically via public speaking tasks. To be well-suited for virtual reality, however, a novel approach is necessary in the design of new stress tasks. We hypothesized that a virtual reality stress task involving social evaluative threat through a dance competition in front of an audience, elicits a stress response, measured by autonomic nervous system, cortisol, and testosterone reactivity. Participants (n = 18) showed autonomic nervous system reactivity in terms of increased heart rate and decreased respiratory sinus arrhythmia, indicating a stress response. Levels of cortisol increased in response to the dance competition, especially within responders, whereas testosterone levels did not change significantly over time. A virtual reality dance competition involving physical social evaluative threat elicits a stress response. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： virtual reality

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Instance Shadow Detection

Instance Shadow Detection

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Tianyu Wang Xiaowei Hu Qiong Wang Pheng-Ann Heng Chi-Wing Fu Department of Computer Science and Engineering The Chinese University of Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, we design LISA, named after Light-guided Instance Shadow-object Association, an end-to-end framework to automatically predict the shadow and object instances, together with the shadow-object associations and light direction. Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results. In our evaluations, we formulate a new metric named the shadow-object average precision to measure the performance of our results. Further, we conducted various experiments and demonstrate our method's applicability on light direction estimation and photo editing.

关键词： Head Feature extraction Proposals Image segmentation Image color analysis Training computer vision

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