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arXiv 2021年

作者： Amakasu, Takashi Chauvet, Nicolas Bachelier, Guillaume Huant, Serge Horisaki, Ryoichi Naruse, Makoto Department of Mathematical Engineering and Information Physics Faculty of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Université Grenoble Alpes CNRS Institut Néel Grenoble38042 France

In recent cross-disciplinary studies involving both optics and computing, single-photon-based decision-making has been demonstrated by utilizing the wave-particle duality of light to solve multi-armed bandit problems. Furthermore, entangled-photon-based decision-making has managed to solve a competitive multi-armed bandit problem in such a way that conflicts of decisions among players are avoided while ensuring equality. However, as these studies are based on the polarization of light, the number of available choices is limited to two, corresponding to two orthogonal polarization states. Here we propose a scalable principle to solve competitive decision-making situations by using the orbital angular momentum of photons based on its high dimensionality, which theoretically allows an unlimited number of arms. Moreover, by extending the Hong-Ou-Mandel effect to more than two states, we theoretically establish an experimental configuration able to generate multi-photon states with orbital angular momentum and conditions that provide conflict-free selections at every turn. We numerically examine total rewards regarding three-armed bandit problems, for which the proposed strategy accomplishes almost the theoretical maximum, which is greater than a conventional mixed strategy intending to realize Nash equilibrium. This is thanks to the quantum interference effect that achieves no-conflict selections, even in the exploring phase to find the best arms. © 2021, CC BY.

关键词： Photons

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Super-resolving Herschel imaging: A proof of concept using Deep Neural Networks

arXiv

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arXiv 2021年

作者： Lauritsen, Lynge Dickinson, Hugh Bromley, Jane Serjeant, Stephen Lim, Chen-Fatt Gao, Zhen-Kai Wang, Wei-Hao School of Physical Sciences Faculty of Science Technology Engineering & Mathematics The Open University Walton Hall Kents Hill Milton KeynesMK7 6AA United Kingdom School of Computing & Communications Faculty of Science Technology Engineering & Mathematics The Open University Walton Hall Kents Hill Milton KeynesMK7 6AA United Kingdom Graduate Institute of Astrophysics National Taiwan University Taipei10617 Taiwan No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Graduate Institute of Astronomy National Central University Taoyuan32001 Taiwan

Wide-field sub-millimetre surveys have driven many major advances in galaxy evolution in the past decade, but without extensive follow-up observations the coarse angular resolution of these surveys limits the science exploitation. This has driven the development of various analytical deconvolution methods. In the last half a decade Generative Adversarial Networks have been used to attempt deconvolutions on optical data. Here we present an autoencoder with a novel loss function to overcome this problem in the sub-millimeter wavelength range. This approach is successfully demonstrated on Herschel SPIRE 500 µm COSMOS data, with the super-resolving target being the JCMT SCUBA-2 450 µm observations of the same field. We reproduce the JCMT SCUBA-2 images with high fidelity using this autoencoder. This is quantified through the point source fluxes and positions, the completeness and the purity. Copyright © 2021, The Authors. All rights reserved.

关键词： Data handling

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On a gap in the proof of the generalised quantum Stein's lemma and its consequences for the reversibility of quantum resources

arXiv

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arXiv 2022年

作者： Berta, Mario Brandão, Fernando G.S.L. Gour, Gilad Lami, Ludovico Plenio, Martin B. Regula, Bartosz Tomamichel, Marco Institute for Quantum Information RWTH Aachen University Aachen Germany Department of Computing Imperial College London London United Kingdom Institute for Quantum Information and Matter California Institute of Technology PasadenaCA United States AWS Center for Quantum Computing PasadenaCA United States Department of Mathematics and Statistics Institute for Quantum Science and Technology University of Calgary ABT2N 1N4 Canada Institut für Theoretische Physik und IQST Universität Ulm Albert-Einstein-Allee 11 UlmD-89069 Germany QuSoft Science Park 123 Amsterdam1098 XG Netherlands Korteweg–de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands Institute for Theoretical Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Saitama Wako351-0198 Japan Department of Physics Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Center for Quantum Technologies National University of Singapore Singapore Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore

We show that the proof of the generalised quantum Stein’s lemma [Brandão & Plenio, Commun. Math. Phys. 295, 791 (2010)] is not correct due to a gap in the argument leading to Lemma III.9. Hence, the main achievability result of Brandão & Plenio is not known to hold. This puts into question a number of established results in the literature, in particular the reversibility of quantum entanglement [Brandão & Plenio, Commun. Math. Phys. 295, 829 (2010);Nat. Phys. 4, 873 (2008)] and of general quantum resources [Brandão & Gour, Phys. Rev. Lett. 115, 070503 (2015)] under asymptotically resource non-generating operations. We discuss potential ways to recover variants of the newly unsettled results using other approaches. © 2022, CC BY.

关键词： Quantum entanglement

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Tensor-based quantum phase difference estimation for large-scale demonstration

arXiv

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arXiv 2024年

作者： Kanno, Shu Sugisaki, Kenji Nakamura, Hajime Yamauchi, Hiroshi Sakuma, Rei Kobayashi, Takao Gao, Qi Yamamoto, Naoki Mitsubishi Chemical Corporation Science & Innovation Center Kanagawa Yokohama227-8502 Japan Graduate School of Science and Technology Keio University 7-1 Shinkawasaki Saiwai-ku Kawasaki Kanagawa212-0032 Japan Centre for Quantum Engineering Research and Education TCG Centres for Research and Education in Science and Technology Sector V Salt Lake Kolkata700091 India Keio University 2-15-45 Mita Minato-ku Tokyo108-8345 Japan SoftBank Corp. Research Institute of Advanced Technology Tokyo105-7529 Japan Materials Informatics Initiative RD Technology & Digital Transformation Center JSR Corporation 3-103-9 Tonomachi Kawasaki-ku Kawasaki Kanagawa210-0821 Japan Department of Applied Physics and Physico-Informatics Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan

We develop an energy calculation algorithm leveraging quantum phase difference estimation (QPDE) scheme and a tensor-network-based unitary compression method in the preparation of superposition states and time-evolution gates. Alongside its efficient implementation, this algorithm reduces depolarization noise affections exponentially. We demonstrated energy gap calculations for one-dimensional Hubbard models on IBM superconducting devices using circuits up to 32-system (plus one-ancilla) qubits, a five-fold increase over previous QPE demonstrations, at the 7242 controlled-Z gate level of standard transpilation, utilizing a Q-CTRL error suppression module. Additionally, we propose a technique towards molecular executions using spatial orbital localization and index sorting, verified by a 13- (17-)qubit hexatriene (octatetraene) simulation. Since QPDE can handle the same objectives as QPE, our algorithm represents a leap forward in quantum computing on real devices. © 2024, CC BY-NC-SA.

关键词： Energy gap

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BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting 23rd

BCData: A Large-Scale Dataset and Benchmark for Cell Detecti...

引用

23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Huang, Zhongyi Ding, Yao Song, Guoli Wang, Lin Geng, Ruizhe He, Hongliang Du, Shan Liu, Xia Tian, Yonghong Liang, Yongsheng Zhou, S. Kevin Chen, Jie School of Electronic and Computer Engineering Peking University Shenzhen China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Shenzhen Hospital University of Chinese Academy of Sciences Shenzhen China The Second Peoples Hospital of Shenzhen Shenzhen China School of Electronics Engineering and Computer Science Peking University Beijing China Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030597214

Breast cancer is a main malignant tumor for women and the incidence is trending to ascend. Detecting positive and negative tumor cells in the immunohistochemically stained sections of breast tissue to compute the Ki-67 index is an essential means to determine the degree of malignancy of breast cancer. However, there are scarcely public datasets about cell detection of Ki-67 stained images. In this paper, we introduce a large-scale Breast tumor Cell Dataset (BCData) for cell detection and counting, which contains 1,338 images with 181,074 annotated cells belonging to two categories, i.e., positive and negative tumor cells. (We state that our dataset can only be used for non-commercial research.) Our dataset varies widely in both the distributing density of tumor cells and the Ki-67 index. We conduct several cell detection and counting methods on this dataset to set the first benchmark. We believe that our dataset will facilitate further research in cell detection and counting fields in clustering, overlapping, and variational stained conditions. Our dataset is available at https://***/view/bcdataset © 2020, Springer Nature Switzerland AG.

关键词： Cells

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Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site

Optimal Feature Identification for Machine Prediction of Win...

引用

Computational Intelligence (ICCI),International Conference on

作者： Muhammad Umair Manzoor Ahmed Hashmani Horio Keiichi High-Performance Cloud Computing Center (HPC3) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9781728184081

ISBN: (纸本)9781728184104

The hike in fossil-fuel prices and increasing environmental damage due to the subsequent Carbon Monoxide (CO) emission from burning fossil-fuel is becoming a major concern for every nation. The possibility of generating power from natural sources such as solar, wind, and sea waves is thus considered as an alternative. In the case of the sea waves, the kinetic energy of surface waves can be converted into single direction motion which runs a turbine to generate electricity. A Wave Energy Converter (WEC) is such an installation that converts the wave energy into electrical energy. In this study, we have conducted a literature investigation to identify the significant meteorological and wind-wave data parameters which determine wave-energy potential at a wave energy converter site and then identified optimal feature sets from buoy data for machine prediction of those identified parameters. The authors hope that by suggesting optimal feature sets, the outcomes of this study will help in improving the computational efficiency of machine learning models specially designed for wave parameter prediction at WEC sites.

关键词： Numerical models Computational modeling Predictive models Machine learning Meteorology Mathematical model Ocean temperature

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Modelling and exploiting taxonomic knowledge for developing mobile learning systems to enhance children's structural and functional categorization

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Computers and Education: Artificial Intelligence 2021年 2卷

作者： Abbas, MuhammadAzeem Hwang, Gwo-Jen Ajayi, Saheed Mustafa, Ghulam Bilal, Muhammad School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Pakistan Bristol Business School University of West of the England Bristol United Kingdom

The recent decade has seen increased attention focused on understanding category formation–a cognition ability of preschool aged children. Children organize their knowledge about real-world objects by categorizing them under some common properties or functions. The advancement and popularity of mobile devices with touch screens provide a good opportunity for young children to learn and practice. In this study, an approach that models structural and functional categorization knowledge for developing mobile learning systems with dynamic categorization exemplars is proposed. A mobile application was implemented based on the proposed model for pre-schoolers (aged 3–6 years). Moreover, the quasi-experimental pre-test and post-test method was used to evaluate the effectiveness of the proposed knowledge-based application in terms of categorization ability learning. The results show that the children who experienced dynamically created categorization exemplars from the modelled knowledge achieved increased scores compared to those who followed the traditional teaching using books and worksheets. © 2021 The Authors

关键词： Touch screens

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Sampling-based quasiprobability simulation for fault-tolerant quantum error correction on the surface codes under coherent noise

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Physical Review Research 2021年第4期3卷 043130-043130页

作者： Shigeo Hakkaku Kosuke Mitarai Keisuke Fujii Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan RIKEN Center for Quantum Computing (RQC) Hirosawa 2-1 Wako Saitama 351-0198 Japan Fujitsu Quantum Computing Joint Research Division at QIQB Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan

We propose a sampling-based simulation for fault-tolerant quantum error correction under coherent noise. A mixture of incoherent and coherent noise, possibly due to over-rotation, is decomposed into Clifford channels with a quasiprobability distribution. Then, an unbiased estimator of the logical error probability is constructed by sampling Clifford channels with an appropriate postprocessing. We characterize the sampling cost via the channel robustness and find that the proposed sampling-based method is feasible even for planar surface codes with relatively large code distances intractable for full state-vector simulations. As a demonstration, we simulate repetitive faulty syndrome measurements on the planar surface code of distance 5 with 81 qubits. We find that the coherent error increases the logical error rate. This is a practical application of the quasiprobability simulation for a meaningful task and would be useful to explore experimental quantum error correction on the near-term quantum devices.

关键词： Quantum algorithms Quantum computation Quantum error correction Quantum simulation Resource theories Surface code quantum computing

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Effects of spatial uniformity of continuous and pulse laser irradiation on the microstructure and mechanical properties of Ti-6Al-4V in powder bed fusion

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Materials Today Communications 2025年 46卷

作者： Moon, Seunghwan Ji, Seungmuk Ko, Yoonseok Lee, Min-Su Jun, Tea-Sung Kim, Dong-Ik Yeo, Jong-Souk School of Integrated Technology College of Computing Yonsei University Seoul03722 Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul03722 Korea Republic of Center for Energy Materials Research Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Mechanical Engineering Incheon National University Incheon22012 Korea Republic of

Powder bed fusion of metals with laser beam (PBF-LB/M) using Ti-6Al-4V (Ti64) has enabled delicate fabrication of biocompatible medical implants. This research focuses on how the spatial uniformity of laser irradiation affects material under continuous wave (CW) and pulse width modulated (PWM) laser conditions, using a self-developed PBF-LB/M system. The volumetric energy density (VED) was used as a reference parameter in order to accurately compare the effects of CW and PWM lasers, but limitations of VED, which does not include spatial and kinetic factors, which are reinforced in the PWM laser process, were also considered. Results revealed that the uniform distribution of energy irradiation across the entire region brought similar porosities and Vickers hardness tendencies for CW and PWM laser processes. In contrast, the spatially nonuniform energy transfer resulted in a significantly higher porosity in the specimen fabricated using the PWM laser than that produced using the CW laser at the same VED, reducing compressive yield strength. The electron backscattered diffraction analysis showed no significant differences in the average grain size, phase distribution, and misorientation angle of the samples produced using CW and PWM lasers under the same VED. However, sharper grains with an aspect ratio of less than 0.3 were 7 % higher in specimens produced using the PWM laser. This study highlights the possibility of controlling the microstructure and mechanical properties in a wider range when using the PWM laser with spatially nonuniform irradiation. © 2025 Elsevier Ltd

关键词： Vickers hardness

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