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Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow

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Chinese Physics C 2024年第4期48卷 240-251页

作者：孙天阳熊春雨金上捷王钰鑫张敬飞张鑫 Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave(GW)signals,thereby exerting a notable impact on the processing of GW *** inference of GW parameters,crucial for GW astronomy research,is particularly susceptible to such *** this study,we pioneer the utilization of a temporal and time-spectral fusion normalizing flow for likelihood-free inference of GW parameters,seamlessly integrating the high temporal resolution of the time domain with the frequency separation characteristics of both time and frequency ***,our findings indicate that the accuracy of this inference method is comparable to that of traditional non-glitch sampling ***,our approach exhibits a greater efficiency,boasting processing times on the order of *** conclusion,the application of a normalizing flow emerges as pivotal in handling GW signals affected by transient noises,offering a promising avenue for enhancing the field of GW astronomy research.

关键词： gravitational wave glitch non-Gaussian and transient noise normalizing flow machine learning likelihood-free parameter inference

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A normalizing flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

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Computers, Materials & Continua 2024年第2期78卷 1631-1648页

作者： Lanyao Zhang Shichao Kan Yigang Cen Xiaoling Chen Linna Zhang Yansen Huang School of Mechanical Engineering Guizhou UniversityGuiyang550025China School of Computer Science and Engineering Central South UniversityChangsha410083China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing100044China College of Civil Engineering Guizhou UniversityGuiyang550025China Guizhou Lianjian Civil Engineering Quality Inspection Monitoring Center Co. Ltd.Guiyang550025China

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be ***,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside ***,this paper proposes a normalizing flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature ***,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly *** two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection *** experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior *** the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 ***,it achieves 100%optimal detection performance in five *** the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

关键词： Anomaly detection normalizing flow source domain feature space target domain feature space bidirectional mapping residual network

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ReChoreoNet: Repertoire-based Dance Re-choreography with Music-conditioned Temporal and Style Clues

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Machine Intelligence Research 2024年第4期21卷 771-781页

作者： Ho Yin Au Jie Chen Junkun Jiang Yike Guo Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Department of Computer Science and Engineering The Hong Kong University of Science and EngineeringHong Kong 999077China

To generate dance that temporally and aesthetically matches the music is a challenging problem in three ***,the generated motion should be beats-aligned to the local musical ***,the global aesthetic style should be matched between motion and *** third,the generated motion should be diverse and *** address these challenges,we propose ReChoreoNet,which re-choreographs high-quality dance motion for a given piece of music.A data-driven learning strategy is proposed to efficiently correlate the temporal connections between music and motion in a progressively learned cross-modality embedding *** beats-aligned content motion will be subsequently used as autoregressive context and control signal to control a normalizing-flow model,which transfers the style of a prototype motion to the final generated *** addition,we present an aesthetically labelled music-dance repertoire(MDR)for both efficient learning of the cross-modality embedding,and understanding of the aesthetic connections between music and *** demonstrate that our repertoire-based framework is robustly extensible in both content and *** quantitative and qualitative experiments have been carried out to validate the efficiency of our proposed model.

关键词： Generative model cross-modality learning normalizing flow tempo synchronization style transfer.

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Sampling with Prior Knowledge for High-dimensional Gravitational Wave Data Analysis

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Big Data Mining and Analytics 2022年第1期5卷 53-63页

作者： He Wang Zhoujian Cao Yue Zhou Zong-Kuan Guo Zhixiang Ren CAS Key Laboratory of Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesP.O.Box 2735Beijing 100190China Department of Astronomy Beijing Normal UniversityBeijing 100875China the Department of Networked Intelligence Peng Cheng LaboratoryShenzhen 518055China

Extracting knowledge from high-dimensional data has been notoriously difficult,primarily due to the so-called"curse of dimensionality"and the complex joint distributions of these *** is a particularly profound issue for high-dimensional gravitational wave data analysis where one requires to conduct Bayesian inference and estimate joint posterior *** this study,we incorporate prior physical knowledge by sampling from desired interim distributions to develop the training ***,the more relevant regions of the high-dimensional feature space are covered by additional data points,such that the model can learn the subtle but important *** adapt the normalizing flow method to be more expressive and trainable,such that the information can be effectively extracted and represented by the transformation between the prior and target *** trained,our model only takes approximately 1 s on one V100 GPU to generate thousands of samples for probabilistic inference *** evaluation of our approach confirms the efficacy and efficiency of gravitational wave data inferences and points to a promising direction for similar *** source code,specifications,and detailed procedures are publicly accessible on GitHub.

关键词： high-dimensional data prior sampling normalizing flow gravitational wave

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