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BjuIR:A multi-omics database with various tools for accelerating functional genomics research in Brassica juncea

Plant Communications 2024年第8期5卷 96-99页

作者： Linna Zhang Jinyuan Xiao Congyuan Liang Yifan Chen Changchun Yu Xinle Zhao Jiawei Li Mingli Yan Qian Yang Hao Chen Zhongsong Liu Zhengjie Wan Zhiquan Yang Qing-Yong Yang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops College of InformaticsHuazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data College of InformaticsHuazhong Agricultural UniversityWuhan 430070China National Key Laboratory of Crop Genetic Improvement Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070China Yazhouwan National Laboratory Sanya 572025China National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhan 430070China Crop Research Institute Hunan Academy of Agricultural SciencesChangsha 410125China College of Agronomy Hunan Agricultural UniversityChangsha 410128China

Dear Editor,Advances in high-throughput omics technologies,along with methodologies for integrating multi-omics datasets,have substantially enhanced the efficiency of identifying candidate genes in breeding(Gusev et al.,2018;Gupta et al.,2019).However,this process is often complex and *** address this challenge,databases that integrate extensive data and enable convenient and efficient functional genomics studies are being developed(Ma et al.,2021;Yang et al.,2023).

关键词： functional integrating convenient

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Smart Assessment of Urban Old Residential Areas Based on Object Detection

Smart Assessment of Urban Old Residential Areas Based on Obj...

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2023 International Conference on Construction and Real Estate Management: The Human-Centered Construction Transformation, ICCREM 2023

作者： Wang, Shijia Gao, Ming Wang, Hao Gao, Ran School of Management Science and Engineering Central Univ. of Finance and Economics Beijing China School of Management Science and Engineering Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province Dongbei Univ. of Finance and Economics Dalian China

ISBN: (数字)9780784485217

ISBN: (纸本)9780784485217

In the stage of rapid urban renewal, the renovation of old residential areas is moving along very slowly. Traditional methods of old residential areas assessment have the pain points of low efficiency and high consumption of manpower and material resources, while the application of object detection technology in the field of intelligent assessment is more and more extensive, and satisfactory results have been achieved. This paper attempts to explore how to use object detection technology to automatically identify and locate key objects in old communities, such as buildings, parking spaces, and trash bins, so as to realize smart assessment and management of the environment in old residential areas. Through the analysis of experimental results, the effectiveness and practicality of the proposed method are verified. © ASCE.

关键词： Object detection

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Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis

arXiv

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arXiv 2023年

作者： Haoran, Sun Yang, Wang Haipeng, Liu Biao, Qian Meng, Wang Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei China Department of Computer Science and Information Engineering Hefei University of Technology Hefei China

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel Fine-grained text-image Fusion based Generative Adversarial Networks, dubbed FF-GAN, which consists of two modules: Fine-grained text-image Fusion Block (FF-Block) and Global Semantic Refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given *** is available at htt s:// ithub com/haoranhfut/FF-GAN Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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TransD-based Multi-hop Meta Learning for Few-shot knowledge Graph Completion

TransD-based Multi-hop Meta Learning for Few-shot Knowledge ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jindi Li Kui Yu Yuling Li Yuhong Zhang Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Few-shot knowledge graph completion (FKGC), which aims to infer missing facts about a relation from only a few reference triples, has recently attracted great attention. The core of solving the FKGC task is to learn a vector representation for each few-shot relation using the corresponding entity represen-tations. To this end, existing models generally enhance entity representations with their direct neighbors. However, a large number of entities have few direct neighbors. Hence, encoding only direct neighborhood is insufficient to obtain satisfactory en-tity representations. In addition, current models typically utilize static embeddings to represent entities, ignoring their diverse semantics, i.e., an entity may show distinct semantics within different few-shot relations. To address these issues, we propose a new FKGC framework, namely TransD-based Multi-hop Meta Learning (TDML). TDML consists of three main components: a multi-hop neighbor encoder to enhance entity representations by aggregating heterogeneous multi-hop neighbors, a transformer encoder to generate the relation meta representations, and a TransD-based relation representation updater that allows each entity to exhibit relation-specific semantics and tune the relation meta representations. Extensive experiments on two public datasets demonstrate that our model outperforms state-of-the-art FKGC methods.

关键词：

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Unified Image Harmonization with Region Augmented Attention Normalization

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Annals of data Science 2024年第5期11卷 1865-1886页

作者： Hou, Junjie Zhang, Yuqi Su, Duo Sino-Danish College University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China

The image harmonization task endeavors to adjust foreground information within an image synthesis process to achieve visual consistency by leveraging background information. In academic research, this task conventionally involves the utilization of simple synthesized images and matching masks as inputs. However, obtaining precise masks for image harmonization in practical applications poses a significant challenge, thereby creating a notable disparity between research findings and real-world applicability. To mitigate this disparity, we propose a redefinition of the image harmonization task as "Unified Image Harmonization," where the input comprises only a single image, thereby enhancing its applicability in real-world scenarios. To address this challenge, we have developed a novel framework. Within this framework, we initially employ inharmonious region localization to detect the mask, which is subsequently utilized for harmonization tasks. The pivotal aspect of the harmonization process lies in normalization, which is accountable for information transfer. Nonetheless, the current background-to-foreground information transfer and guidance mechanisms are limited by single-layer guidance, thereby constraining their effectiveness. To overcome this limitation, we introduce Region Augmented Attention Normalization (RA2N), which enhances the attention mechanism for foreground feature alignment, consequently leading to improved alignment and transfer capabilities. Through qualitative and quantitative comparisons on the iHarmony4 dataset, our model exhibits exceptional performance not only in unified image harmonization but also in conventional image harmonization tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Image reconstruction

