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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Light, the universe, and everything-12 Herculean tasks for quantum cowboys and black diamond skiers

arXiv

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arXiv 2018年

作者： Agarwal, Girish Allen, Roland E. Bezdìková, Iva Boyd, Robert W. Chen, Goong Hanson, Ronald Hawthorne, Dean L. Hemmer, Philip Kim, Moochan B. Kocharovskaya, Olga Lee, David M. Lidström, Sebastian K. Lidström, Suzy Losert, Harald Maier, Helmut Neuberger, John W. Padgett, Miles J. Raizen, Mark Rajendran, Surjeet Rasel, Ernst Schleich, Wolfgang P. Scully, Marlan O. Shchedrin, Gavriil Shvets, Gennady Sokolov, Alexei Svidzinsky, Anatoly Walsworth, Ronald L. Weiss, Rainer Wilczek, Frank Willner, Alan E. Yablonovich, Eli Zheludev, Nikolay Department of Physics and Astronomy Texas A&M University College StationTX77843 United States Department of Physics Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague Brehova 7 Praha 1-Staré mìsto11519 Czech Republic Department of Physics Technical University of Ostrava 17 Listopadu 15 Ostrava-Poruba70833 Czech Republic Department of Physics School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N6N5 Canada Institute of Optics Department of Physics and Astronomy University of Rochester RochesterNY14627 United States School of Physics and Astronomy University of Glasgow GlasgowG128QQ United Kingdom Department of Mathematics Texas A&M University College StationTX77843 United States Science Program Texas A&M University at Qatar Education City Doha Qatar Texas A&M University College StationTX77843 United States QuTech and Kavli Institute of Nanoscience Delft University of Technology P.O. Box 5046 Delft2600 GA Netherlands Cornell Laboratory of Ornithology Cornell University IthacaNY14850 United States Laboratory of Atomic and Solid State Physics Cornell University IthacaNY14853 United States Department of Electrical & Computer Engineering Texas A&M Energy Institute Texas A&M University TX77843 United States Department of Physics Strathclyde University GlasgowG11XJ United Kingdom Physica Scripta Royal Swedish Academy of Sciences StockholmSE-10405 Sweden Universität Ulm Albert-Einstein-Allee 11 Ulm89081 Germany Institut für Zahlentheorie und Wahrscheinlichkeitstheorie Universität Ulm Albert-Einstein-Allee 11 Ulm89081 Germany Department of Mathematics University of North Texas DentonTX76203-5017 United States College of Natural Sciences University of Texas AustinTX78712 United States Department of Physics University of California BerkeleyCA94720-7300 United States Institut fuer Quantenoptik QUEST-LFS Welfengarten 1 Hannover30167 Germany Hagler Insti

The Winter Colloquium on the Physics of Quantum Electronics (PQE) has been a seminal force in quantum optics and related areas since 1971. It is rather mindboggling to recognize how the concepts presented at these conferences have transformed scientific understanding and human society. In January, 2017, the participants of PQE were asked to consider the equally important prospects for the future, and to formulate a set of questions representing some of the greatest aspirations in this broad field. The result is this multi-authored paper, in which many of the world's leading experts address the following fundamental questions: (1) What is the future of gravitational wave astronomy? (2) Are there new quantum phases of matter away from equilibrium that can be found and exploited-such as the time crystal? (3) Quantum theory in uncharted territory: What can we learn? (4) What are the ultimate limits for laser photon energies? (5) What are the ultimate limits to temporal, spatial, and optical resolution? (6) What novel roles will atoms play in technology? (7) What applications lie ahead for nitrogen-vacancy centers in diamond? (8) What is the future of quantum coherence, squeezing, and entanglement for enhanced superresolution and sensing? (9) How can we solve (some of) humanity's biggest problems through new quantum technologies? (10) What new understanding of materials and biological molecules will result from their dynamical characterization with free electron lasers? (11) What new technologies and fundamental discoveries might quantum optics achieve by the end of this century? (12) What novel topological structures can be created and employed in quantum optics? Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum optics

