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arXiv 2022年

作者： Dai, X. Trappen, R. Chen, H. Melanson, D. Yurtalan, M.A. Tennant, D.M. Martinez, A.J. Tang, Y. Mozgunov, E. Gibson, J. Grover, J.A. Disseler, S.M. Basham, J.I. Novikov, S. Das, R. Melville, A.J. Niedzielski, B.M. Hirjibehedin, C.F. Serniak, K. Weber, S.J. Yoder, J.L. Oliver, W.D. Zick, K.M. Lidar, D.A. Lupascu, A. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Electrical & Computer Engineering University of Southern California Los AngelesCA90089 United States Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada University of Southern California - Information Sciences Institute ArlingtonVA22203 United States Northrop Grumman Corporation LinthicumMD21090 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States QuEra Computing Inc. 1284 Soldiers Field Road BostonMA02135 United States Atlantic Quantum Corp. CambridgeMA02139 United States Department of Chemistry University of Southern California Los AngelesCA90089 United States Department of Physics University of Southern California Los AngelesCA90089 United States Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics ETH Zurich ZurichCH-8093 Switzerland Department of Physics Harvard University CambridgeMA02138 United States Rigetti Computing BerkeleyCA94710 United States VancouverBCV6E 4B1 Canada

Landau-Zener tunneling, which describes the transition in a two-level system during a sweep through an anti-crossing, is a model applicable to a wide range of physical phenomena. Realistic quantum systems are affected by dissipation due to coupling to their environments. An important aspect of understanding such open quantum systems is the relative energy scales of the system itself and the system-environment coupling, which distinguishes the weak- and strong-coupling regimes. Using a tunable superconducting flux qubit, we observe the crossover from weak to strong coupling to the environment in Landau-Zener tunneling. Our results confirm previous theoretical studies of dissipative Landau-Zener tunneling in the weak and strong coupling limits. We devise a spin bath model that effectively captures the crossover regime. This work is relevant for understanding the role of dissipation in quantum annealing, where the system is expected to go through a cascade of Landau-Zener transitions before reaching the target state. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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Yield estimation in mango orchards using machine vision

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AIP Conference Proceedings 2021年第1期2336卷

作者： M. V. Neethi A. Geetha Kiran Harshvardhan Tiwari 1Department of Computer Science and Engineering Malnad College of Engineering Hassan. Karnataka India 2Center of Incubation Innovation Research and Consultancy (CIIRC) Jyothy Institute of Technology Bengaluru Karnataka India

The world is changing day by day through technical innovations. Due to urbanization, climatic changes, land degradation, water scarcity and also through industrial revolution, migration of people from rural areas to urban areas is drastically increasing, as a result, the availability of labor for agricultural practices are reducing. Agricultural production must be increased to supply the food required to the growing population. Precision farming finds a way to overcome these challenges by providing various economical techniques for effective utilization of land and available resources with less involvement of labor. This paper presents a simple and efficient machine vision mango fruit segmentation algorithm using image processing techniques. The proposed algorithm utilizes color features to segment the mango fruit. The dataset is collected for 5 months temporally from December 2018 to May 2019 right from the flowering stage to the maturation stage of the fruit under daylight condition. The proposed algorithm of mango fruit recognition is tested on the collected dataset. Dominant features are extracted from color features, to obtain more information on the image through different color space RGB image is converted into HSV color space and then de-noising methods are applied to remove the noise formed because of not recognizing the presence of leaves and branches properly. After de-noising the image thresholding method is applied to remove the background. Finally, fruit recognition is performed with the help of segmentation methods. The developed algorithm gives good results of fruit region segmentation.

