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Comparative analysis of SIMILAR to RCD ONE(SRO)family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第1期7卷 45-57页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture FaisalabadSub-Campus BurewalaVehari 61010Pakistan Department of Chemistry and Biochemistry University of AgricultureFaisalabad 38400Pakistan Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad 38400Pakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabad 38400Pakistan Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud ComputingCentre for Advanced Studies in Agriculture and Food SecurityUniversity of Agriculture FaisalabadFaisalabad 38400Pakistan Department of Plant Pathology University of CaliforniaDavisCA 95616USA State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural SciencesAnyangHenan 455000China Federal Seed Certification and Registration Department Ministry of National Food Security and ResearchIslamabad 44090Pakistan School of Agriculture Sciences Zhengzhou UniversityZhengzhou 450000China

Background SRO(Similar to RCD1)genes family is largely recognized for their importance in the growth,develop-ment,and in responding to environmental ***,genome-wide identification and functional characteri-zation of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribu-tion of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target 12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes dur-ing ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

关键词： Cotton SRO miRNAs Gene duplications Gene expression Ovule development

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Differentially private federated knowledge graphs embedding

arXiv

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arXiv 2021年

作者： Peng, Hao Li, Haoran Song, Yangqiu Zheng, Vincent Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Computer Science and Engineering HKUST Hong Kong AI Group Webank Co. Ltd School of Cyber Science and Technology Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China SKLSDE Beihang University Beijing100191 China

Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data. In addition, the centralized embedding model may not scale to the extensive real-world knowledge graphs. Therefore, we propose a novel decentralized scalable learning framework, Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous and peer-to-peer manner while being privacy-preserving. FKGE exploits adversarial generation between pairs of knowledge graphs to translate identical entities and relations of different domains into near embedding spaces. In order to protect the privacy of the training data, FKGE further implements a privacy-preserving neural network structure to guarantee no raw data leakage. We conduct extensive experiments to evaluate FKGE on 11 knowledge graphs, demonstrating a significant and consistent improvement in model quality with at most 17.85% and 7.90% increases in performance on triple classification and link prediction tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Matrix Classifier On Dynamic Functional Connectivity For Mci Identification

Matrix Classifier On Dynamic Functional Connectivity For Mci...

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IEEE International Conference on Image Processing

作者： Lei Zhou Liang Zhang Xiao Bai Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Software Development Environment Beihang University Beijing School of Information and Communication Technology Griffith University Nathan

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

关键词： Feature extraction Correlation Dementia Microsoft Windows Spatiotemporal phenomena Training

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Distributed State Estimation for Heterogeneous Mobile Sensor Networks with Varying Nodes

Distributed State Estimation for Heterogeneous Mobile Sensor...

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第三十九届中国控制会议

作者： Yingrong Yu Jie Ren Xiwang Dong Qingdong Li Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing Aerospace System Engineering Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

On background of cooperative air combat, state estimation of aerial maneuvering target with multi-sensor cooperative reconnaissance is discussed in this paper. Multiple moving platforms equipped with different kinds of detectors construct heterogeneous mobile sensor networks. Graph theory is adopted to describe the communication topology of the networks. In consideration of detection range limit, the number of sensors participating in the detection changes over time. An interactive multiple model algorithm（IMM） is utilized for target motion modeling. The local observation information is processed by Cubature Information Filter（CIF）. A weighted average consensus-based state estimation method for heterogeneous mobile sensor networks with varying nodes is put forward to fuse information from different sensors and obtain a consistent and precise tracking result. Moreover, the stability analysis of proposed method is provided detailed. Finally, numerical simulation results are given to illustrate the effectiveness of the theoretical results.

关键词： Cubature Information Filter consensus theory distributed state estimation mobile sensor networks stochastic boundedness

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Adaptive Distributed State and Input Estimation Using Retrospective-Cost-Based Information Filter

Adaptive Distributed State and Input Estimation Using Retros...