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Designing the Application of Security Text Messages Into Audio Files Using Data Encryption Standard (DES) Algorithms Using the End Of File (EOF) Method

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Journal of Physics: Conference Series 2019年第1期1363卷

作者： A M H Pardede Anggi Pramana Muhammad Zarlis Akbar Iskandar Rosida Tiurma Manurung S Sriadhi Citra Kurniawan Abdurrozzaq Hasibuan Nurintan Asyiah Siregar Ayu Esteka Sari Indrya Mulyaningsih Teguh Hidayat Iskandar Alam Ibrahim Tri Listyorini Edy Winarno Tulus STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Department of Informatics Engineering Universitas Muhammadiyah Sorong Sorong Indonesia Department of English Education Universitas Muhammadiyah Sorong Sorong Indonesia Department of Informatics Universitas Muria Kudus Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia

There are several ways to deal with security issues of confidential data sent via the internet, including using cryptographic techniques and steganography. Steganography is the science and art of concealing information / messages in a media in such a way that its existence is not detected by other parties who are not entitled to the information. Instead, cryptography disguises the meaning of a message, but does not hide that there is a message because the file looks suspicious. The Steganography technique used here was End Of File (EOF). The EOF technique described how to add data or files at the end of the image file. For this technique, the size of data or files that will be hidden could be larger than the size of the image file. The hidden data will be inserted at the end of the file so that it will not affect the image. This steganography application is also equipped with a cryptographic function Data Encryption Standard (DES) at the time of insertion of data that functions as a generator code and encrypts data, so that the security of a data in the file is more protected and protected from those who are not entitled to know the data.

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The Effect of Mixed Pertamax with Tuak Nias (Tuonifaro) on Fuel Consumption at 125CC Machine and Its Dissemination Using Website

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Kristian Tarigan Erianto Ongko H Hartono Dahlan Abdullah Cut Ita Erliana Nurmawati Sulfikar Sallu S Sriadhi Akbar Iskandar Nuning Kurniasih GS Achmad Daengs Ricardo Freedom Nanuru A A Saleh Muhammad Ikhsan Setiawan Wiwien Hadikurniawati Edy Winarno Department of Mechanical Engineering Universitas Darma Agung Medan Indonesia Department of Informatics Akademi Teknologi Industri ImmanuelIndonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Faculty Of Information Technology Universitas Sembilan Belas November Kolaka Sulawesi Tenggara Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Management Universitas 45 Surabaya Indonesia Universitas Kristen Indonesia Maluku. Ambon Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

The purpose of this research is to know how influence of mixture of pertamax with Tuak Nias (Tuonifaro) to fuel consumption at 125 cc engine To prove that Tuak Nias or Tuonifaro can be used as fuel mixture on motorcycle and examine how big influence of mix between Tuak Nias or Tuonifaro with Pertamax, conducting research by literature study method or literature study. Many authors use various supporting / input factors and supporting books related to the titles discussed. because Tuak Nias or tuonifaro contain low 4-5% ethanonic elements and contain 90% alcoholic elements so that the fuel easily burns easily in the combustion chamber and produces great power. The results of this research will be disseminated using the website

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

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Author Correction: 3D-printed liquid metal polymer composites as NIR-responsive 4D printing soft robot

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Nature communications 2024年第1期15卷 943页

作者： Liwen Zhang Xumin Huang Tim Cole Hongda Lu Jiangyu Hang Weihua Li Shi-Yang Tang Cyrille Boyer Thomas P Davis Ruirui Qiao Australian Institute of Bioengineering & Nanotechnology The University of Queensland Brisbane QLD 4072 Australia. Department of Electronic Electrical and Systems Engineering University of Birmingham Birmingham UK. School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Wollongong NSW 2522 Australia. School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK. shiyang.tang@soton.ac.uk. Cluster for Advanced Macromolecular Design School of Chemical Engineering The University of New South Wales Sydney NSW 2052 Australia. Australian Institute of Bioengineering & Nanotechnology The University of Queensland Brisbane QLD 4072 Australia. t.davis@uq.edu.au. Australian Institute of Bioengineering & Nanotechnology The University of Queensland Brisbane QLD 4072 Australia. r.qiao@uq.edu.au.

