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Astronomical Image Denoising by Self-Supervised Deep Learning and Restoration Processes

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arXiv 2025年

作者： Liu, Tie Quan, Yuhui Su, Yingna Guo, Yang Liu, Shu Ji, Haisheng Hao, Qi Gao, Yulong Liu, Yuxia Wang, Yikang Sun, Wenqing Ding, Mingde School of Astronomy and Space Science Nanjing University 163 Xianlin Avenue Jiangsu Nanjing210023 China Ministry of Education 163 Xianlin Avenue Jiangsu Nanjing210023 China School of Computer Science and Engineering South China University of Technology 382 Waihuan East Road Guangdong Guangzhou510006 China Pazhou Lab 248 Qiaotou Street Guangdong Guangzhou510335 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 10 Yuanhua Road Jiangsu Nanjing210034 China School of Astronomy and Space Science University of Science and Technology of China 96 Jinsai Road Anhui Hefei230026 China Department of Rheumatology Nanjing Drum Tower Hospital The Affiliated Hospital Of Nanjing University 321 Zhongshan Road Jiangsu Nanjing210008 China College of Engineering Peking University 5 Yiheyuan Road Beijing100871 China State Key Laboratory of Low-carbon Smart Coal-fired Power Generation and Ultra-clean Emission China Energy Science and Technology Research Institute Co. Ltd 10 Xianjing Road Nanjing210034 China Faculty of Electrical Engineering and Computer Science Ningbo University No. 818 Fenghua Road Zhejiang Ningbo315211 China

Image denoising based on deep learning has witnessed significant advancements in recent years. However, existing deep learning methods lack quantitative control of the deviation or error on denoised images. The neural networks Self2Self is designed for denoising single-image, training on it and denoising itself, during which training is costly. In this work we explore training Self2Self on an astronomical image and denoising other images of the same kind, which is suitable for quickly denoising massive images in astronomy. To address the deviation issue, the abnormal pixels whose deviation exceeds a predefined threshold are restored to their initial values. The noise reduction includes training, denoising, restoring and named TDR-method, by which the noise level of the solar magnetograms is improved from about 8 G to 2 G. Furthermore, the TDR-method is applied to galaxy images from the Hubble Space Telescope and makes weak galaxy structures become much clearer. This capability of enhancing weak signals makes the TDR-method applicable in various disciplines. © 2025, CC BY-NC-SA.

关键词： Image denoising

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

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arXiv 2022年

作者： Huang, He-Liang Xu, Xiao-Yue Guo, Chu Tian, Guojing Wei, Shi-Jie Sun, Xiaoming Bao, Wan-Su Long, Gui-Lu Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Beijing Academy of Quantum Information Sciences Beijing100193 China Department of Physics Tsinghua University Beijing100084 China

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field. © 2022, CC BY.

关键词： Benchmarking

Attention-based multi-level feature fusion for named entity recognition 29

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Attention-based multi-level feature fusion for named entity ...

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Yang, Zhiwei Chen, Hechang Zhang, Jiawei Ma, Jing Chang, Yi College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China IFM Lab Department of Computer Science Florida State University TallahasseeFL United States Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China International Center of Future Science Jilin University Changchun China

ISBN: (纸本)9780999241165

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Embeddings

Hyperbolic variational graph neural network for modeling dynamic graphs

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arXiv 2021年

作者： Sun, Li Zhang, Zhongbao Zhang, Jiawei Wang, Feiyang Peng, Hao Su, Sen Yu, Philip S. State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China IFM Lab Department of Computer Science Florida State University FL United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Department of Computer Science University of Illinois ChicagoIL United States

Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representation in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach. To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions. Furthermore, we introduce a reparameterisable sampling algorithm for the hyperbolic normal distribution to enable the gradient-based learning of HVGNN. Extensive experiments show that HVGNN outperforms state-of-the-art baselines on real-world datasets. © 2021, CC BY-NC-SA.

关键词： Normal distribution

Top-k contrast order-preserving pattern mining

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arXiv 2023年

作者： Wu, Youxi Meng, Yufei Li, Yan Guo, Lei Zhu, Xingquan Fournier-Viger, Philippe Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Shijiazhuang University of Applied Technology Shijiazhuang050081 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Shenzhen University Shenzhen China Research Center for Knowledge Engineering The Zhejiang Lab Hangzhou311121 China

Recently, order-preserving pattern (OPP) mining, a new sequential pattern mining method, has been proposed to mine frequent relative orders in a time series. Although frequent relative orders can be used as features to classify a time series, the mined patterns do not reflect the differences between two classes of time series well. To effectively discover the differences between time series, this paper addresses the top-k contrast OPP (COPP) mining and proposes a COPP-Miner algorithm to discover the top-k contrast patterns as features for time series classification, avoiding the problem of improper parameter setting. COPP-Miner is composed of three parts: extreme point extraction to reduce the length of the original time series, forward mining, and reverse mining to discover COPPs. Forward mining contains three steps: group pattern fusion strategy to generate candidate patterns, the support rate calculation method to efficiently calculate the support of a pattern, and two pruning strategies to further prune candidate patterns. Reverse mining uses one pruning strategy to prune candidate patterns and consists of applying the same process as forward mining. Experimental results validate the efficiency of the proposed algorithm and show that top-k COPPs can be used as features to obtain a better classification performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

A STABLE, FAST, AND FULLY AUTOMATIC LEARNING ALGORITHM FOR PREDICTIVE CODING NETWORKS

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arXiv 2022年

作者： Salvatori, Tommaso Song, Yuhang Yordanov, Yordan Millidge, Beren Emde, Cornelius Xu, Zhenghua Sha, Lei Bogacz, Rafal Lukasiewicz, Thomas VERSES AI Research Lab Los AngelesCA90016 United States MRC Brain Network Dynamics Unit University of Oxford United Kingdom Department of Computer Science University of Oxford United Kingdom State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University China Institute of Logic and Computation TU Wien Austria

Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one, and has theoretical guarantees in terms of convergence. The proposed algorithm, that we call incremental predictive coding (iPC) is also more biologically plausible than the original one, as it it fully automatic. In an extensive set of experiments, we show that iPC constantly performs better than the original formulation on a large number of benchmarks for image classification, as well as for the training of both conditional and masked language models, in terms of test accuracy, efficiency, and convergence with respect to a large set of hyperparameters. Copyright © 2022, The Authors. All rights reserved.