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第三十九届中国控制会议

作者： Hong Wang Liang Han Yuan Liang Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University School of Sino-French Engineer Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

In this paper, the problem of distributed state and input estimation using sensor networks for linear system is investigated. First, the retrospective-cost-based information filter（RCIF） is proposed to estimate the state and input simultaneously, by combining the retrospective cost input estimator subsystem and information filter state estimator subsystem. Next, the retrospective-cost adaptive input estimator subsystem is formulated, which utilizes retrospective cost optimization and recursive minimum mean square estimation to drive the estimated input to approximate the actual input without prior information. Then,the consensus algorithm is used to extend the RCIF to distributed estimation, and to improve the convergence rate, the adaptive update law of consensus weights is presented. Finally, a simulation example is illustrated to validate the effectiveness and feasibility of the proposed algorithm.

关键词： Adaptive Distributed Estimation State estimation Input estimation Retrospective cost optimization Consensus

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Dynamic aspiration based on win-stay-lose-learn rule in spatial prisoner's dilemma game

arXiv

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arXiv 2020年

作者： Shi, Zhenyu Wei, Wei Feng, Xiangnan Li, Xing Zheng, Zhiming School of Mathematical Sciences Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China Peng Cheng Laboratory ShenzhenGuangdong China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang China

Prisoner’s dilemma game is the most commonly used model of spatial evolutionary game which is considered as a paradigm to portray competition among selfish individuals. In recent years, Win-Stay-Lose-Learn, a strategy updating rule base on aspiration, has been proved to be an effective model to promote cooperation in spatial prisoner’s dilemma game, which leads aspiration to receive lots of attention. But in many research the assumption that individuals aspiration is fixed is inconsistent with recent results from psychology. In this paper, according to Expected Value Theory and Achievement Motivation Theory, we propose a dynamic aspiration model based on Win-Stay-Lose-Learn rule in which individual’s aspiration is inspired by its payoff. It is found that dynamic aspiration has a significant impact on the evolution process, and different initial aspirations lead to different results, which are called Stable Coexistence under Low Aspiration, Dependent Coexistence under Moderate aspiration and Defection Explosion under High Aspiration respectively. Furthermore, a deep analysis is performed on the local structures which cause cooperator’s existence or defector’s expansion, and the evolution process for different parameters including strategy and aspiration. As a result, the intrinsic structures leading to defectors’ expansion and cooperators’ survival are achieved for different evolution process, which provides a penetrating understanding of the evolution. Compared to fixed aspiration model, dynamic aspiration introduces a more satisfactory explanation on population evolution laws and can promote deeper comprehension for the principle of prisoners dilemma. Copyright © 2020, The Authors. All rights reserved.

关键词： Expansion

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Cross-reference stitching quality assessment for 360◦ omnidirectional images 27

Cross-reference stitching quality assessment for 360◦ omnid...

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27th ACM International Conference on Multimedia, MM 2019

作者： Li, Jia Yu, Kaiwen Zhao, Yifan Zhang, Yu Xu, Long State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University China National Astronomical Observatories Chinese Academy of Sciences China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (纸本)9781450368896

Along with the development of virtual reality (VR), omnidirectional images play an important role in producing multimedia content with an immersive experience. However, despite various existing approaches for omnidirectional image stitching, how to quantitatively assess the quality of stitched images is still insufficiently explored. To address this problem, we first establish a novel omnidirectional image dataset containing stitched images as well as dual-fisheye images captured from standard quarters of 0◦, 90◦, 180◦, and 270◦. In this manner, when evaluating the quality of an image stitched from a pair of fisheye images (e.g., 0◦ and 180◦), the other pair of fisheye images (e.g., 90◦ and 270◦) can be used as the cross-reference to provide ground-truth observations of the stitching regions. Based on this dataset, we propose a set of Omnidirectional Stitching Image Quality Assessment (OS-IQA) metrics. In these metrics, the stitching regions are assessed by exploring the local relationships between the stitched image and its cross-reference with histogram statistics, perceptual hash and sparse reconstruction, while the whole stitched images are assessed by the global indicators of color difference and fitness of blind *** and quantitative experiments show our method outperforms the classic IQA metrics and is highly consistent with human subjective evaluations. To the best of our knowledge, it is the first attempt that assesses the stitching quality of omnidirectional images by using cross-references. © 2019 Association for computing Machinery.

