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Particle gibbs with ancestor sampling

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2014年第1期15卷

作者： Kevin Murphy Bernhard Schölkopf Fredrik Lindsten Michael I. Jordan Thomas B. Schön Google Department of Engineering University of Cambridge Cambridge UK and Division of Automatic Control Linköping University Linköping Sweden Computer Science Division and Department of Statistics University of California Berkeley CA Department of Information Technology Uppsala University Uppsala Sweden

Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). We present a new PMCMC algorithm that we refer to as particle Gibbs with ancestor sampling (PGAS). PGAS provides the data analyst with an off-the-shelf class of Markov kernels that can be used to simulate, for instance, the typically high-dimensional and highly autocorrelated state trajectory in a state-space model. The ancestor sampling procedure enables fast mixing of the PGAS kernel even when using seemingly few particles in the underlying SMC sampler. This is important as it can significantly reduce the computational burden that is typically associated with using SMC. PGAS is conceptually similar to the existing PG with backward simulation (PGBS) procedure. Instead of using separate forward and backward sweeps as in PGBS, however, we achieve the same effect in a single forward sweep. This makes PGAS well suited for addressing inference problems not only in state-space models, but also in models with more complex dependencies, such as non-Markovian, Bayesian nonparametric, and general probabilistic graphical models.

关键词： Bayesian inference non-Markovian models particle Markov chain Monte Carlo sequential Monte Carlo state-space models

HRTS: Hierarchical Rauch-Tung-Striebel Smoother With Online Learning Priors for EEG Denoising

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Journal of Network Intelligence

Journal of Network Intelligence 2024年第2期9卷 673-688页

作者： Wang, Chuan-Sheng Zhang, Ling Fu, Tian-Lin Chen, Zhao-Qi Zhang, Fu-Quan Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fujian Province Fuzhou City China College of Computer and Big Data Fuzhou University No.2 Wulongjiang North Road Fuzhou University Town Fujian Province Fuzhou City China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fujian Province Fuzhou City China Fujian Province University No.1 Campus New Village Longjiang Street Fujian Province Fuqing City China

Electroencephalogram (EEG) is a nonlinear signal that reflects the physio-logical state of the brain at different times, containing rich information. However, the possible interference during the collection and transmission process often leads to a large amount of unnecessary noise in the EEG signal. Traditional denoising methods may face difficulties in high-dimensional data processing and the inability to effectively handle different types of Gaussian white noise. To better eliminate the interference of Gaussian white noise, this article specifically adopts the Hierarchical Rauch-Tung-Striebel Smoother (HRTS) method. This method can effectively integrate the learned structural prior EEG signals into the state space model, thereby describing the relationship between EEG signals and noise. Capture the spatiotemporal characteristics of EEG signals through hierarchical modeling, and optimize the components of EEG signals through estimation and prediction of hidden variables. Finally, the mean square error (MSE) is used as an evaluation indicator to compare and evaluate the denoising results. Empirical research has shown that the HRTS algorithm can not only effectively reduce runtime and better process high-dimensional data, but also significantly improve the quality of EEG signals, effectively suppress noise interference, and more accurately reflect the characteristics of brain activity. Compared to denoising algorithms such as Kalman filtering and Kalman wavelet filtering, the HRTS algorithm has more advantages in denoising EEG signals. © 2024, J. Network Intell. All rigjts reserved.

关键词： Electroencephalography

Using the Model of a Functional Rationalizer of Consumer Behavior in Recommendation Systems for Managing the Transport Activity of the Urban Population

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Using the Model of a Functional Rationalizer of Consumer Beh...

International Conference on Soft Computing and Measurements (SCM)

作者： Yaroslav A. Seliverstov Svytoslav A. Seliverstov Nikolay V. Podoprigora Alexey L. Starichenkov Roman S. Naryshkin Laboratory of Intelligent Transport Systems Solomenko Institute of Transport Problems of the Russian Academy of Sciences Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University St. Petersburg Russian Federation Department of Ground Transportation Technological Machines Saint Petersburg State University of Architecture and Civil Engineering St. Petersburg Russian Federation Department of Ship Control Systems Saint Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation Department of Programming Technology Saint Petersburg State University St. Petersburg Russian Federation

ISBN: (数字)9781728196923

ISBN: (纸本)9781728196930

An overview of existing urban software mobile applications of the transport and economic direction is given. A model of a functional rationalizer of consumer behavior is being built. The software model of the functional rationalizer of consumer behavior (SM FRCB) was developed for the first time. PMFRCB allows the user to expand the number of his preferred needs (occupations, activities) during the daily cycle through intelligent recommendations. Also, SM FRCB allows you to optimize needs (activities, activities) at the cost and rate of their use. The SM FRCB is made in Python_3 using the libraries matplotlib, collections, numpy, pyplot, mplot3d. The analysis of consumer behavior before and after using the functional rationalizer model is carried out. The graphical results of a structural analysis of the costs of old and new consumer behavior, analysis of consumer behavior within the boundaries of the theory of possibilities and the model of transport behavior of a resident before and after applying the functional rationalizer model are presented. The prospects of further improving the SM FRCB are considered.