关键词： Network coding

A practical framework of multi-person 3D human pose estimation with a single RGB camera

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A practical framework of multi-person 3D human pose estimati...

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Le Ma Sen Lian Shandong Wang Weiliang Meng Jun Xiao Xiaopeng Zhang School of Artificial Intelligence University of Chinese Academy of Sciences NLPR Institute of Automation Chinese Academy of Sciences Intel Labs China Zhejiang Lab The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences

We propose a practical framework named 'DN-2DPN-3DPN' for multi-person 3D pose estimation with a single RGB camera. Our framework performs three-stages tasks on the input video: our DetectNet(DN) firstly detects the people's bounding box individually for each frame of the video, while our 2DPoseNet(2DPN) estimates the 2D poses for each person in the second stage, and our 3DPoseNet(3DPN) is finally applied to obtain the 3D poses of the people. Experiments validate that our method can achieve state-of-the-art performance for multi-person 3D human pose estimation on the Human3.6M dataset.

关键词： Three-dimensional displays Conferences Pose estimation Virtual reality User interfaces Cameras Real-time systems

Co2-Induced Switching between Mof-Based Bio-Mimic Slow Anion Channel and Proton Pump for Medical Exhalation Detection

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SSRN

SSRN 2024年

作者： Chen, Honghao Yue, Xiaorui Fan, Yifei Zheng, Bin Lv, Sitao Wang, Fengnan Gao, Yixun Li, Hao Lee, Yi-Kuen French, Patrick J. Umar Siddiqui, Ahmad M. Wang, Yao Zhou, Guofu Guangdong Provincial Key Laboratory of Optical Information Materials and Technology Institute of Electronic Paper Displays South China Academy of Advanced Optoelectronics South China Normal University Guangzhou510006 China National Center for International Research on Green Optoelectronics South China Normal University Guangzhou510006 China Department of Thoracic Oncology State Key Laboratory of Respiratory Diseases The First Affiliated Hospital of Guangzhou Medical University Guangzhou510006 China Department of Mechanical & Aerospace Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong BE Lab Faculty EWI Delft University of Technology Delft2628CD Netherlands Najran University Najran11001 Saudi Arabia

Inspired by the activation mechanism of slow anion channels (SLAC1) in plants that proton pump reversibly induces plant stomata open for CO2 adsorption, a CO2-switching H+ conduction/HCO3– diffusion dual ion channel ( PCBD ion channel) was constructed by assembling γ-cyclodextrin-MOF (γ-CD-MOF) and 3,4,9,10-perylenetetracarboxylic acid (Ptca) for CO2 chemiresistive sensing. The obtained CO2 sensor exhibited high response (Rg/R0 = 1.33, 50 ppm) and selectivity, low practical limit of detection (1 ppm) and excellent consistency (94.5%) with a commercial infrared CO2 meter at room temperature. It is indicated that hydrogen bond networks in PCBD ion channel will be enlarged with the increasing of carboxylic group’s content on perylene skeleton, thereby modulating proton conductivity at molecular level and furthermore CO2 sensing performance of the composite. The PCBD ion channel-based sensor has been utilized to distinguish the exhaled CO2 concentration between lung cancer patients and healthy individuals, illustrating its promising application prospect in non-invasive diagnose. © 2024, The Authors. All rights reserved.

关键词： Carbon dioxide

Hybrid Collaborative Filtering-Based API Recommendation

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Hybrid Collaborative Filtering-Based API Recommendation

IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Yongchao Wang Yu Zhou Taolue Chen Jingxuan Zhang Wenhua Yang Zhiqiu Huang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Lab. for Novel Software Technology Nanjing University China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology China University of London Birkbeck UK

ISBN: (纸本)9781665458146

Automatic API recommendations can liberate software developers from labor-intensive programming tasks. Collaborative filtering (CF) techniques, which have been proved to be superior to other classic techniques, are widely used in recommendation tasks such as music, book, and goods recommendations, but are rarely used in the recommendation of APIs. In this paper, we employ the hybrid of CF techniques to build an API recommendation system. More precisely, We treat the API recommendation task as an item recommendation problem, where method declarations are regarded as users, API calls are regarded as items. First, we use the memory-based CF technique to find the most similar projects, collect the most similar declarations, and take API calls used by the considered declarations together to generate a rating matrix. Next, we use the model-based CF technique to complete the missing values in the rating matrix, then a ranked list of APIs is generated based on the completed rating matrix and sent to the developers as a recommendation result. Experimental results show that compared with the state-of-the-art work, the proposed approach can achieve better performance in terms of a comprehensive set of metrics, such as Success Rate, Precision, Recall, MRR, and NDCG for the top-1, top-3 and top-5 recommended APIs.

关键词： Measurement Weight measurement Collaborative filtering Software quality Programming Reliability engineering Software reliability