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Joint image synthesis and fusion with converted features for Alzheimer's disease diagnosis

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Chen, Zhaodong Wang, Mingxia Nan, Fengtao Yang, Yun Li, Shunbao Zhou, Menghui Qi, Jun Wang, Hanwen Yang, Po National Pilot School of Software Yunnan University Kunming650500 China School of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom Northwest A&F University College of information engineering Northwest A&F University Xianyang712199 China Department of Computing School of Advanced Technology Xi'an JiaoTong-Liverpool University Suzhou215028 China

The effectiveness of complete multi-modal neuroimaging data in the diagnosis of Alzheimer's disease has been extensively demonstrated and applied. Dealing with incomplete modalities poses a common challenge in multi-modal neuroimaging diagnosis. The mainstream approaches aim to synthesize missing neuroimaging data in order to make full use of all available samples. However, these methods treat image synthesis and disease diagnosis as two independent tasks, overlooking the potential feature of cross-modality image synthesis for downstream tasks. To this end, we propose the Joint Image Synthesis and Classification Learning method to jointly optimize image synthesis and disease diagnosis using incomplete neuroimaging modalities. Our approach comprises a submodule for synthesizing missing neuroimaging data and a decision fusion submodule that integrates features from different modalities and the high-level/converted features generated during synthesis. Experimental results demonstrate that our joint optimization approach outperforms conventional two-stage methods. Our method is capable of handling arbitrary neuroimaging modality missing scenarios and achieves state-of-the-art performance in both Alzheimer's Disease identification and mild cognitive impairment conversion classification tasks. Finally, we further explored the importance of different converted features. This highlights the effectiveness of our approach in addressing the challenges of Alzheimer's Disease diagnosis and provides insights for future research in multi-modal medical image analysis. © 2025

关键词： Neurodegenerative diseases

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Quantum Mixed State Compiling

arXiv

引用

arXiv 2022年

作者： Ezzell, Nic Ball, Elliott M. Siddiqui, Aliza U. Wilde, Mark M. Sornborger, Andrew T. Coles, Patrick J. Holmes, Zoë Department of Physics & Astronomy University of Southern California Los AngelesCA90089 United States Information Sciences Los Alamos National Laboratory Los AlamosNM87545 United States Physics Department Lancaster University LancasterLA1 4YB United Kingdom Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Division of Computer Science and Engineering Louisiana State University Baton RougeLA70803 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States Quantum Science Center Oak RidgeTN37931 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States LausanneCH-1015 Switzerland

The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generalization of previous VQAs that aimed at learning preparation circuits for pure states. We consider two different ansätze for compiling the target state;the first is based on learning a purification of the state and the second on representing it as a convex combination of pure states. In both cases, the resources required to store and manipulate the compiled state grow with the rank of the approximation. Thus, by learning a lower rank approximation of the target state, our algorithm provides a means of compressing a state for more efficient processing. As a byproduct of our algorithm, one effectively learns the principal components of the target state, and hence our algorithm further provides a new method for principal component analysis. We investigate the efficacy of our algorithm through extensive numerical implementations, showing that typical random states and thermal states of many body systems may be learnt this way. Additionally, we demonstrate on quantum hardware how our algorithm can be used to study hardware noise-induced states. © 2022, CC BY.

关键词： Principal component analysis

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Prediction of formation energies of large-scale disordered systems via active-learning-based executions of ab initio local-energy calculations: A case study on a Fe random grain boundary model with millions of atoms

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Physical Review Materials 2020年第11期4卷 113602-113602页

作者： Tomoyuki Tamura Masayuki Karasuyama Department of Physical Science and Engineering Nagoya Institute of Technology Nagoya 466-8555 Japan Center for Materials Research by Information Integration National Institute for Materials Science (NIMS) Tsukuba 305-0047 Japan Department of Computer Science Nagoya Institute of Technology Nagoya 466-8555 Japan PRESTO Japan Science and Technological Agency 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan

We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is assumed that the atomic energy is represented by the linear regression of the atomic structural descriptor. The atomic energy is obtained through DFT calculations using a small cell extracted from a huge GB model, called replica DFT atomic energy. The uncertainty reduction (UR) approach in active learning is used to efficiently collect the training data for the atomic energy. In this approach, atomic energy is not required to search for candidate points; therefore, sequential DFT calculations are not required. This approach is suitable for massively parallel computers that can execute a large number of jobs simultaneously. In this study, we demonstrate the prediction of the atomic energy of a Fe random GB model containing one million atoms using the UR approach, and we show that the prediction error decreases more rapidly compared with random sampling. We conclude that the UR approach with replica DFT atomic energy is useful for modeling huge GBs and will be essential for modeling other structural defects.

