检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Chen, Guorui Jiang, Lili Wu, Shuang Lv, Bosai Li, Hongyuan Chittari, Bheema Lingam Watanabe, Kenji Takashi Taniguchi Shi, Zhiwen Jung, Jeil Zhang, Yuanbo Wang, Feng Department of Physics University of California at Berkeley BerkeleyCA United States State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA United States Key Laboratory of Artificial Structures and Quantum Control Ministry of Education School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Collaborative Innovation Center of Advanced Microstructures Nanjing China National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Physics University of Seoul Seoul02504 Korea Republic of Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Kavli Energy NanoSciences Institute University of California Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States

Mott insulator plays a central role in strongly correlated physics, where the repulsive Coulomb interaction dominates over the electron kinetic energy and leads to insulating states with one electron occupying each unit cell.1,2 Doped Mott insulator is often described by the Hubbard model3, which can give rise to other correlated phenomena such as unusual magnetism and even high temperature superconductivity2,4. A tunable Mott insulator, where the competition between the Coulomb interaction and the kinetic energy can be varied in situ, can provide an invaluable model system for the study of Mott physics. Here we report the realization of such a tunable Mott insulator in the ABC trilayer graphene (TLG) and hexagonal boron nitride (hBN) heterostructure with a moiré superlattice. Unlike massless Dirac electrons in monolayer graphene5,6, electrons in pristine ABC TLG are characterized by quartic energy dispersion and large effective mass7-11 that are conducive for strongly correlated phenomena. The moiré superlattice12-18 in TLG/hBN heterostructures leads to narrow electronic minibands that are gatetunable. Each filled miniband contains 4 electrons in one moiré lattice site due to the spin and valley degeneracy of graphene. The Mott insulator states emerge at 1/4 and 1/2 fillings, corresponding to one electron and two electrons per site, respectively. Moreover, the Mott states in the ABC TLG/hBN heterostructure exhibit unprecedented tunability: The Mott gap can be modulated in situ by a vertical electrical field, and at the mean time the electron doping can be gate-tuned to fill the band from one Mott insulating state to another. Our observation of a tunable Mott insulator opens up exciting opportunities to explore novel strongly correlated phenomena in two-dimensional moiré superlattice heterostructures. Copyright © 2018, The Authors. All rights reserved.

关键词： Mott insulators

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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Research Square

引用

Research Square 2021年

作者： Chu, Yanyi Zhang, Yan Wang, Qiankun Zhang, Lingfeng Wang, Xuhong Wang, Yanjing Wang, Jianmin Jiang, Xue Salahub, Dennis Russell Xiong, Yi Wei, Dong-Qing State Key Laboratory of Microbial Metabolism Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200030 China Department of Chemistry CMS - Centre for Molecular Simulation IQST - Institute for Quantum Science and Technology University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Clinical Oncology The University of Hong Kong-Shenzhen Hospital Shenzhen China School of Electrical Engineering and Computer Science University of Ottawa 75 Laurier Ave. OttawaONK1N 6N5 Canada The Ministry of Education Key Laboratory of System control and information processing Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Peng Cheng Laboratory Vanke Cloud City Phase I Building 8 Xili Street Nanshan District Guangdong Shenzhen518055 China

Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide. © 2021, CC BY.

关键词： Peptides

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

引用

arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Topological-darkness-assisted phase regulation for atomically thin meta-optics

arXiv

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arXiv 2019年

作者： Wang, Yingwei Deng, Zi-Lan Hu, Dejiao Yuan, Jian Ou, Qingdong Qin, Fei Zhang, Yinan Ouyang, Xu Peng, Bo Cao, Yaoyu Guan, Bai-Ou Zhang, Yupeng He, Jun Qiu, Chengwei Bao, Qiaoliang Li, Xiangping Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics Technology Jinan University Guangzhou510632 China Hunan Key Laboratory for Super-microstructure and Ultrafast Process School of Physics and Electronics Central South University 932 South Lushan Road Changsha Hunan410083 China College of Physics and Electronic Information Huaibei Normal University Huaibei235000 China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Electronic Science and Technology Shenzhen University Shenzhen518060 China Monash University ClaytonVIC3800 Australia National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Films and Integrated Devices School of Microelectronics and Solid State Electronics University of Electronic Science and Technology of China Chengdu610054 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore

Two-dimensional (2D) noble-metal dichalcogenides 1, 2 have emerged as a new platform for the realization of versatile flat optics with a considerable degree of miniaturization. However, light field manipulation at the atomic scale is widely considered unattainable since the vanishing thickness and intrinsic losses of 2D materials completely suppress both resonances and phase accumulation effects. Empowered by conventionally perceived adverse effects of intrinsic losses, we show that the structured PtSe2 films integrated with a uniform substrate can regulate nontrivial singular phase and realize atomic-thick meta-optics in the presence of topological darkness 3, 4. We experimentally demonstrate a series of atomic-thick binary meta-optics that allows angle-robust and high unit-thickness diffraction efficiency of 0.96%/nm in visible frequencies, given its thickness of merely 4.3 nm. Our results unlock the potential of a new class of 2D flat optics for light field manipulation at an atomic thickness. Copyright © 2019, The Authors. All rights reserved.

关键词： Platinum compounds

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Transient Electronics: Materials and Wireless Microfluidic Systems for Electronics Capable of Chemical Dissolution on Demand (Adv. Funct. Mater. 9/2015)

引用

Advanced Functional Materials 2015年第9期25卷

作者： Chi Hwan Lee Jae‐Woong Jeong Yuhao Liu Yihui Zhang Yan Shi Seung‐Kyun Kang Jeonghyun Kim Jae Soon Kim Na Yeon Lee Bong Hoon Kim Kyung‐In Jang Lan Yin Min Ku Kim Anthony Banks Ungyu Paik Yonggang Huang John A. Rogers Department of Materials Science and Engineering Beckman Institute for Advanced Science and Technologyand Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana IL 61801 USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Civil and Environmental Engineering and Mechanical Engineering Northwestern University Evanston IL 60208 USA Center for Mechanics and Materials Tsinghua University Beijing 100084 China State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Department of Materials Science and Engineering Department of Energy Engineering Hanyang University Seoul 133‐791 Republic of Korea Department of Chemistry University of Illinois at Urbana‐Champaign Urbana IL 61801 USA Department of Bioengineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA Department of Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

引用

arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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Author Correction: Ultra-thin high-efficiency mid-infraredtransmissive Huygens meta-optics

引用

Nature communications 2018年第1期9卷 2399页

作者： Li Zhang Jun Ding Hanyu Zheng Sensong An Hongtao Lin Bowen Zheng Qingyang Du Gufan Yin Jerome Michon Yifei Zhang Zhuoran Fang Mikhail Y Shalaginov Longjiang Deng Tian Gu Hualiang Zhang Juejun Hu State Key Laboratory of Electronic Thin Films and Integrated Devices National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of China Chengdu Sichuan 611731 China. Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA. School of Information and Science Technology East China Normal University Shanghai 200062 China. Department of Electrical & Computer Engineering University of Massachusetts Lowell Lowell MA 01854 USA. Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA. gutian@mit.edu. Department of Electrical & Computer Engineering University of Massachusetts Lowell Lowell MA 01854 USA. hualiang_zhang@uml.edu. Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA. hujuejun@mit.edu.

The original version of this Article omitted the following from the Acknowledgements:'J.D. and H. Zhang acknowledge initial funding for design of the meta-atoms provided by the National science Foundation under award CMMI-1266251. Z.L. and H. Zheng contributed to the Device Fabrication section and were independently funded as visiting scholars by the National Natural science Foundation of China under award 51772042 and the "111" project (No. B13042) led by Professor Huaiwu Zhang. Later work contained within the Device Modeling and Device Characterization sections and some revisions to the manuscript were funded under Defense Advanced Research Projects Agency Defense sciences Office (DSO) Program: EXTREME Optics and Imaging (EXTREME) under Agreement No. HR00111720029. The authors also acknowledge fabrication facility support by the Harvard University Center for Nanoscale Systems funded by the National science Foundation under award 0335765. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the department of Defense or the U.S. Government.' This has been corrected in both the PDF and HTML versions of the Article.

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Traceable random numbers from a nonlocal quantum advantage

