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arXiv 2023年

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2023, CC BY-NC-SA.

关键词： Maximum principle

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms

Enhancing Security and Scalability of Metaverse with Blockch...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Anand Singh Rajawat S.B. Goyal Aarti Goyal Kavita Rajawat Maria Simona Raboaca Chaman Verma Traian Candin Mihaltan School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Mind Intellect Technology Indore India ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world.

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A Character Recognition Method for Chip Surface Characters Based on Text Replacement

SSRN

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SSRN 2022年

作者： Xu, Wei Shi, Yecun Xi, Yu Liu, Jingyue Wang, Tan Zhang, Haichao Guo, Cuijuan Bai, Jinjun Xu, Degang Yao, Jianquan Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems School of Electronic and Information Engineering Tiangong University Tianjin300387 China Microelectronic Device Reliability Laboratory Research Test Center of Defense Technology for Aerospace Science and Industry Beijing100854 China Department of Electrical and Computer Engineering Northeatern University BostonMA02115 United States School of Precision Instruments and Optoelectronics Engineering Institute of Laser and Optoelectronics Tianjin University Tianjin300072 China

Due to the particular screen printing rules defined by the unique chip manufacturer and the huge variety of chips, it is difficult to identify chip information by naked eye in chip reliability detection. Automatic identification character technology can help us obtain the information of the chip. But the special character samples in the dataset is extremely uneven. When recognizing characters on chip surface by deep learning, the limited certain characters dataset will reduce the recognition accuracy. A character recognition method for chip surface characters based on text replacement is proposed in this paper. Firstly, the efficient channel attention modules are added to enhance the feature extraction of characters in the text style transfer process. Secondly, the dilated convolution layers are used to replace the residual module during the background restoration process, which help to obtain a more comprehensive background texture feature. Finally, a one-stage target detection network YOLOv5s is used to detect the characters on the chip surface. The experimental results demonstrated that the three evaluation metrics for image generation quality (l2 error, psnr, and ssim) achieved better performance than previous work due to the text replacement network. The overall charac- © 2022, The Authors. All rights reserved.

关键词： Convolution

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Machine Learning Techniques for Analysis of Mars Weather Data

Machine Learning Techniques for Analysis of Mars Weather Dat...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Piyush Pant Anand Singh Rajawat S. B. Goyal Baharu Bin Kemat Traian Candin Mihălţan Chaman Verma Maria Simona Răboacă School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia City Graduate School City University Petaling Jaya Malaysia Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Romania

The exploration of Mars has provided vast amounts of weather data that present unique challenges for analysis and prediction. To address these challenges, this research paper focuses on the application of machine learning techniques for the analysis of Mars weather data. The objective is to develop models that can effectively extract patterns, uncover hidden relationships, and enable accurate predictions in the Martian weather system. The research begins with a comprehensive review of the available Mars weather data. The dataset, consisting of historical records, serves as the foundation for training and evaluating machine learning models. The analysis of the Mars weather dataset reveals the planet's icy and harsh climate, with average maximum temperatures of around -21°C and average minimum temperatures of -80°C. The temperature variations show that the minimum temperature fluctuates within a narrower range of 20-30°C over the course of Mars sols from 0 to 2000, while the maximum temperature experiences larger variations of about 40-50°C. During this time, the atmospheric pressure on Mars fluctuates between 720 Pa and 950 Pa. In addition, using the elbow method revealed that 3 clusters were the ideal number for identifying distinct patterns in the weather data. The linear regression model also attained an accuracy of 85%, demonstrating its efficacy in forecasting weather patterns on Mars.

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A Van der Waals Moiré Bilayer Photonic Crystal Cavity

arXiv

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arXiv 2025年

作者： Spencer, Lesley Coste, Nathan Ni, Xueqi Park, Seungmin Schaeper, Otto C. Kim, Young Duck Taniguchi, Takashi Watanabe, Kenji Toth, Milos Zalogina, Anastasiia Tang, Haoning Aharonovich, Igor School of Mathematical and Physical Sciences University of Technology Sydney UltimoNSW2007 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems University of Technology Sydney UltimoNSW2007 Australia Department of Physics National University of Singapore 119077 Singapore Department of Physics Kyung Hee University Seoul02447 Korea Republic of International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

Enhancing light-matter interactions with photonic structures is critical in classical and quantum nanophotonics. Recently, Moiré twisted bilayer optical materials have been proposed as a promising means towards a tunable and controllable platform for nanophotonic devices, with proof of principle realisations in the near infrared spectral range. However, the realisation of Moiré photonic crystal (PhC) cavities has been challenging, due to a lack of advanced nanofabrication techniques and availability of standalone transparent membranes. Here, we leverage the properties of the van der Waals material hexagonal Boron Nitride to realize Moiré bilayer PhC cavities. We design and fabricate a range of devices with controllable twist angles, with flatband modes in the visible spectral range (~ 450 nm). Optical characterization confirms the presence of spatially periodic cavity modes originating from the engineered dispersion relation (flatband). Our findings present a major step towards harnessing a two-dimensional van der Waals material for the next-generation of on chip, twisted nanophotonic systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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Recent measurement of the Telescope Array energy spectrum and observation of the shoulder feature in the Northern Hemisphere 37

Recent measurement of the Telescope Array energy spectrum an...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Ivanov, D. Bergman, D. Furlich, G. Gonzalez, R. Thomson, G.B. Tsunesada, Y. Abbasi, R.U. Abe, M. Abu-Zayyad, T. Allen, M. Arai, Y. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kido, E. Kim, H.B. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Suzawa, T. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. University of Utah Department of Physics & Astronomy High Energy Astrophysics Institute Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics Loyola University Chicago ChicagoIL United States The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Todai Institutes for Advanced Study University of Tokyo Chiba Kashiwa Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics Sungkyunkwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawashi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Bruss

The Telescope Array (TA) is a hybrid cosmic ray detector deployed in 2007 in Millard County, Utah, USA, which consists of a surface detector of 507 plastic scintillation counters spanning a 700 km2 area on the ground that is overlooked by three fluorescence detector stations. The High Resolution Fly’s Eye (HiRes) experiment is a predecessor of TA, which consisted of two fluorescence detector stations operating from 1997 until 2006 from Dugway Proving Ground, Utah, USA, and which was the the first cosmic ray experiment with sufficient resolution and exposure to successfully observe the Greisen–Zatsepin–Kuzmin (GZK) suppression at 1019.75 eV. In this work, we present an updated TA energy spectrum result and a joint fit of independent spectrum measurements by the TA surface detector, TA fluorescence detector, and HiRes fluorescence detector to a broken power law function, which exhibits the ankle, GZK suppression, and the new shoulder feature initially seen by the Pierre Auger Observatory in the Southern Hemisphere. HiRes and TA observe the shoulder feature in the Northern Hemisphere at 1019.25 eV, with a statistical significance of 5.3 standard deviations. © Copyright owned by the author(s).

关键词： Cosmic rays

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Geographic information science in the era of geospatial big data: A cyberspace perspective

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The Innovation 2022年第5期3卷 21-22页

作者： Xintao Liu Min Chen Christophe Claramunt Michael Batty Mei-Po Kwan Ahmad MSenousi Tao Cheng Josef Strobl Arzu Cöltekin John Wilson Temenoujka Bandrova Milan Konecny Paul MTorrens Fengyuan Zhang Li He Jinfeng Wang Carlo Ratti Olaf Kolditz Alexander Klippel Songnian Li Hui Lin Guonian Lu Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong SARKowloonChina Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China The French Naval Academy Research Institute 29240 Lanveoc-PoulmicFrance The Bartlett Centre for Advanced Spatial Analysis(CASA) University College London(UCL)WC1E 6BT LondonUK Institute of Space and Earth Information Science The Chinese University of Hong KongHong Kong SARShatin 999077China Department of Civil Engineering Cairo University12613 CairoEgypt SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)WC1E 6BT LondonUK Department of Geoinformatics University of Salzburg5020 SalzburgAustria Institute of Interactive Technologies(IIT) University of Applied Sciences and Arts Northwestern Switzerland FHNW5210 WindischSwitzerland Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90007USA Laboratory on Cartography Department of Photogrammetry and CartographyUniversity of ArchitectureCivil Engineering and Geodesy1164 SofiaBulgaria Laboratory on Geoinformatics and Cartography Department of GeographyMasaryk University61137 BrnoCeskáRepublika Computer Science and Engineering Tandon School&Center for Urban Science+ProgressNew York UniversityNew YorkNY 10012USA School of Humanities and Social Science Xi’an Jiaotong UniversityXi’an 710049China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMA 02139USA Centre for Environmental Research Helmholtz Centre for Environmental Research-UFZ04318 LeipzigGermany Laboratory of Geo-information Science and Remote Sensing Wageningen University&ResearchWageningen 6708the Netherlands Department of Civil Engineering Toronto Metropolitan UniversityTorontoON M5B 2K3Canada School of Geography and Env