关键词： Consumer behavior Mobile applications Sociology Statistics Logistics Analytical models Software

Constructing Epidermal Rete Ridges Using Composite Hydrogel to Enhance Multiple Signaling Pathways for the Maintenance of Epidermal Stem Cell Niche

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SSRN

SSRN 2023年

作者： Shen, Zhizhong Liu, Zixian Sun, Lei Li, Meng Han, Lu Wang, Jianming Wu, Xunwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030031 China General Hospital of TISCO North Street Xinghualing District Taiyuan030809 China Engineering Laboratory for Biomaterials and Tissue Regeneration Ningbo Stomatology Hospital Savaid Stomatology School Hangzhou Medical College Ningbo China Department of Tissue Engineering and Regeneration School and Hospital of Stomatology Cheeloo College of Medicine Shandong University Shandong Key Laboratory of Oral Tissue Regeneration Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration Shandong Jinan China Cutaneous Biology Research Center Massachusetts General Hospital Harvard Medical School BostonMA United States

The undulating microstructure rete ridge (RR) located at the junction between the dermis and epidermis plays a crucial role in improving skin mechanical properties and maintaining skin homeostasis. However, the investigation of RR microstructures are usually neglected in current tissue engineering for skin regeneration. Here, to create an epidermal model with RR microstructures, keratinocytes were inoculated onto a patterned GelMA-PEGDA hydrogel constructed using molding technology. Furthermore, grafting acryloylated Arg-Gly-Asp (RGD) peptides on the hydrogel surface significantly improved cell adhesion, fusion, and development. RT-PCR, Western blot, and immunofluorescence staining confirmed that cells on RR microstructures exhibited higher gene and protein expression associated with epidermal stem cells. RNA sequencing analysis of cells on RR microstructure showed higher gene expression profiles related to stem cell maintenance, basement membrane formation, and epidermal development. Furthermore, RT-PCR analysis of epidermal models of various dimensions demonstrated that smaller microstructures were more conducive to epidermal stem cell marker gene expression, which is analogous to human skin. Overall, we have successfully developed a method for integrating RR microstructures into an epidermal model that mimics natural skin to maintain epidermal stem cell niche, providing a valuable reference for researching skin regeneration within the fields of tissue engineering and regenerative medicine. © 2023, The Authors. All rights reserved.

关键词： Microstructure

Enhanced phase sensitivity in a Mach-Zehnder interferometer via photon recycling

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arXiv 2023年

作者： Li, Dong Yuan, Chun-Hua Ma, Xiaoping Wang, Qingle Lee, Hwang Yao, Yao Zhang, Weiping Microsystem and Terahertz Research Center China Academy of Engineering Physics Sichuan Chengdu610200 China Institute of Electronic Engineering China Academy of Engineering Physics Sichuan Mianyang621999 China State Key Laboratory of Precision Spectroscopy Quantum Institute of Atom and Light Department of Physics East China Normal University Shanghai200062 China College of Mathematical and Physical Sciences Qingdao University of Science and Technology Qingdao China School of Control and Computer Engineering North China Electric Power University Beijing102206 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Hearne Institute for Theoretical Physics Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Department of Physics Shanghai Jiao Tong University Tsung-Dao Lee Institute Shanghai200240 China Collaborative Innovation Center of Extreme Optics Shanxi University Shanxi Taiyuan030006 China

We propose an alternative scheme for phase estimation in a Mach-Zehnder interferometer (MZI) with photon recycling. It is demonstrated that with the same coherent-state input and homodyne detection, our proposal possesses a phase sensitivity beyond the traditional MZI. For instance, it can achieve an enhancement factor of ∼9.32 in the phase sensitivity compared with the conventional scheme even with a photon loss of 10% on the photon-recycled arm. From another point of view, the quantum Cramér-Rao bound (QCRB) is also investigated. It is found that our scheme is able to achieve a lower QCRB than the traditional one. Intriguingly, the QCRB of our scheme is dependent of the phase shift φ while the traditional scheme has a constant QCRB regardless of the phase shift. Finally, we present the underlying mechanisms behind the enhanced phase sensitivity (due to the improved number of photons inside the interferometer). We believe that our results may pave the way to enhance the phase sensitivity via photon recycling. © 2023, CC BY.

关键词： Photons

Self adaptation of chimera states

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arXiv 2018年

作者： Yao, Nan Huang, Zi-Gang Ren, Hai-Peng Grebogi, Celso Lai, Ying-Cheng Department of Applied Physics Xi’an University of Technology Xi’an710048 China School of Life Science and Technology Xi’an Jiao Tong University Xi’an710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Institute for Complex Systems and Mathematical Biology King’s College University of Aberdeen AberdeenAB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of "intelligence" in achieving robustness through self adaptation. The behavior can be exploited for controlled generation of chimera states with their coherent component placed in any desired spatial region of the system. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

Set-up of active magnetic bearings for control of flexible shaft

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AIP Conference Proceedings 2018年第1期2029卷

作者： Adam Piłat Bartosz Sikora Jan Klocek Jan Cieślik 1AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics Mickiewicza 30 30-059 Krakow Poland 2Optister Jalowcowa 50 31-329 Krakow Poland 3AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Department of Manufacturing Systems Mickiewicza 30 30-059 Krakow Poland

This paper presents the new laboratory test rig of the vertical axis flexible shaft supported by two active magnetic bearings. Behind the motivation of establishing this setup is the control of vibration modes of the flexible rotor - shaft assembly, while the rotational speed varies and the unbalanced mass on the whirling disc is introduced to the system. The proposed model is aimed to simulate real operation conditions of the rotating machinery with an applied external load, with a variable structure caused by a change of material properties of the rotor - shaft assembly, or due to a fault of some elements coupled with the rotor.

关键词：

Artificial intelligence enabled radio propagation for communications - Part I: Channel characterization and antenna-channel optimization

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Purple Mountain Laboratories Nanjing211111 China The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper. © 2021, CC BY-NC-ND.

关键词： Machine learning

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

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arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation