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arXiv 2025年

作者： Zuo, Xiaoxiong Tao, Yihan Huang, Yang Kang, Zhixuan Chen, Huaxi Cui, Chenzhou Pan, Jiashu Kong, Xiao Tang, Xiaoyu Han, Henggeng Mu, Haiyang Xu, Yunfei Fan, Dongwei Xue, Guirong Luo, Ali Liu, Jifeng National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China National Astronomical Data Center Beijing100101 China Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Research Center for Astronomical Computing Zhejiang Lab Hangzhou311100 China School of Engineering Westlake University Zhejiang Hangzhou310030 China Zhejiang Lab Hangzhou311100 China Institute for Frontiers in Astronomy and Astrophysics Beiing Normal University Beijing102206 China New Cornerstone Science Laboratory NationalAstronomical Observatories Chinese Academy of Sciences Beiing100012 China

Time-domain surveys have advanced astronomical research by revealing diverse variable phenomena, from stellar flares to transient events. The scale and complexity of survey data, along with the demand for rapid classification, present significant challenges for analysis. While machine learning offers solutions, most existing models are tailored to single tasks, struggle to generalize, and depend heavily on large, accurately labeled datasets. We introduce FALCO, a foundation model for astronomical light curve analysis in time-domain astronomy. This paper presents the primary version of FALCO trained via self-supervised learning on unlabeled Kepler light curves using a Transformer-based architecture. The model has been evaluated across three distinct light curve analysis tasks and it demonstrates robust performance in all tasks, achieving an accuracy of 95% for stellar variability classification across eight classes, an overall RMSE of 0.1305 dex in surface gravity estimation (with significantly improved precision of RMSE Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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GECAM Localization of High Energy Transients and the Systematic Error

arXiv

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arXiv 2022年

作者： Zhao, Yi Xue, Wang-Chen Xiong, Shao-Lin Wang, Yuan-Hao Liu, Jia-Cong Luo, Qi Zhang, Yan-Qiu Sun, Jian-Chao Zhao, Xiao-Yun Cai, Ce Xiao, Shuo Huang, Yue Li, Xiao-Bo Zhang, Zhen Liao, Jin-Yuan Yang, Sheng Qiao, Rui Guo, Dong-Ya Zheng, Chao Yi, Qi-Bin Xie, Sheng-Lun Guo, Zhi-Wei Li, Chao-Yang Wang, Chen-Wei Tan, Wen-Jun Wang, Yue Peng, Wen-Xi Zheng, Shi-Jie He, Jian-Jian Wang, Ping Wang, Jin Ma, Xiang Song, Xin-Ying Zhang, Hong-Mei Li, Bing Zhang, Peng Wu, Hong Du, Yan-Qi Liang, Jing Zhao, Guo-Ying Li, Xin-Qiao Wen, Xiang-Yang An, Zheng-Hua Sun, Xi-Lei Xu, Yan-Bing Zhang, Fan Zhang, Da-Li Gong, Ke Liu, Ya-Qing Liang, Xiao-Hua Liu, Xiao-Jing Gao, Min Wang, Jin-Zhou Song, Li-Ming Chen, Gang Zhang, Ke-Ke Han, Xing-Bo Wu, Hai-Yan Hu, Tai Geng, Hao Lu, Fang-Jun Zhang, Shu Zhang, Shuang-Nan Lu, Gao-Peng Zeng, Ming Yu, Heng Department of Astronomy Beijing Normal University Beijing100875 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China College of Physics Hebei Normal University 20 South Erhuan Road Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China Institute of Astrophysics Central China Normal University Wuhan430079 China College of Physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Province Baoding City071002 China Physics and Space Science College China West Normal University Nanchong637002 China College of Electronic and information Engineering Tongji University Shanghai201804 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai201304 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Earth and Space Sciences University of Science and Technology of China Hefei230026 China Ministry of Education Beijing100084 China Department of Engineering Physics Tsinghua University Beijing100084 China

Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a pair of microsatellites (i.e. GECAM-A and GECAM-B) dedicated to monitoring gamma-ray transients including gravitational waves high-energy electromagnetic counterparts, Gamma-ray Bursts, Soft Gamma-ray Repeaters, Solar Flares and Terrestrial Gamma-ray Flashes. Since launch in December 2020, GECAM-B has detected hundreds of astronomical and terrestrial events. For these bursts, localization is the key for burst identification and classification as well as follow-up observations in multi-wavelength. Here, we propose a Bayesian localization method with Poisson data with Gaussian background profile likelihood to localize GECAM bursts based on the burst counts distribution in detectors with different orientations. We demonstrate that this method can work well for all kinds of bursts, especially for extremely short ones. In addition, we propose a new method to estimate the systematic error of localization based on a confidence level test, which can overcome some problems of the existing method in literature. We validate this method by Monte Carlo simulations, and then apply it to a burst sample with accurate location and find that the mean value of the systematic error of GECAM-B localization is ∼ 2.5◦. By considering this systematic error, we can obtain a reliable localization probability map for GECAM bursts. Our methods can be applied to other gamma-ray monitors. Copyright © 2022, The Authors. All rights reserved.

关键词： Errors

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Deciphering the Spatial Stickiness, Co-occurrence, and Co-change of Industries at the Scale of City

International Journal on Smart and Sustainable Cities

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International Journal on Smart and Sustainable Cities 2023年第2期01卷

作者： Lock Yue Chew Ning Ning Chung Hoai Nguyen Huynh Wen Xuan Sia Alvin Chua School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Data Science & Artificial Intelligence Research Centre Nanyang Technological University Singapore 639798 Singapore Centre for University Core Singapore University of Social Sciences Singapore 599494 Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way Connexis North Tower #16-16 Singapore 138632 Singapore Design & Planning Lab Urban Redevelopment Authority Singapore 179369 Singapore

This paper analyzes the location of industries in two metropolitan cities to investigate how firms are spatially distributed, and identify the prevailing trends in firm location choice. By defining a “Region Index” metric, we show how industries have concentrated in or diffused from the regions-of-interest over time. We apply this metric to the Central Area (CA) of Singapore and the London Region to decipher the relative tendency of industries to locate at CA as well as London. To analyze the spatial co-location of industries, we constructed a community network, which reveals subtle spatial relationships between firms. Extending this approach temporally to create a co-change network, we deduce the occurrences of industries in Singapore and London that move into or out of a spatial region synchronously. Based on this approach, we elucidate firm types that not only co-change but also co-occur in space and remain within a region-of-interest. Overall, we observe certain common and contrasting patterns of industry movements in the context of Singapore and London through the analysis. “Other Activities Auxiliary to Financial Services” is found to have the consistent tendency of diffusing out of CA and London throughout the period of study. Conversely, opposite movement patterns are observed where we see the proclivity of IT and digital service firms to locate in London but relocate out of CA, while the Media, Financial Services, and Life Insurance subsectors tend to diffuse out of London but concentrate in the CA of Singapore.

关键词： Spatial morphology cities spatial indices land use urban complexity spatial analysis networks

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Modeling Distributed Stream Processing Systems Under Heavy Workload

Modeling Distributed Stream Processing Systems Under Heavy W...

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International Conference on Cyberworlds

作者： Muhammad Mudassar Qureshi Hanhua Chen Hai Jin Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Big data applications play a significant role in diverse fields. Distributed Stream Processing Engines (DSPEs) are widely used to support real time applications efficiently. Partitioning algorithms are used to partition data streams into multiple nodes to process in parallel to gain efficient performance. Aggregation cost is an important factor when process stateful streaming applications using such partitioning algorithms because it plays an important role on performance when final result is being produced in stateful streaming applications. However, impact of aggregation cost in stream processing is not discussed comprehensively in existing literature. We use performance modeling to identify the importance of aggregation cost when workload is high. We implement performance model on a multi-node cluster to predict the same behavior as on single resource performance model. We demonstrate that stateful streaming applications need more resources as compare to stateless applications when workload is high and both stateful and stateless applications are running in the same DSPE. Experiments results show that a stateful streaming application needs more resources compared to a stateless streaming application when both applications are running on the same DSPE when the workload is high. Further experiment results show that the performance modeling may be helpful to predict maximum workload that can be process on a DSPE and increase in parallelism level is not guaranteed to increase the performance of streaming applications.

关键词： Partitioning algorithms data models Predictive models Heuristic algorithms Big data applications Throughput

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Multi-thread concurrent compression algorithm for genomic big data 20

Multi-thread concurrent compression algorithm for genomic bi...

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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Ji, Yimu Chen, Shuai Yao, Haichang Fang, Houzhi Li, Kui Liu, Shangdong Xie, Zhengyuan Wang, Kairui School of Computer Science Nanjing University of Posts and Telecommunications Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC Intelligent Processing Engineer Research Center Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecom. School of Computer and Software Nanjing Institute of Industry Technology Nanjing China University of Toronto Toronto Canada BSc Mathematics with Finance University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9781728126166

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://***/APandaThief/FastLNGC. © 2019 IEEE.

关键词： Big data

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Accelerated Latent Factor Analysis for Recommender Systems via PID Controller

Accelerated Latent Factor Analysis for Recommender Systems v...

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IEEE International Conference on Networking, Sensing and Control

作者： Jinli Li Xuke Wu Ye Yuan Yajuan Wu Kangkang Ma Yue Zhou Computer School China West Normal University Nanchong Sichuan China Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of information and software engineering University of Electronic Science and Technology of China Chengdu Sichuan China University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

High-dimensional and sparse (HiDS) matrices generated by recommender systems (RSs) contain rich knowledge. A latent factor (LF) model can address such data effectively. Stochastic gradient descent (SGD) is an efficient algorithm for building a LF model on an HiDS matrix. However, it suffers slow convergence. To address this issue, this study proposes to implement a LF model with a proportional integral derivative (PID) controller. The main idea is to continuously apply a correction for SGD to accelerate the training process. Based on such design, a PID-based LF (PLF) model is proposed. Empirical studies on two HiDS matrices from RSs indicate that a PLF model outperforms an LF model in terms of both convergence rate and prediction accuracy for missing data.

关键词： Training Predictive models data models Sparse matrices Acceleration Recommender systems Convergence

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Advances in Computational Intelligence Techniques-Based Multi-Intersection Querying Theory for Efficient QoS in the Next Generation Internet of Things

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Computational intelligence and neuroscience 2023年第1期2023卷 1388425页

作者： Ashish Kumar Kannan K Mamta Dahiya Virendra Singh Kushwah Ayesha Siddiqa Kiranjeet Kaur Saima Ahmed Rahin Department of CSE Manipal University Jaipur Jaipur Rajasthan India. Department of CSE Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh India. Department of Computer Science and Engineering SGT University Gurugram India. School of Computing Science and Engineering (SCSE) VIT Bhopal University Sehore India. Artificial Intelligence and Data Science Department Islamia Engineering College Hyderabad Telangana India. Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab 140413 India. United International University Dhaka Bangladesh.

An environment of physically linked, technologically networked things that can be found online is known as the "Internet of Things." With the use of various devices connected to a network that allows data transfer between these devices, this includes the creation of intelligent communications and computational environments, such as intelligent homes, smart transportation systems, and intelligent FinTech. A variety of learning and optimization methods form the foundation of computational intelligence. Therefore, including new learning techniques such as opposition-based learning, optimization strategies, and reinforcement learning is the key growing trend for the next generation of IoT applications. In this study, a collaborative control system based on multiagent reinforcement learning with intelligent sensors for variable-guidance sections at various junctions is proposed. In the future generation of Internet of Things (IoT) applications, this study provides a multi-intersection variable steering lane-appropriate control approach that uses intelligent sensors to reduce traffic congestion at many junctions. Since the multi-intersection scene's complicated traffic flow cannot be accommodated by the conventional variable steering lane management approach. The priority experience replay algorithm is also included to improve the efficiency of the transition sequence's use in the experience replay pool and speed up the algorithm's convergence for effective quality of service in the upcoming IoT applications. The experimental investigation demonstrates that the multi-intersection variable steering lane with intelligent sensors is an appropriate control mechanism, successfully reducing queue length and delay time. The effectiveness of waiting times and other indicators is superior to that of other control methods, which efficiently coordinate the strategy switching of variable steerable lanes and enhance the traffic capacity of the road network under multiple intersections

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Integrated information as a common signature of dynamical and information-processing complexity

arXiv

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arXiv 2021年

作者： Mediano, Pedro A.M. Rosas, Fernando E. Farah, Juan Carlos Shanahan, Murray Bor, Daniel Barrett, Adam B. Department of Psychology University of Cambridge CambridgeCB2 3EB United Kingdom Centre for Psychedelic Research Department of Brain Science Imperial College London LondonSW7 2DD United Kingdom Data Science Institute Imperial College London LondonSW7 2AZ United Kingdom Centre for Complexity Science Imperial College London LondonSW7 2AZ United Kingdom School of Engineering École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland Department of Computing Imperial College London LondonSW7 2RH United Kingdom Sackler Center for Consciousness Science Department of Informatics University of Sussex BrightonBN1 9RH United Kingdom The Data Intensive Science Centre Department of Physics and Astronomy University of Sussex BrightonBN1 9QH United Kingdom

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behaviour in both dynamical and information-processing systems. Here we argue that a pragmatic use of Integrated Information Theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. Furthermore, by leveraging metrics put forward by the integrated information decomposition (ΦID) framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity - including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata - without relying on idiosyncratic, ad-hoc criteria. These results show how an agnostic use of IIT can provide important steps towards bridging the gap between informational and dynamical approaches to complex systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Information theory

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A two-phase approach for locally differentially private fuzzy co-clustering

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Information sciences 2025年 718卷

作者： Zhang, Pengfei An, Jianlong Cheng, Xiang Zhu, Youwen Zhang, Ji Zhang, Zhikun State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining No. 168 Shungeng Road Tianjiaan District Huainan232001 China School of Computer Science and Engineering Anhui University of Science and Technology No. 168 Shungeng Road Tianjiaan District Huainan232001 China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics No. 237 Longquan Road Yunnan Kunming650221 China No. 3501 Daxue Road Shandong Jinan255020 China Key Laboratory of Equipment Data Security and Guarantee Technology Guilin University of Electronic Technology No. 1 Jinji Road Guangxi Guilin541010 China State Key Laboratory of Networking and Switching Technology No. 10 Xitucheng Road Haidian District Beijing100876 China Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District Beijing100876 China College of Computer and Data Science Nanjing University of Aeronautics and Astronautics No. 29 Yudao Street Qinhuai District Jiangsu Nanjing210016 China School of Mathematics Physics and Computing University of Southern Queensland West Street ToowoombaQLD4350 Australia College of Computer Science and Technology Zhejiang University No. 38 Zheda Road Xihu District Hangzhou310027 China

Clustering analysis is important for providing personalized services across various user experience optimization. As an advanced clustering technique, Fuzzy Co-Clustering (FCC) excels at handling high-dimensional data by simultaneously grouping both samples and attributes while allowing objects to belong to multiple clusters. To alleviate the potential privacy risks posed by directly using raw data, researchers often employ local differential privacy (LDP) techniques for protection. However, current clustering studies under LDP either directly perform clustering on the high-dimensional data, which necessitates splitting the privacy budget heavily, leading to excessive noise injection, or fail to adequately address the impact of Laplacian noise on both objective function modeling and distance metric selection, resulting in reduced clustering accuracy. To address these limitations, we present a two-phase approach SONY, which allocates part of the privacy budget to evaluate attribute importance and perform dimensionality reduction accordingly, and then utilizes the remaining budget to conduct noise-aware clustering on the pruned data. Our key technical contributions include: (1) a Noise-aware Similarity Calculation method (NSC) that replaces traditional Euclidean distance with a specialized metric which accounts for the statistical properties of Laplacian noise;(2) an importance-driven dimension reduction and budget allocation method (IDRBA) which optimizes both attribute selection and privacy budget allocation;and (3) an outlier-aware objective function calculation method (OOC) which enhances robustness against potential noise-induced outliers. We provide theoretical guarantees for SONY's convergence, privacy preservation, utility bounds, and computational complexity. Experimental evaluations conducted on two real-world datasets and one synthetic dataset, demonstrate that SONY outperforms the state-of-the-arts by at least 14% in F-Measure and 10% in Entropy while main

关键词： data privacy

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Correction to: development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

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Journal of Cloud computing 2023年第1期12卷 1-1页

作者： Kumar, Ankit Khan, Surbhi Bhatia Pandey, Saroj Kumar Shankar, Achyut Maple, Carsten Mashat, Arwa Malibari, Areej A. Department of Computer Engineering & Applications GLA University Mathura India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester Byblos UK WMG University of Warwick Coventry UK Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK Faculty of Computing and Information Science King Abdulaziz University Rabigh Saudi Arabia Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

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