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Control flow in active inference systems

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arXiv 2023年

作者： Fields, Chris Fabrocini, Filippo Friston, Karl Glazebrook, James F. Hazan, Hananel Levin, Michael Marcianò, Antonino Allen Discovery Center Tufts University MedfordMA02155 United States College of Design and Innovation Tongji University 281 Fuxin Rd Shanghai200092 China Institute for Computing Applications "Mario Picone" Italy National Research Council Via dei Taurini 19 Rome00185 Italy Wellcome Centre for Human Neuroimaging University College London LondonWC1N 3AR United Kingdom VERSES Research Lab Los AngelesCA90016 United States Department of Mathematics and Computer Science Eastern Illinois University CharlestonIL61920 United States Adjunct Faculty Department of Mathematics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA02115 United States Center for Field Theory and Particle Physics Department of Physics Fudan University Shanghai China Laboratori Nazionali di Frascati INFN Frascati Rome Italy INFN sezione Roma "Tor Vergata" RomeI-00133 Italy

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales. © 2023, CC BY.

关键词： Free energy

Joint Channel Estimation and Signal Recovery for RIS-Empowered Multi-User Communications

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arXiv 2021年

作者： Wei, Li Huang, Chongwen Guo, Qinghua Yang, Zhaohui Zhang, Zhaoyang Alexandropoulos, George C. Debbah, Mérouane Yuen, Chau Pillar Singapore University of Technology and Design Singapore487372 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China International Joint Innovation Center Zhejiang University Haining314400 China Zhejiang-Singapore Innovation and AI Joint Research Lab Hangzhou310027 China Zhejiang Laboratory Hangzhou31121 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

TEAGS: Time-aware text embedding approach to generate subgraphs

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arXiv 2019年

作者： Hosseini, Saeid Najafipour, Saeed Cheung, Ngai-Man Yin, Hongzhi Kangavari, Mohammad Reza Zhou, Xiaofang ST Electronics - SUTD Cyber Security Lab. Singapore University of Technology and Design Singapore School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia Computational Cognitive Model Research Lab. School of Computer Engineering Iran University of Science and Technology Iran

Given a graph over which the contagions (e.g. virus, gossip) propagate, leveraging subgraphs with highly correlated nodes is beneficial to many applications. Yet, challenges abound. First, the propagation pattern between a pair of nodes may change in various temporal dimensions. Second, not always the same contagion is propagated. Hence, state-of-the-art text mining approaches ranging from similarity measures to topic-modeling cannot use the textual contents to compute the weights between the nodes. Third, the word-word co-occurrence patterns may differ in various temporal dimensions, which increases the difficulty to employ current word embedding approaches. We argue that inseparable multi-aspect temporal collaborations are inevitably needed to better calculate the correlation metrics in dynamical processes. In this work, we showcase a sophisticated framework that on the one hand, integrates a neural network based time-aware word embedding component that can collectively construct the word vectors through an assembly of infinite latent temporal facets, and on the other hand, uses an elaborate generative model to compute the edge weights through heterogeneous temporal attributes. After computing the intra-nodes weights, we utilize our Max-Heap Graph cutting algorithm to exploit subgraphs. We then validate our model through comprehensive experiments on real-world propagation data. The results show that the knowledge gained from the versatile temporal dynamics is not only indispensable for word embedding approaches but also plays a significant role in the understanding of the propagation behaviors. Finally, we demonstrate that compared with other rivals, our model can dominantly exploit the subgraphs with highly coordinated nodes. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

Generalization of CNOT-based Discrete Circular Quantum Walk: Simulation and Effect of Gate Errors

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arXiv 2020年

作者： Slimen, Iyed Ben Gueddana, Amor Lakshminarayanan, Vasudevan SysCom Lab National Engineering School of Tunis ENIT University of EL Manar 1002 Le Belvédère Tunis Tunisia Green & Smart Communication Systems Lab Gres’Com Engineering School of Communication of Tunis Sup’Com University of Carthage Ghazela Technopark Ariana2083 Tunisia Theoretical & Experimental Epistemology Lab TEEL School of Optometry and Vision Science University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada

We investigate the counterparts of random walk in universal quantum computing and their implementation using standard quantum circuits. Quantum walk have been recently well investigated for traversing graphs with certain oracles. We focus our study on traversing a 1-D graph, namely a circle, and show how to implement discrete circular quantum walk in quantum circuits built with universal CNOT and single quit gates. We review elementary quantum gates and circuit decomposition and propose a a generalized version of the all CNOT based quantum discrete circular walk. We simulated these circuits on an IBM quantum supercomputer London IBM-Q with 5 qubits. This quantum computer has non perfect gates based on superconducting qubits, therefore we analyze the impact of errors on the fidelity of the Walker circuit. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

The association for the advancement of artificial intelligence 2020 workshop program

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The association for the advancement of artificial intelligen...

作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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CEPC Technical design Report

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Radiation Detection Technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi Yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-Yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui University HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu University Institute of ScienceVaranasi Bandirma Onyedi Eylul University Bandirma Beihang University Beijing Beijing Computational Science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal University Beijing Brown University ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad University Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo University Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central China Normal University WuhanHubei Central South University ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan China academy of engineering physics MianyangSichuan China building materials academy Beijing China Center of Advanced Science and Technology Beijing China Institute of Atomic Energy Beijing China Jiliang University HangzhouZhejiang China Nuclear Instrument CO. LTDBeijing China University of Geosciences Beijing China University of Geosciences WuhanHubei China University of Mining and Technology XuzhouJiangsu China West Normal University NanchongSichuan Chongqing University Department of PhysicsChongqing Chulalongkorn University Bangkok City University of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell University IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu University DalianLiaoning Dalian University of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East China University Of Science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by China,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical design Report(TDR)follows the Preliminary Conceptual design Report(Pre-CDR,2015)and the Conceptual design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

关键词： initiated exceeding precise

NTIRE 2021 Challenge on Image Deblurring

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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： Image enhancement

Breast mass regions classification from mammograms using convolutional neural networks and transfer learning.