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Endotaxial Stabilization of 2D Charge Density Waves with Long-Range Order

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Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Sung, Suk Hyun Agarwal, Nishkarsh Baggari, Ismail El Kezer, Patrick Goh, Yin Min Schnitzer, Noah Shen, Jeremy M Chiang, Tony Liu, Yu Lu, Wenjian Sun, Yuping Kourkoutis, Lena F Heron, John T Sun, Kai Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI USA Rowland Institute at Harvard University Cambridge MA USA Department of Electrical and Computer Engineering University of Michigan Ann Arbor MI USA John A. Paulson School of Engineering and Applied Science Harvard University Cambridge MA USA Department of Materials Science and Engineering Cornell University Ithaca NY USA Kavli Institute at Cornell for Nanoscale Science Ithaca NY USA Key Laboratory of Materials Physics Institute of Solid State Physics Chinese Academy of Sciences Hefei PR China Collaborative Innovation Centre of Advanced Microstructures Nanjing University Nanjing PR China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei PR China School of Applied and Engineering Physics Cornell University Ithaca NY USA Applied Physics Program University of Michigan Ann Arbor MI USA Department of Physics University of Michigan Ann Arbor MI USA

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Publisher Correction: Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

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Scientific reports 2019年第1期9卷 5528页

作者： Sergey D Stavisky Jonathan C Kao Paul Nuyujukian Chethan Pandarinath Christine Blabe Stephen I Ryu Leigh R Hochberg Jaimie M Henderson Krishna V Shenoy Neurosurgery Department Stanford University Stanford CA USA. sergey.stavisky@stanford.edu. Electrical Engineering Department Stanford University Stanford CA USA. sergey.stavisky@stanford.edu. Electrical Engineering Department Stanford University Stanford CA USA. Electrical and Computer Engineering Department University of California at Los Angeles Los Angeles CA USA. Neurosurgery Department Stanford University Stanford CA USA. Bioengineering Department Stanford University Stanford CA USA. Stanford Wu Tsai Neurosciences Institute Stanford University Stanford CA USA. Bio-X Program Stanford University Stanford CA USA. Neurosurgery Department Palo Alto Medical Foundation Palo Alto CA USA. Center for Neurorestoration and Neurotechnology Rehabilitation R&D Service VA Medical Center Providence RI USA. School of Engineering and Carney Institute for Brain Science Brown University Providence RI USA. Department of Neurology Harvard Medical School Boston MA USA. Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Boston MA USA. Neurobiology Department Stanford University Stanford CA USA. Howard Hughes Medical Institute at Stanford University Stanford CA USA.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

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Corrigendum: Moving Next-Generation Phase-Field Models to BMS Applications -A Case Study that Confirms Professor Uzi Landau's Foresight [J. Electrochem. Soc., 171, 063507 (2024)]

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Journal of The Electrochemical Society 2024年第8期171卷 089002-089002页

作者： Tushar K. Telmasre Anthony César Concepción Suryanarayana Kolluri Lubhani Mishra Raghav S. Thiagarajan Aditya Naveen Matam Akshay Subramaniam Taylor R. Garrick Venkat R. Subramanian Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 United States of America Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 United States of America Battery Materials and Systems Research General Motors Global Research and Development Center Warren MI 48090 United States of America Chandra Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712 United States of America Energy Storage and Propulsion R&D and Manufacturing Engineering General Motors Technical Center Warren MI 48092 United States of America Affiliate Faculty Oden Institute for Computational Science and Engineering The University of Texas at Austin Austin TX 78712 United States of America

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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