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SSRN 2023年

作者： Wang, Yi Wang, Yingchao Chen, Tengteng Li, Lei Wang, Guang Zhang, Zhengli Ding, Zhao Guo, Xiang Luo, Zijiang Liu, Xuefei College of Big Data and Information Engineering Guizhou University Guiyang550025 China Power Semiconductor Device Reliability Research Center Ministry of Education Guizhou University Guiyang550025 China Institute of Intelligent Manufacturing Shunde Polytechnic Guangdong Shunde528300 China Key Laboratory of Micro-Nano-Electronics of Guizhou Province Guiyang550025 China School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China

Photocatalytic hydrolysis is one of the most effective ways for hydrogen fuel as a strategy for turning solar energy into hydrogen fuel. Two-dimensional(2D) Janus materials with an out-of-plane polarised electric field not only help to promote photogenerated carrier separation and migration but also lead to energy band bending, which enhances photo-oxidation and retains efficient absorption of visible light. In this manuscript, a systematic first-principles analysis was conducted on the WSn2N4 and MSiSnN4 (M= Mo;W) system. The results show that Janus MSiSnN4 (M= Mo;W) systems have a suitable bandgap and band edge location that include the redox position for water, together with abundant electron mobility(>200 cm2V-1s-1) and outstanding visible light optical absorption capacity(ηSTH = 23.12% for WSiSnN4;ηSTH = 32.91% for MoSiSnN4). The oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) Gibbs free energy changes of WSiSnN4 show that it can make HER and OER processes occur spontaneously under light in acidic and neutral solutions, demonstrating excellent photocatalytic performance. © 2023, The Authors. All rights reserved.

关键词： Light absorption

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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FABind: Fast and Accurate Protein-Ligand Binding

arXiv

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arXiv 2023年

作者： Pei, Qizhi Gao, Kaiyuan Wu, Lijun Zhu, Jinhua Xia, Yingce Xie, Shufang Qin, Tao He, Kun Liu, Tie-Yan Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China China School of Computer Science and Technology Huazhong University of Science and Technology China Microsoft Research AI4Science China School of Information Science and Technology University of Science and Technology of China China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at Github3 Copyright © 2023, The Authors. All rights reserved.

关键词： Ligands

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MyBUD自适应分布式存储管理的设计与实现

MyBUD自适应分布式存储管理的设计与实现

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第29届中国数据库学术会议

作者： ZHOU Ningnan 周宁南 ZHANG Xiao 张孝 SUN Xinyun 孙新云 JU Xingxing 琚星星 LIU Kuicheng 刘奎呈 DU Xiaoyong 杜小勇 WANG Shan 王珊 Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin Universi 中国人民大学数据工程与知识工程教育部重点实验室北京100872 中国人民大学信息学院北京100872

面对日益增长的非结构化数据管理需求，实现了基于“自由表”数据模型和BUD参考体系模型的非结构化数据管理平台MyBUD系统。提出了一种能够根据非结构化数据的类型和访问特点自适应地选择分布式存储子系统的方法，同时也对MyBUD进行了T... 详细信息

面对日益增长的非结构化数据管理需求，实现了基于“自由表”数据模型和BUD参考体系模型的非结构化数据管理平台MyBUD系统。提出了一种能够根据非结构化数据的类型和访问特点自适应地选择分布式存储子系统的方法，同时也对MyBUD进行了TPCC测试和非结构化数据存取实验。结果表明，这种自适应的数据存储方法为MyBUD系统提供了高效的可扩展存储层，为采用数据库方法实现对结构化和非结构化数据统一管理的进一步研究工作奠定了基础。

关键词：数据库系统存储管理自适应算法体系架构服务质量

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Application of Hybrid Monocular SLAM Method in Augmented Reality

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zuoming Zhang Hanwen Wang Man Shu Xin Wang College of Software Engineering Jilin University Changchun Jilin 130000 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin 130000 China

In this paper, we design a hybrid (semi-direct) approach to simultaneous localization and mapping (SLAM) for monocular cameras and apply it to augmented reality (AR) for monocular cameras. We combine the advantagesof the direct method and the feature point method. We use both photometric bundle adjustment which is robust to camera exposure time and motion bundle adjustment which is geometrically robust based on feature points to do tracking process. This approach can maintain an intuitive direct local map as well as a reusable global sparse feature point map. Through the processing of point clouds, such as PCA plane detection and grid reconstruction, we greatly improve the effect of the augmented reality system.

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Application of Monocular Direct Vision Odometry in Augmented Reality

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zuoming Zhang Zixuan Wang Hanwen Wang Xin Wang College of Software Engineering Jilin University Changchun Jilin 130000 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin 130000 China

In recent years, the unlabeled augmented reality system has been gradually applied to various mobile devices, among which stable, accurate, and fast registration is the key to realizing this function. For this technique, this paper introduces camera exposure parameters and puts the data association and pose estimation into a unified nonlinear optimization problem. Moreover, the direct monocular vision odometer is transplanted into the augmented reality system through the position adjustment module. We compare it with the traditional visual odometry method that matches the feature points. The results show that this improved method can be used to track more quickly and build a more visual semi-dense point cloud map, which can be used to support the registration and tracking of virtual objects in augmented reality.