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Rethinking Distribution Alignment for Inter-class Fairness 1

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Ye, Jinhuang Wu, Jiawei Li, Zuoyong Zheng, Xianghan College of Computer and Big Data Fuzhou University Fuzhou350108 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789819997886

ISBN: (纸本)9789819997879

Semi-supervised learning (SSL) is a successful paradigm that can use unlabelled data to alleviate the labelling cost problem in supervised learning. However, the excellent performance brought by SSL does not transfer well to the task of class imbalance. The reason is that the class bias of pseudo-labelling further misleads the decision boundary. To solve this problem, we propose a new plug-and-play approach to handle the class imbalance problem based on a theoretical extension and analysis of distribution alignment. The method, called Basis Transformation Based Distribution Alignment (BTDA), efficiently aligns class distributions while taking into account inter-class *** implements the basis transformation through a learnable transfer matrix, thereby reducing the performance loss caused by pseudo-labelling biases. Extensive experiments show that our proposed BTDA approach can significantly improve performance in class imbalance tasks in terms of both accuracy and recall metrics when integrated with advanced SSL algorithms. Although the idea of BTDA is not complex, it can show advanced performance on datasets such as CIFAR and SVHN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Gender Disparity in Expressed Emotions within Health-Related Online Support Groups

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Proceedings of the Association for Information Science and Technology 2021年第1期58卷 883-885页

作者： Zhao, Yuehua Wang, Hao Deng, Sanhong Chen, Ye Nanjing University Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing China Central China Normal University Wuhan China

Online support groups offer a new way to users to communicate with others regarding certain health issues. Taking autism-related support groups on Facebook as an example, we examine whether the expressed emotions differ between female and male users in online health-related support groups and whether such gender disparity varied based on the topics of the groups. Experimental results reveal a significant gender difference of expressed emotions in the groups. We find that female users tended to express more positive emotions in the group discussions than the male group members did. In addition, users appeared to express different sentiments within the groups focused on various topics. Male users tend to convey more negative emotions in the group that related to treatment, while female users were more positive when posted in the research-related group than male users were. This study is beneficial for tracking and moderating the emotional environment in online support groups. 84 Annual Meeting of the Association for Information Science & Technology | Oct. 29 – Nov. 3, 2021 | Salt Lake City, UT. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Sentiment analysis

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Understanding transitions in animated bar charts

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Visual Intelligence 2023年第1期1卷 1-15页

作者： Wei, Datong Liu, Can Zhang, Xiaolong Yuan, Xiaoru Key Laboratory of Machine Perception (Ministry of Education) and the School of Intelligence Science and Technology Peking University Beijing China College of Information Sciences and Technology Pennsylvania State University University Park United States National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China

Animated bar charts have gained popularity on the Internet for their ability to display historical rankings through smooth animated transitions that clearly show the rankings over time. To investigate the usage scenarios of these charts, we analyzed the pop-up comments to understand viewers’ focus. Our findings suggest that animated transitions play a key role in viewers’ perception of ranking changes. We summarized various transition methods and evaluated their impact on user perception and memory through user experiments. We also conducted a detailed analysis of each transition method’s implementation. Our research sheds light on the effectiveness of different transition strategies for animated bar charts. © The Author(s) 2023.

关键词： Animation Quantitative evaluation Transition Visualization for the masses

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knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

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data Intelligence 2022年第3期4卷 573-598页

作者： Danyang Hu Meng Wang Feng Gao Fangfang Xu Jinguang Gu School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration of the People’s Republic of ChinaBeijing 10038China Institute of Big Data Science and Engineering Wuhan University of Science and TechnologyWuhan 430065China

Temporal information is pervasive and crucial in medical records and other clinical text,as it formulates the development process of medical conditions and is vital for clinical decision ***,providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is *** order to capture complex temporal semantics in clinical text,we propose a novel Clinical Time Ontology(CTO)as an extension from OWL *** specifically,we identified eight timerelated problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time,cyclic time,irregular time,negations and other complex aspects of clinical ***,we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple ***,we provided a formulaic and graphical presentation of complex time and complex time *** carried out empirical study on the expressiveness and usability of CTO using real-world healthcare ***,experiment results demonstrate that CTO could faithfully represent and reason over 93%of the temporal expressions,and it can cover a wider range of time-related classes in clinical domain.

关键词： Clinical text Temporal ontology Temporal relations OWL Negation of temporal relation

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Sharing Economy in Local Energy Markets

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 714-726页

作者： Zhaoyuan Wu Jianxiao Wang Haiwang Zhong Feng Gao Tianjiao Pu Chin-Woo Tan Xiupeng Chen Gengyin Li Huiru Zhao Ming Zhou Qing Xia School of Economics and Management North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China State Key Lab of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China China Electric Power Research Institute Beijing 100192China Department of Civil and Environmental Engineering Stanford UniversityPalo AltoCA 94305USA Engineering and Technology Institute Groningen University of Groningen9742 AG Groningenthe Netherlands

With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of power consumption behavior,the low utilization rate of flexible resources,and difficulties in cost *** the core idea of“access over ownership”,the concept of the sharing economy has gained substantial popularity in the local energy market in recent ***,we provide an overview of the potential market design for the sharing economy in local energy markets(LEMs)and conduct a detailed review of research related to local energy sharing,enabling technologies,and potential *** paper can provide a useful reference and insights for the activation of demand-side flexibility ***,this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

关键词： Energy sharing flexibility potential local energy market information and communication technology sharing economy

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