关键词： Grain boundaries Structural properties Machine learning

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Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing

arXiv

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arXiv 2022年

作者： Xu, Wei Yang, Zhaohui Ng, Derrick Wing Kwan Levorato, Marco Eldar, Yonina C. Debbah, Mérouane The National Mobile Communications Research Lab Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The School of Electrical Engineering and Telecommunications The University of New South Wales Australia The Department of Computer Science University of California IrvineCA92697 United States The Faculty of Math and CS Weizmann Institute of Science Rehovot7610001 Israel The Technology Innovation Institute The Mohamed Bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi9639 United Arab Emirates

To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange. However, the current design of separating EL deployment and communication optimization does not yet reap the promised benefits of distributed signal processing, and sometimes suffers from excessive signalling overhead, long processing delay, and unstable learning convergence. In this paper, we provide an overview on practical distributed EL techniques and their interplay with advanced communication optimization designs. In particular, typical performance metrics for dual-functional learning and communication networks are discussed. Also, recent achievements of enabling techniques for the dual-functional design are surveyed with exemplifications from the mutual perspectives of "communications for learning" and "learning for communications." The application of EL techniques within a variety of future communication systems are also envisioned for beyond 5G (B5G) wireless networks. For the application in goal-oriented semantic communication, we present a first mathematical model of the goal-oriented source entropy as an optimization problem. In addition, from the viewpoint of information theory, we identify fundamental open problems of characterizing rate regions for communication networks supporting distributed learning-and-computing tasks. We also present technical challenges as well as emerging application opportunities in this field, with the aim of inspiring future research and promoting widespread developments of EL in B5G. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic data interchange

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Roadmap on Nonlocality in Photonic Materials and Metamaterials

arXiv

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arXiv 2025年

作者： Monticone, Francesco Mortensen, N. Asger Fernández-Domínguez, Antonio I. Luo, Yu Tserkezis, Christos Khurgin, Jacob B. Shahbazyan, Tigran V. Chaves, André J. Peres, Nuno M.R. Wegner, Gino Busch, Kurt Hu, Huatian della Sala, Fabio Zhang, Pu Ciracì, Cristian Aizpurua, Javier Babaze, Antton Borisov, Andrei G. Chen, Xue-Wen Christensen, Thomas Yan, Wei Yang, Yi Hohenester, Ulrich Huber, Lorenz Wubs, Martijn de Liberato, Simone Gonçalves, P.A.D. de Abajo, F. Javier García Hess, Ortwin Tarasenko, Illya Cox, Joel D. Jelver, Line Dias, Eduardo J.C. Sánchez, Miguel Sánchez Margetis, Dionisios Gómez-Santos, Guillermo Stauber, Tobias Tretyakov, Sergei Simovski, Constantin Pakniyat, Samaneh Gómez-Díaz, J. Sebastián Bondarev, Igor V. Biehs, Svend-Age Boltasseva, Alexandra Shalaev, Vladimir M. Krasavin, Alexey V. Zayats, Anatoly V. Alù, Andrea Song, Jung-Hwan Brongersma, Mark L. Levy, Uriel Long, Olivia Y. Guo, Cheng Fan, Shanhui Bozhevolnyi, Sergey I. Overvig, Adam Prudêncio, Filipa R. Silveirinha, Mário G. Gangaraj, S. Ali Hassani Argyropoulos, Christos Huidobro, Paloma A. Galiffi, Emanuele Yang, Fan Pendry, John B. Miller, David A.B. School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States POLIMA Center for Polariton-driven Light–Matter Interactions University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid MadridE-28049 Spain Universidad Autónoma de Madrid MadridE-28049 Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Physics Jackson State University JacksonMS39217 United States Department of Physics Aeronautics Institute of Technology SP So Jos dos Campos12228-900 Brazil Departamento de Física Universidade do Minho BragaP-4710-057 Portugal Av Mestre José Veiga Braga4715-330 Portugal Max-Born-Institut Berlin12489 Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik Berlin12489 Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 Arnesano73010 Italy Via Monteroni Campus Unisalento Lecce73100 Italy School of physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Donostia International Physics Center DIPC Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Electricity and Electronics University of the Basque Country Leioa48940 Spain Department of Applied Physics University of the Basque Country Donostia20018 Spain Université Paris-Saclay CNRS Institut des Scienc

