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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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Nonvolatile waveguide transmission tuning with electrically-driven ultra-small GST phase-change material

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Science Bulletin 2019年第11期64卷 782-789页

作者： Hanyu Zhang Linjie Zhou Jian Xu Ningning Wang Hao Hu Liangjun Lu B. M. A. Rahman Jianping Chen State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Laboratory of Navigation and Location ServicesShanghai Institute for Advanced Communication and Data ScienceDepartment of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical and Electronic Engineering CityUniversity of LondonLondon EC IV OHBUK

Low-power reconfigurable optical circuits are highly demanded to satisfy a variety of different applications. Conventional electro-optic and thermo-optic refractive index tuning methods in silicon photonics are not suitable for reconfiguration of optical circuits due to their high static power consumption and volatility. We propose and demonstrate a nonvolatile tuning method by utilizing the reversible phase change property of GST integrated on top of the silicon waveguide. The phase change is enabled by applying electrical pulses to the lm-sized GST active region in a sandwich structure. The experimental results show that the optical transmission of the silicon waveguide can be tuned by controlling the phase state of GST.

关键词： Phase change material Nonvolatile optical switch Integrated optics devices Chalcogenide Ge2Sb2Te5

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Robust Low-rank Deep Feature Recovery in CNNs: Toward Low Information Loss and Fast Convergence

Robust Low-rank Deep Feature Recovery in CNNs: Toward Low In...

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IEEE International Conference on data Mining (ICDM)

作者： Jiahuan Ren Zhao Zhang Jicong Fan Haijun Zhang Mingliang Xu Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Data Science The Chinese University of Hong Kong (Shenzhen) & Shenzhen Research Institute of Big Data Shenzhen China Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665423991

Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make the model converge in training, due to the useful information loss during the convolution and pooling operations. We therefore propose a general feature recovery layer, termed Low-rank Deep Feature Recovery (LDFR), to enhance the representation ability of the convolutional features by seamlessly integrating low-rank recovery into CNNs, which can be easily extended to all existing CNNs-based models. To be specific, to recover the lost information during the convolution operation, LDFR aims at learning the low-rank projections to embed the feature maps onto a low-rank subspace based on some selected informative convolutional feature maps. Such low-rank recovery operation can ensure all convolutional feature maps to be reconstructed easily to recover the underlying subspace with more useful and detailed information discovered, e.g., the strokes of characters or the texture information of clothes can be enhanced after LDFR. In addition, to make the learnt low-rank subspaces more powerful for feature recovery, we design a fusion strategy to obtain a generalized subspace, which averages over all learnt sub-spaces in each LDFR layer, so that the convolutional feature maps in test phase can be recovered effectively via low-rank embedding. Extensive results on several image datasets show that existing CNNs-based models equipped with our LDFR layer can obtain better performance.

关键词： Training Image recognition Convolution Conferences Image representation data mining Convolutional neural networks

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Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks

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IEEE Transactions on Multimedia 2021年 23卷 2019-2032页

作者： Lei Sang Min Xu Shengsheng Qian Matt Martin Peter Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia Institute of Automation Chinese Academy of Sciences Beijing China INTERACT Technology Sydney NSW Australia Mininglamp Academy of Sciences Mininglamp Technology Beijing China

With the emergence of online social networks (OSNs), video recommendation has come to play a crucial role in mitigating the semantic gap between users and videos. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users’ preferences. Although these methods have achieved promising results, they fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data from OSNs. In this paper, we study the problem of video recommendation in Heterogeneous Information Networks (HINs) due to its excellence in characterizing heterogeneous and complex context information. We propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. Then, each video is represented as the composition of the multimodal content feature and global dependency structure information using an attention network. The learned video embedding with sequential based recommendation are jointly optimized for the final rating prediction. Experimental results on real-world YouTube video recommendation scenarios demonstrate the effectiveness of the proposed methods compared with strong baselines.

