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Single-cell technologies:From research to application

The Innovation 2022年第6期3卷 92-108页

作者： Lu Wen Guoqiang Li Tao Huang Wei Geng Hao Pei Jialiang Yang Miao Zhu Pengfei Zhang Rui Hou Geng Tian Wentao Su Jian Chen Dake Zhang Pingan Zhu Wei Zhang Xiuxin Zhang Ning Zhang Yunlong Zhao Xin Cao Guangdun Peng Xianwen Ren Nan Jiang Caihuan Tian Zi-Jiang Chen Biomedical Pioneering Innovation Centre(BIOPIC) Peking UniversityBeijing 100871China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China School of Chemical Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Mozhuo Biotech(Zhejiang)Co. Ltd.TongxiangJiaxing 314500China Geneis(Beijing)Co. Ltd.Beijing 100102China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China School of Food Science and Technology Dalian Polytechnic UniversityDalian 116034China State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationBeijing Advanced Innovation Center for Biomedical EngineeringSchool of Engineering MedicineBeihang UniversityBeijing 100083China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Center of Peony Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Flower Crops(North China)Key Laboratory of Biology and Genetic Improvement of Horticultural CropsMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Hepatic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Advanced Technology Institute University of SurreyGuildfordSurreyGU27XHUK National Physical Laboratory TeddingtonMiddlesex TW110LWUK Institute of Clinical Science Zhongshan HospitalFudan UniversityShanghai 200032China West China School of Basic Medical Sciences&Forensic Medicine Sichuan UniversityChengdu 610041China Jinfeng Laboratory Chongqing 401329China Center for Reproduc

In recent years,more and more single-cell technologies have been developed.A vast amount of single-cell omics data has been generated by large projects,such as the Human Cell Atlas,the Mouse Cell Atlas,the Mouse RNA Atlas,the Mouse ATAC Atlas,and the Plant Cell *** on these single-cell big data,thousands of bioinformatics algorithms for quality control,clustering,cell-type annotation,developmental inference,cell-cell transition,cell-cell interaction,and spatial analysis are *** powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence,the molecular landscape at the single-cell level can be revealed.

关键词： Atlas landscape application

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Modeling topical relevance for multi-turn dialogue generation

arXiv

引用

arXiv 2020年

作者： Zhang, Hainan Lan, Yanyan Pang, Liang Chen, Hongshen Ding, Zhuoye Yin, Dawei CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS China *** Beijing China University of Chinese Academy of Sciences China

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly. However, existing models usually use word or sentence level similarities to detect the relevant contexts, which fail to well capture the topical level relevance. In this paper, we propose a new model, named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole training dataset. Then, the topic level attention weights are computed based on the topic representation of each context. Finally, the attention weights and the topic distribution are utilized in the decoding process to generate the corresponding responses. Experimental results on both Chinese customer services data and English Ubuntu dialogue data show that STAR-BTM significantly outperforms several state-of-the-art methods, in terms of both metric-based and human evaluations. Copyright © 2020, The Authors. All rights reserved.

关键词： Stars

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Explore the Impact of Cellular Resource Allocation on Mobile UHD Video Streaming over 5G UDN

Explore the Impact of Cellular Resource Allocation on Mobile...

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IEEE Conference on Wireless Communications and networking

作者： Xinjing Yuan Lingjun Pu Xiaohang Xu Jingdong Xu Institute of Systems and Networks College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China State Key Laboratory of Integrated Services Networks (Xidian University) Xi’an China

The incoming 5G cellular network is stepping into a densification era, where various kinds of base stations are densely deployed to provide fruitful mobile services such as video streaming. In order to improve the performance of these mobile services, the way to optimally allocate cellular resources for the network-wide users is a crucial problem. In this paper, we consider the mobile Ultra-High-Definition (UHD) video streaming service, envision a 5G ultra dense network (UDN) consisting of a series of Video Base Stations (VBSs) dedicated to video streaming services for multiple users, and mainly explore the impact of cellular resource allocation on video streaming. We incorporate two important video streaming states into cellular resource allocation and formulate a streaming-aware and fairness-aware cellular resource allocation problem. To deal with the formulated problem, we provide a novel graph transformation and design an optimal matching algorithm which can be solved in polynomial time. Extensive trace-driven simulations validate the superior performance of the proposed algorithm under different user mobility patterns and network scales.