arXiv

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arXiv 2024年

作者： Kavuri, Gautam A. Palfree, Jasper Reddy, Dileep V. Zhang, Yanbao Bienfang, Joshua C. Mazurek, Michael D. Alhejji, Mohammad A. Siddiqui, Aliza U. Cavanagh, Joseph M. Dalal, Aagam Abellán, Carlos Amaya, Waldimar Mitchell, Morgan W. Stange, Katherine E. Beale, Paul D. Brandão, Luís T.A.N. Booth, Harold Peralta, René Nam, Sae Woo Mirin, Richard P. Stevens, Martin J. Knill, Emanuel Shalm, Lynden K. Department of Physics University of Colorado BoulderCO80309 United States The National Institute of Standards and Technology BoulderCO80305 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Joint Quantum Institute National Institute of Standards and Technology University of Maryland 100 Bureau Drive GaithersburgMD20899 United States Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Quside Technologies S.L. Castelldefels Barcelona Spain ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain ICREA - Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain Department of Mathematics University of Colorado BoulderCO80309 United States GaithersburgMD20899 United States Information Technology Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Physical Measurement Laboratory National Institute of Standards and Technology BoulderCO80305 United States Center for Theory of Quantum Matter University of Colorado BoulderCO80305 United States Applied and Computational Mathematics Division National Institute of Standards and Technology BoulderCO80305 United States Quantum Engineering Initiative Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States

The unpredictability of random numbers is fundamental to both digital security [1, 2] and applications that fairly distribute resources [3, 4]. However, existing random number generators have limitations—the generation processes cannot be fully traced, audited, and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators [5] are auditable, but they cannot guarantee that their outputs were a priori unpredictable given knowledge of the initial seed. Device-independent quantum random number generators [6–9] can ensure that the source of randomness was unknown beforehand, but the steps used to extract the randomness are vulnerable to tampering. Here, for the first time, we demonstrate a fully traceable random number generation protocol based on device-independent techniques. Our protocol extracts randomness from unpredictable non-local quantum correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol is at the heart of a public traceable and certifiable quantum randomness beacon that we have launched [10]. Over the first 40 days of operation, we completed the protocol 7434 out of 7454 attempts—a success rate of 99.7%. Each time the protocol succeeded, the beacon emitted a pulse of 512 bits of traceable randomness. The bits are certified to be uniform with error times actual success probability bounded by 2−64. The generation of certifiable and traceable randomness represents one of the first public services that operates with an entanglement-derived advantage over comparable classical approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Random number generation

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A Critical Review of MANET Testbed Using Mobile Robot Technology

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Journal of Physics: Conference Series 2018年第1期1019卷

作者： Farkhana Muchtar Abdul Hanan Abdullah Marina Md Arshad Mohd Helmy Abd Wahab Siti Nor Zawani Ahmmad Gaddafi Abdul-Salaam Faculty of Computing Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia School of Computer Sciences Universiti Sains Malaysia 11800 Gelugor Pulau Pinang Malaysia Faculty of Electronic and Electrical Engineering Universiti Tun Hussein Onn 86400 Batu Pahat Johor Malaysia Instrumental and Control Engineering University Kuala Lumpur MITEC Persiaran Sinaran Ilmu Bandar Seri Alam 81750 Johor Malaysia Department of Computer Science Kwame Nkrumah University of Science and Technology Ghana

This paper is a continuation of our previous paper under the same topic, MANET testbed using mobile robot technology. In our previous paper, we studied the topic by scrutinizing all the technical aspects and presented it as a technical review. However in this paper, we study the topic and presents it as a critical review that dwells into four aspect, namely (i) purpose, accessibility and scope of testbed facilities, (ii) usability and controllability of robot mobility in t estbed facilities, (iii) repeatability and reproducibility of real mobility in t estbeds, and (iv) tools for MANET implementation, deployment and debugging for experiments. With the wealth of information on the topic provided in this paper, the content of this paper is expected to be a source of reference for MANET researchers who are at a crossroad when selecting the preferred mobile robot technology and approach to suit their own specific needs.

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Adaptive observer for nonlinear fractional-order systems

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International Journal of Adaptive control and Signal Processing 2016年第3期31卷

作者： Ibrahima N'doye Taous-Meriem Laleg-Kirati Mohamed Darouach Holger Voos Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Research Center for Automatic Control of Nancy (CRAN UMR 7039 CNRS) University of Lorraine IUT de Longwy 186 rue de Lorraine 54400 Cosnes et Romain France Faculty of Science Technology and Communication (FSTC) University of Luxembourg 6 rue Richard Coudenhove-Kalergi L-1359 Luxembourg

In this paper, an adaptive observer is proposed for the joint estimation of states and parameters of a fractional nonlinear system with external perturbations. The convergence of the proposed observer is derived in terms of linear matrix inequalities (LMIs) by using an indirect Lyapunov *** proposed adaptive observer is also robust against Lipschitz additive nonlinear uncertainty. The performance of the observer is illustrated through some examples including the chaotic Lorenz and Lü's systems. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： adaptive observer design fractional-order nonlinear system indirect Lyapunov approach linear matrix inequality (LMI) chaotic Lorenz system chaotic Lü's system

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