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Dynamic MCMC sampling

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arXiv 2019年

作者： Feng, Weiming He, Kun Sun, Xiaoming Yin, Yitong State Key Laboratory for Novel Software Technology Nanjing University CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from Gibbs distributions arising by various graphical models, e.g. Markov random fields (MRF). Traditional MCMC sampling algorithms are focused on a classic static setting, where the input is fixed. In this paper we study the problem of sampling from a MRF when the graphical model itself is changing dynamically with time. The problem is well motivated by the growing volume and velocity of data in today's applications of the MCMC methods. For the two major MCMC approaches, respectively for the approximate and perfect sampling, namely, the Gibbs sampling and the coupling from the past (CFTP), we give dynamic versions for the respective MCMC sampling algorithms. On MRF with n variables and bounded maximum degrees, these dynamic sampling algorithms can maintain approximate samples within 1/Poly(n) total variation errors, or perfect samples, while the MRF is dynamically changing. Furthermore, the dynamic sampling algorithms are efficient with O(n) space cost, and O(log2 n) incremental time cost upon each local update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains. These decay conditions were well known in the literature of couplings for rapid mixing of Markov chains, and now for the first time, are used to imply efficient dynamic sampling algorithms. Consequently, we have efficient dynamic (approximate or perfect) sampling algorithms with O(n) space cost and O (log2 n) incremental time cost, for the following models when the maximum degree is bounded: generalMRF satisfying the Dobrushin-Shlosman condition (for approximate sampling);Ising model with temperature β where e-2|β| > 1 - 2 β+1 (for both approximate and perfect samplings);hardcore model with fugacity λ 2λ (for approximate sampling);or q > 2Δ2 + 3Δ (for perfect sampling). These results show that the coupling of single-site Markov chains that

关键词： Learning algorithms

Author Correction: Identification of recurrent USP48 and BRAF mutations in Cushing's disease (vol 9, 3171, 2018)

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NATURE COMMUNICATIONS 2023年第1期14卷 1页

作者： Chen, Jianhua Jian, Xuemin Deng, Siyu Ma, Zengyi Shou, Xuefei Shen, Yue Zhang, Qilin Song, Zhijian Li, Zhiqiang Peng, Hong Peng, Cheng Chen, Min Luo, Cheng Zhao, Dan Ye, Zhao Shen, Ming Zhang, Yichao Zhou, Juan Fahira, Aamir Wang, Yongfei Li, Shiqi Zhang, Zhaoyun Ye, Hongying Li, Yiming Shen, Jiawei Chen, Hong Tang, Feng Yao, Zhenwei Shi, Zhifeng Chen, Chunjui Xie, Lu Wang, Ye Fu, Chaowei Mao, Ying Zhou, Liangfu Gao, Daming Yan, Hai Zhao, Yao Huang, Chuanxin Shi, Yongyong Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) and the Collaborative Innovation Center for Brain Science Shanghai Jiao Tong University Shanghai 200030 China Department of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai 200233 China Institute of Neuropsychiatric Science and Systems Biological Medicine Shanghai Jiao Tong University Shanghai 200030 China Department of Psychiatry First Teaching Hospital of Xinjiang Medical University Urumqi Xinjiang 830054 China The Affiliated Hospital of Qingdao University & The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes) Qingdao University Qingdao Shandong 266003 China Shanghai Institute of Immunology Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai 200025 China Department of Neurosurgery Huashan Hospital Shanghai Medical College Fudan University Shanghai 200040 China Shanghai Pituitary Tumor Center Shanghai 200040 China Institute of Neurosurgery Fudan University Shanghai 200040 China State Key Laboratory of Medical Neurobiology Institute of Neurosurgery Shanghai Medical College Fudan University 200040 Shanghai China Department of Endocrinology Huashan Hospital Shanghai Medical College Fudan University Shanghai 200040 China Department of Pathology Huashan Hospital Shanghai Medical College Fudan University Shanghai 200040 China Department of Radiology Huashan Hospital Shanghai Medical College Fudan University Shanghai 200040 China CAS Key Laboratory of Systems Biology CAS Center for Excellence in Molecular Cell Science Innovation Center for Cell Signaling Network Shanghai Institute of B

Peripheral-free device pairing by randomly switching power

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arXiv 2020年

作者： Shao, Zhijian Weng, Jian Zhang, Yue Wu, Yongdong Li, Ming Weng, Jiasi Luo, Weiqi Yu, Shui College of Informatin Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China University of Technology Sydney UltimoNSW Australia