关键词： Cellular networks Base stations Correlation 5G mobile communication Conferences Bit rate Transforms

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Genome Warehouse: A Public Repository Housing Genome-scale data

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Genomics, Proteomics & Bioinformatics 2021年第4期19卷 584-589页

作者： Meili Chen Yingke Ma Song Wu Xinchang Zheng Hongen Kang Jian Sang Xingjian Xu Lili Hao Zhaohua Li Zheng Gong Jingfa Xiao Zhang Zhang Wenming Zhao Yiming Bao National Genomics Data Center Beijing Institute of GenomicsChinese Academy of Sciences/China National Center for BioinformationBeijing 100101China CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Cancer Epidemiology and Genetics National Cancer InstituteNational Institutes of Health.Bethesda.MD 20X92.USA College of Computer Science Technology Inner Mongolia Normal UniversityHohhot 010010China

The Genome Warehouse(GWH)is a public repository housing genome assembly data for a wide range of species and delivering a series of web services for genome data submission,storage,release,and *** one of the core resources in the National Genomics data Center(NGDC),part of the China National Center for Bioinformation(CNCB;https://***),GWH accepts both full and partial(chloroplast,mitochondrion,and plasmid)genome sequences with different assembly levels,as well as an update of existing genome *** each assembly,GWH collects detailed genome-related metadata of biological project,biological sample,and genome assembly,in addition to genome sequence and *** archive high-quality genome sequences and annotations,GWH is equipped with a uniform and standardized procedure for quality *** basic browse and search functionalities,all released genome sequences and annotations can be visualized with *** May 21,2021,GWH has received 19,124 direct submissions covering a diversity of 1108 species and has released 8772 of ***,GWH serves as an important resource for genomescale data management and provides free and publicly accessible data to support research activities throughout the *** is publicly accessible at https://***/gwh.

关键词： Genome submission Genome sequence Genome annotation Genome Warehouse Quality control

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***:An augmented reality-based visualization platform for 3D genome

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The Innovation 2021年第3期2卷 19-20页

作者： Bixia Tang Xiaoxing Li Guan Li Dong Tian Feifei Li Zhihua Zhang CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing 100101China National Genomics Data Center Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China Computer Network Information Center Chinese Academy of SciencesBeijing 100190China School of Life Science University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Many visualization tools have been developed for 3D genome data integration using two-dimensional(2D)devices such as PC monitors or ***,the 2D surface is only suitable for displaying linear data,and it has done little to inform our understanding of the complex interconnections between 3D genome architecture and its various associated-omics *** breakthrough in immersive display technologies,e.g.,virtual reality(VR)and augmented reality(AR),has opened a completely new model for data *** visualization has proved a powerful way to enhance 3D structure-related research,e.g.,protein structure and drug ***,visualization in immersive mode,coupled with the integration of 3D genome and its associated-omics data,is *** a few attempts have been made for single features,e.g.,Juicebox VR,which projects a Hi-C contact matrix into a virtual mountain field,and the WashU Epigenome Browser,which provides a 3D scene for epigenome tracks.A visualization tool for immersive integration of 3D genome architecture with high-dimensional-omics data has not yet been published.

关键词： integration breakthrough visualization

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Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention network

arXiv

引用

arXiv 2021年

作者： Fang, Yuchen Qin, Yanjun Luo, Haiyong Zhao, Fang Xu, Bingbing Wang, Chenxing Zeng, Liang School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications China Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Tsinghua University China

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e.g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas;ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network;iii) the quadratic complexity of the full graph attention introduces heavy computational needs. To achieve the effective traffic flow forecasting, we propose an efficient spectral graph attention network with disentangled traffic sequences. Specifically, the discrete wavelet transform is leveraged to obtain the low- and high-frequency components of traffic sequences, and a dual-channel encoder is elaborately designed to accurately capture the spatio-temporal dependencies under long- and short-term schemas of the low- and high-frequency components. Moreover, a novel wavelet-based graph positional encoding and a query sampling strategy are introduced in our spectral graph attention to effectively guide message passing and efficiently calculate the attention. Extensive experiments on four real-world datasets show the superiority of our model, i.e., the higher traffic forecasting precision with lower computational cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Message passing

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Online Change-Point Detection of Force Signal at the Tip of Surgical Instrument with Morphological Wavelet

Online Change-Point Detection of Force Signal at the Tip of ...

引用

International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yinan Deng Lijun Hao Tangwen Yang Shaotao Dai Guoli Song Institute of Information Science and the Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing China School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation CAS Shenyang China

ISBN: (纸本)9781665431545

Change-point detection of force signal at the tip of surgical tool can provide an early warning signal for the adjustment of robotic controller, potentially avoiding unwanted collisions between end-effector and tissue of human body. In this paper, an online model-free change-point detection method with a small delay and low computational complexity is presented. An averaged-difference morphological operator is first constructed as the detail analysis operator of morphological wavelet (MW) to map time series to MW domain. Then, a significance difference test based on Chebyshev’s principle is presented to compare the means of MW detail coefficients in the past and current windows, to determine whether a change-point has occurred. Experiment results on simulated force signals demonstrate the reliability and timeliness of the proposed method.