The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

关键词： computer cyber instance

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Graph Neural Networks in computer Vision - Architectures, Datasets and Common Approaches

arXiv

引用

arXiv 2022年

作者： Krzywda, Maciej Lukasik, Szymon Gandomi, Amir H. Faculty of Physics and Applied Computer Science AGH University of Science and Technology al. Mickiewicza 30 Kraków30-059 Poland Faculty of Engineering and IT University of Technology Sydney 5 Broadway UltimoNSW2007 Australia Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland

Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand;it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field. © 2022, CC BY-SA.

关键词： computer vision

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Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

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InfoMat 2020年第6期2卷 1131-1162页

作者： Qiongfeng Shi Bowei Dong Tianyiyi He Zhongda Sun Jianxiong Zhu Zixuan Zhang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore117576Singapore Center for Intelligent Sensors and MEMS National University of SingaporeSingapore117608Singapore Hybrid-Integrated Flexible(Stretchable)Electronic Systems Program National University of SingaporeSingapore117608Singapore NUS Suzhou Research Institute(NUSRI) Suzhou215123China NUS Graduate School for Integrative Science and Engineering National University of SingaporeSingapore117456Singapore

The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life,for example,healthcare monitoring and treatment,ambient monitoring,soft robotics,prosthetics,flexible display,communication,human-machine interactions,and so *** to the development in recent years,the next-generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence(AI)and internet of things(IoT),to achieve a higher level of comfort,convenience,connection,and ***,this review provides an opportune overview of the recent progress in wearable electronics,photonics,and systems,in terms of emerging materials,transducing mechanisms,structural configurations,applications,and their further integration with other ***,development of general wearable electronics and photonics is summarized for the applications of physical sensing,chemical sensing,humanmachine interaction,display,communication,and so *** self-sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage ***,technology fusion of wearable systems and AI is reviewed,showing the emergence and rapid development of intelligent/smart *** the last section of this review,perspectives about the future development trends of the next-generation wearable electronics/photonics are provided,that is,toward multifunctional,self-sustainable,and intelligent wearable systems in the AI/IoT era.

关键词： artificial intelligence energy harvesting human-machine interface internet of things wearable electronics wearable photonics

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TrueCome: Effective data truth discovery based on fuzzy clustering with prior constraints

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Information Sciences 2025年 717卷

作者： Gao, Lijun Wu, Fei Wang, Jie Yan, Zheng Pedrycz, Witold School of Cyber Engineering Xidian University Shaanxi Xi'an710126 China Department of Electrical and Computer Engineering University of Alberta EdmontonAB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istinye Universitys Istanbul Sariyer Turkey

Data truth discovery is a process to determine accurate information from multiple conflicting data sources. Existing truth discovery schemes suffer from low efficiency and insufficient accuracy caused by noisy data and source unreliability, particularly facing three key limitations: (1) inability to leverage inter-attribute constraints for distance metric learning, (2) lack of effective mechanisms for distinguishing truth clusters from noisy streaming data. Few of them can discover the truth for streaming data with noise, and (3) static source reliability estimation that fails to adapt to streaming data dynamics. Few of them can discover the truth for streaming data with noise. To overcome these problems, we propose TrueCome, a possibilistic C-Means truth discovery scheme that leverages constraints between different attributes of an object and applies dynamically updated data source reliability to discover truth for both static and streaming data. TrueCome contains two functional modules: distance learning and truth discovery. The distance learning module constructs a distance function by mining prior constraints of object attributes. Then, the truth discovery module obtains the true values of an object through three steps: data clustering based on data sample distance and data source reliability and attribute weights, truth cluster identification by calculating cluster trust degrees, and truth acquisition derived from True Value Clusters (TVCs). In particular, TrueCome employs Maximum A Posteriori (MAP) estimation to adaptively update source reliability (i.e., source weight), allowing it to handle both static and streaming data effectively. Extensive experiments on two real-world datasets and one synthetic dataset exhibit the superiority of TrueCome over several baselines in terms of accuracy and efficiency, particularly for streaming data with noise. We also validate the design rationality of TrueCome through ablation studies. © 2025 Elsevier Inc.

关键词： Data reduction

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