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces–can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward–toward new scientific discoveries and technological advancements. Copyright © 2025, The Authors. All rights reserved.

关键词： Nanocrystals

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Learning management system empowered by machine learning

引用

AIP Conference Proceedings 2022年第1期2393卷

作者： Md. Alimul Haque Deepa Sonal Shameemul Haque Moidur Rahman Kailash Kumar 1Department of Computer Science V. K. S. University Ara India 2Department of Computer Science Patna Women's College Patna India 3Al-Hafeez College Ara India 4College of Computer Science and Information Technology Jazan University Jizan Kingdom of Saudi Arabia 5College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia

Using a Learning Management System to generate, share, monitor, and maintain various kinds of education and creation content has proven a milestone in improving online learning. Since the first Learning Management System (LMS) emerged, significant technical developments made this platform a vital technology for curriculum management, rich content management, appraisal and assessment, and complex cooperation. The future expects several improvements in its development, processes, and execution, with many emerging fields of the study exploring different innovations relevant to the LMS. Online learning has been on the increase globally due to the rapid growth in technology in education. The 2019 Coronavirus disease (COVID-19) pandemic has implemented online classes for students in all colleges and universities. However, students are unknown about their ability to consider online learning. This paper presents how machine learning could support the Learning Management System's controlling to resolve student's feedback and queries associated with technical issues and problems when they have engaged in online classes. In this paper, a web-based online survey is conducted. Two hundred fifteen (215) students who have enrolled in various streams of Ph.D., Master, and Bachelor level programs in Colleges and University of Asian countries participated in this survey. Based on students' feedback, results are analyzed. A detailed analysis of possible solutions or suggestions is stated and proposed a Machine Learning-based LMS Model to handle students' problems and challenges efficiently during the online classes.

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Beamspace Precoding and Beam Selection for Wideband Millimeter-Wave Mimo Relying On Lens Antenna Arrays

arXiv

引用

arXiv 2020年

作者： Shen, Wenqian Bu, Xiangyuan Gao, Xinyu Xing, Chengwen Hanzo, Lajos School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Millimeter-wave (mmWave) multiple-input multiple-out (MIMO) systems relying on lens antenna arrays are capable of achieving a high antenna-gain at a considerably reduced number of radio frequency (RF) chains via beam selection. However, the traditional beam selection network suffers from significant performance loss in wideband systems due to the effect of beam squint. In this paper, we propose a phase shifter-aided beam selection network, which enables a single RF chain to support multiple focused-energy beams, for mitigating the beam squint in wideband mmWave MIMO systems. Based on this architecture, we additionally design an efficient transmit precoder (TPC) for maximizing the achievable sum-rate, which is composed of beam selection and beamspace precoding. Specifically, we decouple the design problems of beamspace precoding and beam selection by exploiting the fact that the beam selection matrix has a limited number of candidates. For the beamspace precoding design, we propose a successive interference cancellation (SIC)-based method, which decomposes the associated optimization problem into a series of subproblems and solves them successively. For the beam selection design, we propose an energy-max beam selection method for avoiding the high complexity of exhaustive search, and derive the number of required beams for striking an attractive trade-off between the hardware cost and system performance. Our simulation results show that the proposed beamspace precoding and beam selection methods achieve both a higher sum-rate and a higher energy efficiency than its conventional counterparts. Copyright © 2020, The Authors. All rights reserved.

关键词： Antenna arrays

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