关键词： Semantics Collaboration YouTube Australia Visualization Context modeling

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Robust and Fast Measure of Information via Low-rank Representation

arXiv

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arXiv 2022年

作者： Dong, Yuxin Gong, Tieliang Yu, Shujian Chen, Hong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China Machine Learning Group UiT - The Arctic University of Norway Norway College of Science Huazhong Agriculture University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

The matrix-based Rényi’s entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi’s entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi’s entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n3) to O(n2s) or even O(ns2), where n is the number of data samples and s n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi’s entropy in terms of both performance and computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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The minority matters: a diversity-promoting collaborative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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LadRa-Net: Locally-aware dynamic re-read attention net for sentence semantic matching

arXiv

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arXiv 2021年

作者： Zhang, Kun Lv, Guangyi Wu, Le Chen, Enhong Liu, Qi Wang, Meng Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Anhui Hefei230029 China AI Lab at Lenovo Research Beijing100094 China University of Science and Technology of China Hefei230026 China

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Language Inference (NLI), Paraphrase Identification (PI), and so on. Much recent progress has been made in this area, especially attention-based methods and pre-trained language model based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of attention mechanism. In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations. Based on this attention variation, we develop a novel Dynamic Re-read Network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in dynamic re-read attention seems insufficient for sentence semantics, and employing pre-trained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRr-Net to Locally-Aware Dynamic Re-read Attention Net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of Byte-Pair Encoding (BPE) in pre-trained language models and boost the performance of dynamic reread attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research. © 2021, CC BY.

关键词： Computational linguistics

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knowledge Graph for China’s Genealogy

Knowledge Graph for China’s Genealogy

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IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Tingting Jiang Yi Zhu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Information Engineering Yangzhou University Yangzhou China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical roots, and explore the origins of a family more easily. However, the multi-type, multisource dynamic changes and specialized nature of genealogical data bring challenges to the development of contemporary knowledge graph models. Applying existing methods to genealogical data can result in problems of overlooking certain specialized vocabulary and dynamic properties such as personal experiences. In this paper, we propose a genealogical knowledge graph model GKGM that combines HAO intelligence (h uman intelligence + a rtificial intelligence + o rganizational intelligence) and ontology granularity division technology to address the above problems. Furthermore, a method of applying the model to construct genealogical knowledge graphs is demonstrated, and an experiment conducted on a real-world genealogical dataset verifies the feasibility and effectiveness of the model.

关键词： Ontologies data models Blood data mining Biological system modeling History

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Extricating from GroundTruth: An Unpaired Learning Based Evaluation Metric for Image Captioning

Extricating from GroundTruth: An Unpaired Learning Based Eva...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhong-Qiu Zhao Yue-Lin Sun Nan-Xun Wang Wei-Dong Tian Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Recently, instead of pursuing high performance on classical evaluation metrics, the research focus of image captioning has shifted to generating sentences which are more vivid and stylized than human-written ones. However, there are still no applicable metrics which can judge how close the generated captions are to the human-written ones. In this paper, we propose a novel learning-based evaluation metric, namely Unpaired Image Captioning Evaluation (UICE), which can be trained to distinguish between human-written and generated captions. Unlike existing metrics, our UICE consists of two parts: the semantic alignment module measuring the semantic distance between extracted image features and caption meanings, and the syntactic discriminating module syntactically judging how human-like the candidate caption is. The semantic alignment module is implemented by mapping the image features and the word embedding into a unified tensor space. And the syntactic discriminating module is designed to be learning-based, and thereby can be trained to be stylized by users' own, fed with additional personalized corpus during the training process. Extensive experiments indicate that our metric can correctly judge the grammatical correctness of generated captions and the semantic consistency between captions and corresponding images.

关键词： Measurement Semantics Syntactics Feature extraction Training Pathology SPICE

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Toward PTNET Network Topology Analysis and Routing Algorithm Design 6th

Toward PTNET Network Topology Analysis and Routing Algorithm...

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6th International Conference on data Science, ICDS 2019

作者： Han, Zhijie Zhang, Qingfang Du, Xiaoyu Guo, Kun He, Mingshu Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China School of Computer and Information Engineering Henan University KaifengHenan475004 China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789811528095

In recent years, data center network is used for transmission, storage and processing of big data, which plays an important role for applications in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server-centric data center network topology can ensure the scale of the data center network by recursively increasing the number of network nodes and links. relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server-centric data center network topology, which has more advantages in scalability, fault tolerance and so on. Multicast and broadcast in data center network have more application scenarios and use value. For example, the video conference online, multimedia remote education and other development are inseparable from the application and promotion of network multicast and broadcast. So it is necessary to research the routing algorithms of multicast and broadcast in the network. Based on the deep research of PTNet network, this paper analyzes and researches the network topology, multicast and broadcast routing algorithm. © Springer Nature Singapore Pte Ltd 2020.

关键词： Network topology

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