The popularity of Internet-of-Things (IoT) comes with security concerns. Attacks against wireless communication venues of IoT (e.g., Man-in-the-Middle attacks) have grown at an alarming rate over the past decade. Pairing, which allows the establishment of the secure communicating channels for IoT devices without a prior relationship, is thus a paramount capability. Existing secure pairing protocols require auxiliary equipment/peripheral (e.g., displays, speakers and sensors) to achieve authentication, which is unacceptable for low-priced devices such as smart lamps. This paper studies how to design a peripheral-free secure pairing protocol. Concretely, we design the protocol, termed SwitchPairing, via out-of-box power supplying chargers and on-board clocks, achieving security and economics at the same time. When a user wants to pair two or more devices, he/she connects the pairing devices to the same power source, and presses/releases the switch on/off button several times. Then, the press and release timing can be used to derive symmetric keys. We implement a prototype via two CC2640R2F development boards from Texas Instruments (TI) due to its prevalence. Extensive experiments and user studies are also conducted to benchmark our protocol in terms of efficiency and security. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

Analysis of COVID-19 Guideline Quality and Change of Recommendations:A Systematic Review

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Health data science 2021年第1期2021卷 88-109页

作者： Siya Zhao Shuya Lu Shouyuan Wu Zijun Wang Qiangqiang Guo Qianling Shi Hairong Zhang Juanjuan Zhang Hui Liu Yunlan Liu Xianzhuo Zhang Ling Wang Mengjuan Ren Ping Wang Hui Lan Qi Zhou Yajia Sun Jin Cao Qinyuan Li Janne Estill Joseph LMathew Hyeong Sik Ahn Myeong Soo Lee Xiaohui Wang Chenyan Zhou Yaolong Chen School of Public Health Lanzhou UniversityLanzhouChina Institute of Health Data Science Lanzhou UniversityLanzhouChina Department of Pediatric Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduChina Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital ChengduChina Evidence-Based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouChina The First School of Clinical Medicine Lanzhou UniversityLanzhouChina National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and DisordersChina International Science and Technology Cooperation Base of Child Development and Critical DisordersChildren’s Hospital of Chongqing Medical UniversityChongqingChina Institute of Global Health University of GenevaGenevaSwitzerland Institute of Mathematical Statistics and Actuarial Science University of BernBernSwitzerland Advanced Pediatrics Centre PGIMER ChandigarhChandigarhIndia Department of Preventive Medicine Korea UniversitySeoulRepublic of Korea Korea Cochrane Centre SeoulRepublic of Korea Evidence Based Medicine SeoulRepublic of Korea Korea University School of Medicine SeoulRepublic of Korea Korea Institute of Oriental Medicine DaejeonRepublic of Korea University of Science and Technology DaejeonRepublic of Korea London Southbank University LondonUK Tianjin University of Traditional Chinese Medicine TianjinChina WHO Collaborating Centre for Guideline Implementation and Knowledge Translation LanzhouChina Guideline International Network Asia China Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province Lanzhou UniversityLanzhouChina Lanzhou University GRADE Center China

*** of coronavirus disease 2019(COVID-19)clinical practice guidelines(CPGs)and expert consensus statements have been developed and published since the outbreak of the ***,these CPGs are of widely variable ***,this review is aimed at systematically evaluating the methodological and reporting qualities of COVID-19 CPGs,exploring factors that may influence their quality,and analyzing the change of recommendations in CPGs with evidence *** searched five electronic databases and five websites from 1 January to 31 December 2020 to retrieve all COVID-19 *** assessment of the methodological and reporting qualities of CPGs was performed using the AGREE II instrument and RIGHT *** and evidence used to make recommendations in the CPGs regarding some treatments for COVID-19(remdesivir,glucocorticoids,hydroxychloroquine/chloroquine,interferon,and lopinavir-ritonavir)were also systematically *** the statistical inference was performed to identify factors associated with the quality of *** included a total of 92 COVID-19 CPGs developed by 19 ***,the RIGHT checklist reporting rate of COVID-19 CPGs was 33.0%,and the AGREE II domain score was 30.4%.The overall methodological and reporting qualities of COVID-19 CPGs gradually improved during the year *** associated with high methodological and reporting qualities included the evidence-based development process,management of conflicts of interest,and use of established rating systems to assess the quality of evidence and strength of *** recommendations of only seven(7.6%)CPGs were informed by a systematic review of evidence,and these seven CPGs have relatively high methodological and reporting qualities,in which six of them fully meet the Institute of Medicine(IOM)criteria of ***,a rapid advice CPG developed by the World Health Organization(WHO)of the seven CPGs got the highest overa

关键词： register formal transparency

Deep Underground laboratory Measurement of C13(α,n)O16 in the Gamow Windows of the s and i Processes

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Physical Review Letters 2022年第13期129卷 132701-132701页

作者： B. Gao T. Y. Jiao Y. T. Li H. Chen W. P. Lin Z. An L. H. Ru Z. C. Zhang X. D. Tang X. Y. Wang N. T. Zhang X. Fang D. H. Xie Y. H. Fan L. Ma X. Zhang F. Bai P. Wang Y. X. Fan G. Liu H. X. Huang Q. Wu Y. B. Zhu J. L. Chai J. Q. Li L. T. Sun S. Wang J. W. Cai Y. Z. Li J. Su H. Zhang Z. H. Li Y. J. Li E. T. Li C. Chen Y. P. Shen G. Lian B. Guo X. Y. Li L. Y. Zhang J. J. He Y. D. Sheng Y. J. Chen L. H. Wang L. Zhang F. Q. Cao W. Nan W. K. Nan G. X. Li N. Song B. Q. Cui L. H. Chen R. G. Ma S. Q. Yan J. H. Liao Y. B. Wang S. Zeng D. Nan Q. W. Fan N. C. Qi W. L. Sun X. Y. Guo P. Zhang Y. H. Chen Y. Zhou J. F. Zhou J. R. He C. S. Shang M. C. Li S. Kubono W. P. Liu R. J. deBoer M. Wiescher M. Pignatari Joint Department for Nuclear Physics Institute of Modern Physics Chinese Academy of Sciences and Lanzhou University Lanzhou 730000 China CAS Key Laboratory of High Precision Nuclear Spectroscopy Institute of Modern Physics Chinese Academy of Sciences Lanzhou 73000 People’s Republic of China School of Nuclear Science and Technology University of Chinese Academy of Sciences Beijing 100049 People’s Republic of China Key Laboratory of Radiation Physics and Technology of the Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu 610064 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai Guangdong 519082 People’s Republic of China School of Nuclear Science and Technology Lanzhou University Lanzhou 730000 China China Institute of Atomic Energy Beijing 102413 People’s Republic of China Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment Institute of Space Sciences Shandong University Weihai 264209 China Key Laboratory of Beam Technology and Material Modification of Ministry of Education College of Nuclear Science and Technology Beijing Normal University Beijing 100875 China Yalong River Hydropower Development Company Chengdu 610051 China RIKEN Nishina Center Wako Saitama 351-0198 Japan Department of Physics and the Joint Institute for Nuclear Astrophysics University of Notre Dame Notre Dame Indiana 46556 USA Center for Nuclear Study University of Tokyo Wako Saitama 351-0198 Japan Wolfson Fellow of Royal Society School of Physics and Astronomy University of Edinburgh King’s Buildings Edinburgh EH9 3FD United Kingdom Konkoly Observatory Research Centre for Astronomy and Earth Sciences (CSFK) Eötvös Loránd Research Network (ELKH) Konkoly Thege Miklós út 15-17 H-1121 Budapest Hunga

The C13(α,n)O16 reaction is the main neutron source for the slow-neutron-capture process in asymptotic giant branch stars and for the intermediate process. Direct measurements at astrophysical energies in above-ground laboratories are hindered by the extremely small cross sections and vast cosmic-ray-induced background. We performed the first consistent direct measurement in the range of Ec.m.=0.24 to 1.9 MeV using the accelerators at the China Jinping Underground laboratory and Sichuan University. Our measurement covers almost the entire intermediate process Gamow window in which the large uncertainty of the previous experiments has been reduced from 60% down to 15%, eliminates the large systematic uncertainty in the extrapolation arising from the inconsistency of existing datasets, and provides a more reliable reaction rate for the studies of the slow-neutron-capture and intermediate processes along with the first direct determination of the alpha strength for the near-threshold state.

关键词： H & He burning Hydrostatic stellar nucleosynthesis Nuclear astrophysics s process

MMCGAN: Generative Adversarial network with explicit manifold prior

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arXiv 2020年

作者： Zheng, Guanhua Sang, Jitao Xu, Changsheng University of Science and Technology of China School of Computer and Information Technology Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China National Lab of Pattern Recognition Institute of Automation CAS University of Chinese Academy of Sciences Beijing100190 China

Generative Adversarial network(GAN) provides a good generative framework to produce realistic samples, but suffers from two recognized issues as mode collapse and unstable training. In this work, we propose to employ explicit manifold learning as prior to alleviate mode collapse and stabilize training of GAN. Since the basic assumption of conventional manifold learning fails in case of sparse and uneven data distribution, we introduce a new target, Minimum Manifold Coding (MMC), for manifold learning to encourage simple and unfolded manifold. In essence, MMC is the general case of the shortest Hamiltonian Path problem and pursues manifold with minimum Riemann volume. Using the standardized code from MMC as prior, GAN is guaranteed to recover a simple and unfolded manifold covering all the training data. Our experiments on both the toy data and real datasets show the effectiveness of MMCGAN in alleviating mode collapse, stabilizing training, and improving the quality of generated samples. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

ReCoSa: Detecting the relevant contexts with self-attention for multi-turn dialogue generation

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arXiv 2019年

作者： Zhang, Hainan Y., Lan L., Pang J., Guo X., Cheng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

In multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a suitable response accordingly. However, the widely used hierarchical recurrent encoder-decoder models just treat all the contexts indiscriminately, which may hurt the following response generation process. Some researchers try to use the cosine similarity or the traditional attention mechanism to find the relevant contexts, but they suffer from either insufficient relevance assumption or position bias problem. In this paper, we propose a new model, named ReCoSa, to tackle this problem. Firstly, a word level LSTM encoder is conducted to obtain the initial representation of each context. Then, the self-attention mechanism is utilized to update both the context and masked response representation. Finally, the attention weights between each context and response representations are computed and used in the further decoding process. Experimental results on both Chinese customer services dataset and English Ubuntu dialogue dataset show that ReCoSa significantly outperforms baseline models, in terms of both metric-based and human evaluations. Further analysis on attention shows that the detected relevant contexts by ReCoSa are highly coherent with human's understanding, validating the correctness and interpretability of ReCoSa. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal encoding

Coupled graph neural networks for predicting the popularity of online content

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arXiv 2019年

作者： Cao, Qi Shen, Huawei Gao, Jinhua Wei, Bingzheng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Inc

Predicting the popularity of online content in social network is an important problem for the practice of information dissemination, advertising, and recommendation. Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction. These methods ignore the interaction between early adopters and potential adopters or the interactions among potential adopters over social networks. Consequently, they fail to capture the cascading effect triggered by early adopters in social networks, and thus have limited predictive power. In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks among users for popularity *** propose a novel method, namely Coupled-GNNs, which use two coupled graph neural networks to capture the cascading effect in information diffusion. One graph neural network models the interpersonal influence, gated by the adoption state of users. The other graph neural network models the adoption state of users via interpersonal influence from their neighbors. Through such an iterative aggregation of the neighborhood, the proposed method naturally captures the cascading effect of information diffusion in social networks. Experiments conducted on both synthetic data and real-world SinaWeibo data demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

Concurrently extrapolating and interpolating networks for continuous model generation

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arXiv 2020年

作者： Zhao, Lijun Zhang, Jinjing Zhang, Fan Wang, Anhong Bai, Huihui Zhao, Yao Institute of Digital Media and Communication Taiyuan University of Science and Technology Taiyuan030024 China Data Science and Technology North University of China Taiyuan030024 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing Jiaotong University Beijing100044 China

Most deep image smoothing operators are always trained repetitively when different explicit structure-texture pairs are employed as label images for each algorithm configured with different parameters. This kind of training strategy often takes a long time and spends equipment resources in a costly manner. To address this challenging issue, we generalize continuous network interpolation as a more powerful model generation tool, and then propose a simple yet effective model generation strategy to form a sequence of models that only requires a set of specific-effect label images. To precisely learn image smoothing operators, we present a double-state aggregation (DSA) module, which can be easily inserted into most of current network architecture. Based on this module, we design a double-state aggregation neural network structure with a local feature aggregation block and a nonlocal feature aggregation block to obtain operators with large expression capacity. Through the evaluation of many objective and visual experimental results, we show that the proposed method is capable of producing a series of continuous models and achieves better performance than that of several state-of-the-art methods for image smoothing. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrapolation