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arXiv 2021年

作者： Bai, Ge Wu, Ya-Dong Zhu, Yan Hayashi, Masahito Chiribella, Giulio QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong HKU-Oxford Joint Laboratory for Quantum Information and Computation United Kingdom Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan Department of Computer Science University of Oxford Parks Road OxfordOX1 3QD United Kingdom Perimeter Institute For Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada

Complex processes often arise from sequences of simpler interactions involving a few particles at a time. These interactions, however, may not be directly accessible to experiments. Here we develop the first efficient method for unravelling the causal structure of the interactions in a multipartite quantum process, under the assumption that the process has bounded information loss and induces causal dependencies whose strength is above a fixed (but otherwise arbitrary) threshold. Our method is based on a quantum algorithm whose complexity scales polynomially in the total number of input/output systems, in the dimension of the systems involved in each interaction, and in the inverse of the chosen threshold for the strength of the causal dependencies. Under additional assumptions, we also provide a second algorithm that has lower complexity and requires only local state preparation and local measurements. Our algorithms can be used to identify processes that can be characterized efficiently with the technique of quantum process tomography. Similarly, they can be used to identify useful communication channels in quantum networks, and to test the internal structure of uncharacterized quantum circuits. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational complexity

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UAV-Relay Assisted RSMA Fluid Antenna System: Outage Probability Analysis

arXiv

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arXiv 2025年

作者： Ghadi, Farshad Rostami Kaveh, Masoud Hernando-Gallego, Francisco Martín, Diego Wong, Kai-Kit Chae, Chan-Byoung University of Granada Granada18071 Spain Department of Information and Communication Engineering Aalto University Espoo Finland Department of Computer Science Escuela de Ingeniería Informática de Segovia Universidad de Valladolid Segovia40005 Spain Department of Electronic and Electrical Engineering University College London Torrington Place WC1E 7JE United Kingdom Yonsei Frontier Lab Yonsei University Seoul Korea Republic of School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

This letter studies the impact of fluid antenna system (FAS) technology on the performance of unmanned aerial vehicle (UAV)-assisted multiuser communication networks. Specifically, we consider a scenario where a fixed-position antenna (FPA) base station (BS) serves K FAS-equipped users with the assistance of a UAV acting as an aerial relay. The BS employs rate-splitting multiple access (RSMA), while the UAV operates in half-duplex (HD) mode using the decode-and-forward (DF) strategy. For this system, we derive a compact analytical expression for the outage probability (OP) and its asymptotic behavior in the high signal-to-noise ratio (SNR) regime, leveraging the multivariate tdistribution. Our results show how deploying FAS at ground users (GUs) in UAV-aided communications improves overall system performance compared to using FPA GUs. Copyright © 2025, The Authors. All rights reserved.

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Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

arXiv

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arXiv 2023年

作者： Wang, Meng Lin, Tian Wang, Lianyu Lin, Aidi Zou, Ke Xu, Xinxing Zhou, Yi Peng, Yuanyuan Meng, Qingquan Qian, Yiming Deng, Guoyao Wu, Zhiqun Chen, Junhong Lin, Jianhong Zhang, Mingzhi Zhu, Weifang Zhang, Changqing Zhang, Daoqiang Goh, Rick Siow Mong Liu, Yong Pang, Chi Pui Chen, Xinjian Chen, Haoyu Fu, Huazhu 1 Fusionopolis Way #16-16 Connexis Singapore138632 Singapore Joint Shantou International Eye Center Shantou University the Chinese University of Hong Kong Medical College Shantou University Guangdong515041 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu211100 China National Key Laboratory of Fundamental Science on Synthetic Vision the College of Computer Science Sichuan University Sichuan610065 China School of Electronics and Information Engineering Soochow University Jiangsu215006 China School of Biomedical Engineering Anhui Medical University Anhui230032 China Longchuan People’s Hospital Heyuan China Puning People’s Hospital Jieyang China Haifeng PengPai Memory Hospital Shanwei China College of Intelligence and Computing Tianjin University Tianjin300350 China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong State Key Laboratory of Radiation Medicine and Protection Soochow University Suzhou215006 China

Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We established an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculated an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies. © 2023, CC BY.

关键词： Uncertainty analysis

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A low cost microscope prototype

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AIP Conference Proceedings 2021年第1期2372卷

作者： Rana I. Mahmood Khalil I. Mahmoud Ataa Ghazi 1Department of Biomedical Engineering College of Engineering Al-Nahrain University Baghdad Iraq 2Media Technology Engineering College of Engineering University of Information Technology and Communications Baghdad Iraq 3P.O.P Department College of Dentistry Mustansiriya University Baghdad Iraq

One of the most widely used biological and medical instruments are the microscopes. Many new modalities have been developed by constant advancement in the field of microscopy, but their relative sizes and their complexity, and their costs often hinder the usefulness of these instruments in the wider general community and many field environments. In this research, we used a 3D printer and the smartphone camera to design and construct a microscopic prototype to create a relatively low-cost, solid structure and to get pictures that are economically viable and are necessary for recording, analysis, education, and publication to acquire and distribute digital photomicrographs. The two-dimensional program, computer-aided design (Auto CAD) and the three-dimensional program (3D MAX) for 3D printed parts have been used to model and print the necessary components for the microscope prototype. The optical elements include a smartphone camera, an eyepiece, and an objective lens. The use of a traditional eyepiece facilitates device two-way compatibility of smartphones and software with a conventional microscope. The prototype microscope examined several specimens of animal tissue such as skin, follicle hair, and connective tissue. The photos were really accurate, clear, and magnified enough to see the tiny details of the biological cells and tissues. The resulting magnification was comparable to 10x of conventional microscope.

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Game-Theoretic Incentive Mechanism for Blockchain-Based Federated Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Tang, Wenzheng Liu, Erwu Ni, Wei Qu, Xinyu Huang, Butian Li, Kezhi Niyato, Dusit Jamalipour, Abbas Tongji University College of Electronics and Information Engineering Shanghai201804 China Tongji University Department of Ophthalmology Tongji Hospital School of Medicine China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia Hangzhou Yunphant Network Technology Co. Ltd. Hangzhou China University College London Institute of Health Informatics LondonWC1E 6BT United Kingdom College of Computing and Data Science 639798 Singapore University of Sydney School of Electrical and Computer Engineering NSW2006 Australia

Blockchain-based federated learning (BFL) has gained attention for its potential to establish decentralized trust. While existing research primarily focuses on personalized frameworks for various applications, essential aspects including incentive mechanisms—critical for ensuring stable system operation—remain under-explored. To bridge this gap, we propose a game-theoretic incentive mechanism designed to foster active participation in BFL tasks. Specifically, we model a BFL system comprising a model owner (MO), i.e., task publisher, multiple miners, and training terminals, framing their interactions through two-tier Stackelberg games. In the first-tier game, the MO designs reward strategies to incentivize training terminals to contribute more data, enhancing model accuracy. The second-tier game introduces a multi-leader multi-follower Stackelberg game, enabling miners to set model packaging prices based on competitors' strategies and anticipated user behavior. By deriving the Stackelberg equilibrium, we identify optimal strategies for all participants, leading to an incentive mechanism balancing individual interests with overall performance. Compared to its benchmarks, our incentive mechanism offers 5.8% and 53.4% higher utilities in the two games compared to its alternatives, accelerating convergence and improving accuracy. © 2002-2012 IEEE.

关键词： Federated learning

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Dissipative Landau-Zener tunneling in the crossover regime from weak to strong environment coupling

arXiv

引用

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

引用

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

引用

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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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

引用

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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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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