关键词： Virtual reality

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Vehicle re-identification with location and time stamps 32

Vehicle re-identification with location and time stamps

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32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019

作者： Lv, Kai Du, Heming Hou, Yunzhong Deng, Weijian Sheng, Hao Jiao, Jianbin Zheng, Liang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University University of Chinese Academy of Sciences Australian National University

ISBN: (纸本)9781728125060

This paper focuses on the problem of vehicle re-identification (Re-ID). In our attempt, we propose a re-identification framework by exploiting vehicle location and time stamps. The location and time information have the potential to cover the shortage of appearance-based feature representations. First, we introduce an ensemble technique to combine the informative cues of multiple Re-ID models effectively. To further improve the accuracy, we then build up a system to acquire the vehicle location and time stamps. Specifically, we utilize the detected results to obtain the needed information. With the help of the proposed system, we can remove irrelevant images from a given ranking list. Our system finished 3rd place in the 2019 AI-City challenge for city-scale multi-camera vehicle re-identification. © 2019 IEEE Computer Society. All rights reserved.

关键词： Vehicles

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On the Detection of Shilling Attacks in Federated Collaborative Filtering

On the Detection of Shilling Attacks in Federated Collaborat...

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Reliable Distributed Systems

作者： Yangfan Jiang Yipeng Zhou Di Wu Chao Li Yan Wang School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University) Ministry of Education China Macquarie University Sydney NSW Australia Tencent Inc Shenzhen China

ISBN: (数字)9781728176260

ISBN: (纸本)9781728176277

Federated collaborative filtering (Fed-CF) is a variant of federated learning (FL) models, which can protect user privacy in recommender systems. In Fed-CF, the recommendation model is collectively trained across multiple decentralized clients by exchanging gradients only. However, the decentralized nature of Fed-CF makes it vulnerable to shilling attacks, which can be realized by inserting fake ratings of target items to distort recommendation results. Unfortunately, previous detection algorithms cannot work well in the FL framework, as all original data samples are not disclosed at all. In this paper, we are the first to systematically study the problem of shilling attacks in the context of federated learning, and propose an effective detection method called Federated Shilling Attack Detector (FSAD) to detect shilling attackers in Fed-CF. We first show the feasibility of shilling attacks in Fed-CF. Next, we dedicatedly design four novel features based on exchanged gradients among clients. By incorporating these gradient-based features, we train a semi-supervised Bayes classifier to identify shilling attackers effectively. Finally, we conduct extensive experiments based on real-world datasets to evaluate the performance of our proposed FSAD method. The experimental results show that FSAD can detect shilling attackers in Fed-CF with high accuracy, with the F 1 value as high as 0.90 on the Netflix dataset, which approaches the performance of the optimal detector that utilizes complete private user information for detection.

关键词： Privacy Collaborative filtering Detectors Collaborative work Information filters Reliability Recommender systems

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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Chinese Control and Decision Conference, CCDC

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Engineering RMIT University Melbourne VIC Australia

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This brief explores the approximation properties of a unique basis expansion based on Pascal's triangle, which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time representation. The roles of certain parameters, such as sampling time interval or model order, and signal characteristics, i.e., its curvature, on the approximation are investigated. Approximate errors in one and multiple-step predictions are analyzed. Furthermore, time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Shape Electronic mail Analytical models Mathematical analysis data models Upper bound

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