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Optica Acta: International Journal of Optics 2023年第10期70卷

作者： Yuliana Jiménez-Gaona María José Rodríguez-Álvarez Diana Carrión-Figueroa Darwin Castillo-Malla Vasudevan Lakshminarayanan a Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja Loja Ecuadorb Instituto de Instrumentación para la Imagen Molecular i3M Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC) Valencia Spaind Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo Waterloo Canada b Instituto de Instrumentación para la Imagen Molecular i3M Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC) Valencia Spain c Hospital Carlos Andrade Marín IESS Quito Ecuador d Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo Waterloo Canadae Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo Waterloo Canada

This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance image resolution, thus leading to more precise breast tissue segmentation and subsequent classification utilizing convolutional neural networks (CNNs). Three recognized public mammography databases: CBIS-DDSM, Mini-MIAS, and Inbreast were used as pre-processing data. Our statistical findings revealed that the EDSR method (PSNR = 39.05 dB/ SSIM = 0.90) consistently outperformed the visual quality of images when compared to SR-RDN (PSNR = 32.68 dB/SSIM = 0.82). Similarly, UNet demonstrated superior performance over SegNet, boasting an average Intersection over Union (IoU) of 0.862, an average Dice coefficient of 0.991, and an accuracy rate of 0.947 in Region of Interest (RoI) segmentation results. In conclusion, the ResNet model contributed to enhanced accuracy compared to conventional machine learning algorithms. However, it did not surpass state-of-the-art deep CNN-based classifiers, achieving an accuracy rate of 75%.Abbreviations:AUC: Area under curve; CAD: computer aided system; CC: Cranio caudal; CNN: Convolutional neural network; DNN: Deep neural network; DDSM: Digital Database for Screening Mammography; DM: Digital mammography; DL: Deep learning; EDSR: Enhanced Deep Residual Network; E2E: End to End; ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks; ESPCN: Efficient sub-pixel convolutional neural network; GAN: Generative adversarial network; HR: High resolution; IoU: Intersection over Union; LR: Low resolution; MDSR: Multi-scale deep super-resolution; MLO: Mediolateral Oblique; PSNR: Peak signal to Noise Ratio; RoI: Region of interest; RDN: Residua Dense Network; RDB: Residual Dense Block; RNN: Recurrent Neural Network; ReLU: Rectified Linear Unit; SR-GAN: Super-Resolution Using a Generative Adversarial Network; SSIM: Structural Sim

关键词： Breast cancer classification convolutional neural network mammography images segmentation super resolution

Nanoscale 3D printing of glass photonic crystals with near-unity reflectance in the visible spectrum

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science advances 2025年第21期11卷 eadv0267页

作者： Wang Zhang Hongtao Wang Hao Wang Son Tung Ha Lei Chen Xue Liang Li Cheng-Feng Pan Bochang Wu Md Abdur Rahman Yujie Ke Qifeng Ruan Xiaolong Yang Thomas Christensen Joel K W Yang Engineering Product Development Singapore University of Technology and Design Singapore 487372 Singapore. Singapore-HUJ Alliance for Research and Enterprise (SHARE) The Smart Grippers for Soft Robotics (SGSR) Programme Campus for Research Excellence and Technological Enterprise (CREATE) Singapore 138602 Singapore. School of Instrumentation and Optoelectronic Engineering Beihang University Beijing 100191 China. Hangzhou International Innovation Institute Beihang University Hangzhou 311115 China. Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Singapore. College of Mechanical and Vehicle Engineering Hunan University Changsha 410082 China. Department of Electrical and Computer Engineering National University of Singapore (NUS) 4 Engineering Drive 3 Singapore 117576 Singapore. School of Interdisciplinary Studies Lingnan University Tuen Mun Hong Kong SAR China. Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology (Shenzhen) Shenzhen 518055 China. College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China. Department of Electrical and Photonics Engineering Technical University of Denmark Kongens Lyngby 2800 Denmark. POLIMA-Center for Polariton-driven Light-Matter Interactions University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark.

Glass is widely used as an optical material due to its high transparency, thermal stability, and mechanical properties. The ability to fabricate and sculpt glass at the nanoscale would naturally expand its application domain in nanophotonics. Here, we report an approach to print glass in three dimensions with nanoscale resolutions. We developed Glass-Nano, an organic-inorganic hybrid resin containing silicon elements. Using this high-resolution resin, three-dimensional (3D) photonic crystals (PhCs) were printed with two-photon lithography. After printing, the structures were heated to high temperatures in air to remove organic components and convert the remaining material into silica glass. 3D glass PhCs with periodicities as small as 260 nanometers were obtained after sintering at 650°C. The 3D glass PhCs exhibit ~100% reflectance in the visible range, surpassing the typical reflectances observed from similar structures in low-refractive index materials. The quality of PhCs achieved is observed in both electron microscopy and the excellent agreement with band structure calculations of idealized structures.

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