关键词： Wavelet domain Mechatronics Machine vision Instruments Force Time series analysis Chebyshev approximation

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Are Neural Ranking Models Robust?

arXiv

引用

arXiv 2021年

作者： Wu, Chen Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing NO. 6 Kexueyuan South Road Haidian District Beijing100190 China

Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking models that can generalize well on new data. There has been less attention paid to the robustness perspective. Unlike the effectiveness which is about the average performance of a system under normal purpose, robustness cares more about the system performance in the worst case or under malicious operations instead. When a new technique enters into the real-world application, it is critical to know not only how it works in average, but also how would it behave in abnormal situations. So we raise the question in this work: Are neural ranking models robust? To answer this question, firstly, we need to clarify what we refer to when we talk about the robustness of ranking models in IR. We show that robustness is actually a multi-dimensional concept and there are three ways to define it in IR: 1) The performance variance under the independent and identically distributed (I.I.D.) setting;2) The out-of-distribution (OOD) generalizability;and 3) The defensive ability against adversarial operations. The latter two definitions can be further specified into two different perspectives respectively, leading to 5 robustness tasks in total. Based on this taxonomy, we build corresponding benchmark datasets, design empirical experiments, and systematically analyze the robustness of several representative neural ranking models against traditional probabilistic ranking models and learning-to-rank (LTR) models. The empirical results show that there is no simple answer to our question. While neural ranking models are less robust against other IR models in most cases, some of them can still win 2 out of 5 tasks. This is the first comprehensive study on the robustness of neural ranking models. We believe the way we study the robustness as well as our findings would be beneficial to the IR community. We will also

关键词： Machine learning

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PathAFL: Path-Coverage Assisted Fuzzing 20

PathAFL: Path-Coverage Assisted Fuzzing

引用

15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020

作者： Yan, Shengbo Wu, Chenlu Li, Hang Shao, Wei Jia, Chunfu Nankai University College of Cyber Science TianJin China Nankai University College of Artificial Intelligence TianJin China Nankai University College of Cyber Science TianJin Key Laboratory of Network and Data Security Technology TianJin300350 China

ISBN: (纸本)9781450367509

Fuzzing is an effective method to find software bugs and vulnerabilities. One of the most useful techniques is the coverage-guided fuzzing, whose key element is the tracing code coverage information. Existing coverage-guided fuzzers generally use the the number of basic blocks or edges explored to measure code coverage. Path-coverage can provide more accurate coverage information than basic block and edge coverage. However, the number of paths grows exponentially as the size of a program increases. It is almost impossible to trace all the paths of a real-world application. In this paper, we propose a fuzzing solution named PathAFL, which assists a fuzzer by path identification. It can effectively identify and utilize the important h-path, which is a new path but whose edges have all been touched previously. First, PathAFL only inserts one assembly instruction to AFL's original code to calculate the path hash, and uses a selective instrumentation strategy to reduce the tracing granularity of an execution path. Second, we design a fast filtering algorithm to choose higher weight paths from a large number of h-paths and add them to the seed queue. Third, both the seed selection algorithm and the power schedule are implemented based on the path weight. Finally we implemented PathAFL based on the popular fuzzer AFL and evaluated it on 10 well-fuzzed benchmark programs. In 24 hours, PathAFL explored 38% more paths and 9.3% more edges than AFL. Compared with CollAFL-x, the number is 25% and 5.9% correspondingly. Moreover, PathAFL found the more bugs on the LAVA-M dataset, even four unlisted bugs. The results show that PathAFL outperforms the previous fuzzers in terms of both code coverage and bug discovery. In well-tested programs, PathAFL found 8 new security bugs with 6 CVEs assigned. © 2020 ACM.

关键词： Program debugging

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Delving into the Continuous Domain Adaptation

arXiv

引用

arXiv 2022年

作者： Xu, Yinsong Jiang, Zhuqing Men, Aidong Liu, Yang Chen, Qingchao School of Artificial Intelligence Beijing University of Posts and Telecommunications National Institute of Health Data Science Peking University Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System and Network Culture Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Wangxuan Institute of Computer Technology Peking University Beijing China National Institute of Health Data Science Peking University Beijing China

Existing domain adaptation methods assume that domain discrepancies are caused by a few discrete attributes and variations, e.g., art, real, painting, quickdraw, etc. We argue that this is not realistic as it is implausible to define the real-world datasets using a few discrete attributes. Therefore, we propose to investigate a new problem namely the Continuous Domain Adaptation (CDA) through the lens where infinite domains are formed by continuously varying attributes. Leveraging knowledge of two labeled source domains and several observed unlabeled target domains data, the objective of CDA is to learn a generalized model for whole data distribution with the continuous attribute. Besides the contributions of formulating a new problem, we also propose a novel approach as a strong CDA baseline. To be specific, firstly we propose a novel alternating training strategy to reduce discrepancies among multiple domains meanwhile generalize to unseen target domains. Secondly, we propose a continuity constraint when estimating the cross-domain divergence measurement. Finally, to decouple the discrepancy from the mini-batch size, we design a domain-specific queue to maintain the global view of the source domain that further boosts the adaptation performances. Our method is proven to achieve the state-of-the-art in CDA problem using extensive experiments. The code is available at https://***/SPIresearch/CDA. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge management

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