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RSAFormer: A method of polyp segmentation with region self-attention transformer

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Computers in Biology and Medicine 2024年 172卷 108268-108268页

作者： Yin, Xuehui Zeng, Jun Hou, Tianxiao Tang, Chao Gan, Chenquan Jain, Deepak Kumar García, Salvador School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune412115 India Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain

Colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ability due to the lack of clear and highly similar boundaries between normal tissue and polyps. To deal with this problem, we propose a region self-attention enhancement network (RSAFormer) with a transformer encoder to capture more robust features. Different from other excellent methods, RSAFormer uniquely employs a dual decoder structure to generate various feature maps. Contrasting with traditional methods that typically employ a single decoder, it offers more flexibility and detail in feature extraction. RSAFormer also introduces a region self-attention enhancement module (RSA) to acquire more accurate feature information and foster a stronger interplay between low-level and high-level features. This module enhances uncertain areas to extract more precise boundary information, these areas being signified by regional context. Extensive experiments were conducted on five prevalent polyp datasets to demonstrate RSAFormer's proficiency. It achieves 92.2% and 83.5% mean Dice on Kvasir and ETIS, respectively, which outperformed most of the state-of-the-art models. © 2024 Elsevier Ltd

关键词： Decoding

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Empirical Analysis of Electric Vehicles’ Charging Patterns: Case Study from Shanghai

SSRN

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SSRN 2022年

作者： Li, Zhi Xu, Zhengtian Chen, Zhibin Xie, Chi Chen, Guodong Zhong, Minghui 1555 Century Avenue Shanghai200122 China Department of Civil and Urban Engineering New York University 6 MetroTech Center BrooklynNY11201 United States Department of Civil and Environmental Engineering George Washington University 800 22nd St NW WashingtonWA20052 United States Key Road and Traffic Engineering Laboratory of Ministry of Education Tongji University 1239 Siping Road Shanghai200092 China 1555 Century Avenue Shanghai200122 China Shanghai Electric Vehicle Public Data Collecting Monitoring and Research Center 888 Moyunan Road Shanghai201805 China

Automotive electrification is a vital component of the carbon-neutrality agenda. To counter the influence of shrinking public incentives for purchasing electric vehicles (EVs), it is crucial to deepen the understanding of the existing users’ use cases and clear the barriers for potential EV owners in daily usage. To this end, our study analyzes an 11-month usage data from a large sample of EVs, including 3,777 battery EVs and 5,973 plug-in hybrid EVs in Shanghai, China. Based on their daily travel intensity, the EVs are clustered into two groups used primarily for private and commercial purposes. We leverage the data to uncover how, when, and where users in each group charge their EVs;investigate EVs’ energy consumption efficiency and its relationship to charging;and discuss EV-related policy implications. The outcome of this study may aid in the development of more targeted EV promotion policies and thus contributes to the carbon neutrality. © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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Enhanced surface water-energy coupling on the Tibetan Plateau over the past six decades (1960–2020)

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Fundamental Research 2024年

作者： Yang, Kun Sun, Jing Lu, Hui McColl, Kaighin A. Jiang, Yaozhi He, Qing Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing 100084 China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System Science Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing 100101 China Department of Earth and Planetary Sciences Harvard University Cambridge 02138 MA United States School of Engineering and Applied Sciences Harvard University Cambridge 02138 MA United States River and Environmental Engineering Laboratory The University of Tokyo Tokyo 113 8656 Japan

Long-term changes in surface water and energy budgets on the Tibetan Plateau (TP) are essential for understanding the variability of the Asian summer monsoon and water resources. The water budget and energy budget are inherently coupled, and in this study evaporative fraction (EF, the proportion of the surface available energy used for evapotranspiration) is used to quantify their coupling strength and simultaneously study its response to climate change in the TP. A so-called ‘surface flux equilibrium’ (SFE) method that uses only air temperature and humidity as inputs to estimate EF is evaluated and adopted for understanding EF variability in the TP during the warm season of the past six decades (1960–2020). The results show an overall increase in EF of the TP, but this increase is not uniform, instead comprising three distinct stages of change, i.e., little change until the 1980s, a significant increase in the next two decades, and a slight but insignificant decrease in the last two decades. Accordingly, evapotranspiration has undergone similar decadal changes over this period. More importantly, there is a spatial pattern of the response to climate change, showing a stronger enhancement of EF in the west TP than in the east TP during 1979–2020. This response pattern shows a weakened contrast in the surface water cycle between the dry west and the wet east TP. The enhanced coupling and diminished spatial contrast may provide a perspective for understanding spatiotemporal changes in environment and ecology. Finally, it is worth pointing out that given the simplicity of the SFE method, it is expected that it can be applied in climate-sensitive regions outside the TP. © 2024

关键词： Climate change Evaporative fraction Spatial pattern Surface flux equilibrium Tibetan Plateau Water-energy coupling

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Learning with Limited Annotations: A Survey on Deep Semi-supervised Learning for Medical Image Segmentation

arXiv

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arXiv 2022年

作者： Jiao, Rushi Zhang, Yichi Ding, Le Xue, Bingsen Zhang, Jicong Cai, Rong Jin, Cheng School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Data Science Fudan University Shanghai200433 China School of Engineering Medicine Beihang University Beijing100191 China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai200433 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Beijing Anding Hospital Capital Medical University Beijing100088 China Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beihang University Beijing100191 China Hefei Innovation Research Institute Beihang University Hefei230012 China

Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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