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The lifestyle transition of Arthrobotrys oligospora is mediated by microRNA-like RNAs

Science China(Life Sciences) 2020年第4期63卷 543-551页

作者： Xinglai Ji Heng Li Weihua Zhang Jiai Wang Lianming Liang Chenggang Zou Zefen Yu Shuqun Liu Ke-Qin Zhang State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan Yunnan UniversityKunming 650091China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Medical Biology Chinses Academy of Medical Sciences&Peking Union Medical CollegeKunming 650118China

The lifestyle transition of fungi,defined as switching from taking organic material as nutrients to pathogens,is a fundamental phenomenon in ***,the mechanisms of such transition remain largely *** we show microRNA-like RNAs(milRNAs)play a key role in fungal lifestyle transition for the first *** identified milRNAs by small RNA sequencing in Arthrobotrys oligospora,a known nematode-trapping *** them,7 highly expressed milRNAs were confirmed by northern-blot *** out two milRNAs significantly decreased ***’s ability to switch *** further identified that two of these milRNAs were associated with argonaute protein QDE-2 by RNA-immunoprecipitation(RIP)*** of the predicted target genes of milRNAs were found in immunoprecipitation(IP)products of *** of argonaute gene qde-2 also led to serious defects in lifestyle ***,knocking out individual milRNAs or qde-2 lead to diverse responses under different conditions,and qde-2 itself may be targeted by the ***,it indicates the lifestyle transition of fungi is mediated by milRNAs through RNA interference(RNAi)machinery,revealing the wide existence of miRNAs in fungi kingdom and providing new insights into understanding the adaptation of fungi from scavengers to predators and the mechanisms underlying fungal infections.

关键词： lifestyle transition Arthrobotrys oligospora,microRNA-like RNAs nematode-trapping fungi trap induction fungal adaptation

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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USER: A unified information search and recommendation model based on integrated behavior sequence

arXiv

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arXiv 2021年

作者： Yao, Jing Dou, Zhicheng Xie, Ruobing Lu, Yanxiong Wang, Zhiping Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China WeChat Search Application Department Tencent China Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal - satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently - they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information SEarch and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way. Experiments on a dataset from a real-world information content service platform verify that our model outperforms separate search and recommendation baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

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Dynamic programming based top-k aggregate queries in uncertain database

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Journal of Information and Computational Science 2009年第3期6卷 1589-1596页

作者： Liu, Dexi School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China

A Top-k aggregate query ranks groups of tuples by their aggregate values, sum or average for example, and returns k groups with the highest aggregate values. We propose a dynamic programming based method to process uncertain kRanks aggregate queries in uncertain database, where the number of retrieved tuples and group states generated on these tuples are minimized. Our method has two levels, group state generation and U-x-kRanks query processing. In the former level, group states, which satisfy the properties of x-tuple [13], are generated one after the other according to their aggregate values, while in the latter level, dynamic programming based uncertain x-tuple kRanks query processing [15] are employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions. 1548-7741/ Copyright © 2009 Binary Information Press.

关键词： Dynamic programming

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Managing data provenance in database

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Journal of Information and Computational Science 2009年第1期6卷 423-431页

作者： Liu, Xiping Wan, Changxuan Jiang, Tengjiao School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China

The paper studies the management problem of data provenance. Firstly, two data models, which enhance the traditional relational model and tree model, are proposed to reveal the basic nature of data provenance. To answer the new challenges to data management posed by data provenance, DBPro, a new database with the ability of managing both data and provenance, is introduced. The storage of data provenance is a core problem in designing DBPro, which is coped with two storage schemes. The comparison between these two storage schemes is also conducted. © 2009 Binary Information Press.

关键词： database systems

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Predictable recovery for replication real-time main memory databases using request logs

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Journal of Information and Computational Science 2009年第3期6卷 1607-1614页

作者： Liao, Guoqiong Li, Jing School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China

Reliable telecommunication applications in future need the supports from replication real-time main memory databases. In order to improve recovery performance and provide predictable recovery, this paper proposes a new recovery method using request logs for the databases. Firstly, the format and correctness of request logs are discussed. Then, a recovery model based on request logs is given. Thirdly, the recovery algorithms for local recovery and resynchronization are provided in details. At last, a formula to estimate denial-of-service time (DoS) is presented for predictable recovery. 1548-7741/ Copyright © 2009 Binary Information Press.

关键词： Recovery

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A temporal object-oriented RFID data model for object tracking and monitoring in supply chain

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Journal of Information and Computational Science 2009年第3期6卷 1505-1513页

作者： Deng, Kun Liao, Guoqiong School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China

With the significant advantages of RFID technologies, RFID is being gradually adopted and deployed in a wide area of applications, such as supply chain and logistics. Since RFID data has the characteristics including time-dependent, dynamically changing, large volumes, and carrying implicit semantics, relational data model is not suitable to express and handle RFID data for object tracking and monitoring applications in supply chain and logistics application. This paper first proposes a temporal object-oriented RFID data model based on EPCIS specification, and then the model implementation based on TOODM is introduced in details. In the end, some query examples using TOSQL to track and monitor objects are given. The results shows that our model can express the semantics of RFID data directly and can deal with temporal queries effectively. 1548-7741/ Copyright © 2009 Binary Information Press.

关键词： Radio frequency identification (RFID)

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A hybrid model for explaining older adult' continuance intention toward SNSs

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International Journal of Smart Home 2015年第1期9卷 93-102页

作者： Liu, Wei Jiangxi Key Laboratory of Data and Knowledge Engineering Institute of Information Resources Management School of Information Technology Jiangxi University of Finance and Economics Nanchang China

Due to the rapid growth of social net services (SNSs), research into SNSs continuance usage has recently emerged as an important issue in information systems adaption. This study develops an integrated model based on the expectation-confirmation model (ECM), the technology acceptance model (TAM), and the theory of planned behavior (TPB), and apply them into the context of older adults' continuance intention toward SNSs. The hypothesized model is validated empirically using a sample collected from 250 older adults who had prior experience with SNSs and was tested against the proposed research model using structure equation modeling. Analysis results demonstrate that satisfaction has the most significant effect on older adults' continuance intention, followed by perceived usefulness, attitude, subject norm and perceived behavioral control. The results of these findings for SNSs practitioners are discussed at the end of this work. © 2015 SERSC.

关键词： Social networking (online)

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Commit processing of mobile distributed real-time transactions based on mobile agents

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Journal of Information and Computational Science 2009年第3期6卷 1627-1634页

作者： Di, Guoqiang Liu, Yaoyao Liao, Guoqiong School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China

In mobile computing environment, the features of limited resources, frequent disconnection and long transactions make the success rate of mobile distributed real-time transactions low. Through analyzing the factors impacting the execution of mobile distributed real-time transactions, this paper suggests a mobile distributed real-time transaction model based on mobile agents firstly. Then, a nested advance commit protocol (NACP) is presented, which allows the transactions executing in fixed server to release locks early. The results of performance testing show that NACP can not only effectively increase the successful rate of mobile distributed real-time transactions, but also support disconnection well. 1548-7741/ Copyright © 2009 Binary Information Press.

关键词： Mobile agents

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Joint-Motion Mutual Learning for Pose Estimation in Video 24

Joint-Motion Mutual Learning for Pose Estimation in Video

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A STAR Singapore Singapore Georgia Institute of Technology AtlantaGA United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798400706868

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a by-product of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatio-temporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. © 2024 ACM.

关键词： Motion estimation

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