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arXiv 2021年

作者： Mathew, Joshua Tian, Xin Wong, Chau-Wai Ho, Simon Milton, Donald K. Wu, Min Department of Electrical and Computer Engineering NC State University RaleighNC27695 United States Department of Electrical and Computer Engineering Forensic Sciences Cluster Secure Computing Institute NC State University RaleighNC27695 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Physical Therapy and Rehabilitation Science University of Maryland School of Medicine BaltimoreMD21201 United States Maryland Institute for Applied Environmental Health School of Public Health University of Maryland College ParkMD20742 United States

Peripheral blood oxygen saturation (SpO2) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO2 before any obvious symptoms. Measuring an individual’s SpO2 without having to come into contact with the person can lower the risk of cross contamination and blood circulation problems. The prevalence of smartphones has motivated researchers to investigate methods for monitoring SpO2 using smartphone cameras. Most prior schemes involving smartphones are contact-based: They require using a fingertip to cover the phone’s camera and the nearby light source to capture reemitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO2 estimation scheme using smartphone cameras. The scheme analyzes the videos of an individual’s hand for physiological sensing, which is convenient and comfortable for users and can protect their privacy and allow for keeping face masks on. We design explainable neural network architectures inspired by the optophysiological models for SpO2 measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO2 measurement, showing the potential of the proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO2 estimation performance. © 2021, CC BY.

关键词： COVID-19

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Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues

arXiv

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arXiv 2022年

作者： Aggarwal, Ankush Jensen, Bjørn Sand Pant, Sanjay Lee, Chung-Hao Glasgow Computational Engineering Centre James Watt School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom School of Computing Science University of Glasgow GlasgowG12 8LT United Kingdom Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby2800 Denmark Zienkiewicz Centre for Computational Engineering Swansea University SwanseaSA1 8EP United Kingdom School of Aerospace and Mechanical Engineering The University of Oklahoma NormanOK73019 United States

Data-based approaches are promising alternatives to the traditional analytical constitutive models for solid mechanics. Herein, we propose a Gaussian process (GP) based constitutive modeling framework, specifically focusing on planar, hyperelastic and incompressible soft tissues. The strain energy density of soft tissues is modeled as a GP, which can be regressed to experimental stress-strain data obtained from biaxial experiments. Moreover, the GP model can be weakly constrained to be convex. A key advantage of a GP-based model is that, in addition to the mean value, it provides a probability density (i.e. associated uncertainty) for the strain energy density. To simulate the effect of this uncertainty, a non-intrusive stochastic finite element analysis (SFEA) framework is proposed. The proposed framework is verified against an artificial dataset based on the Gasser–Ogden–Holzapfel model and applied to a real experimental dataset of a porcine aortic valve leaflet tissue. Results show that the proposed framework can be trained with limited experimental data and fits the data better than several existing models. The SFEA framework provides a straightforward way of using the experimental data and quantifying the resulting uncertainty in simulation-based predictions. © 2022, CC BY.

关键词： Machine learning

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Inertial self-adaptive algorithms with new step size rule for solving split variational inclusion problems in Banach spaces

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Communications in Nonlinear science and Numerical Simulation 2025年 150卷

作者： Sunthrayuth, Pongsakorn Cholamjiak, Prasit Siramaneerat, Issara Yao, Jen-Chih Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Pathumthani12110 Thailand AI-Powered Digital Platform Research Unit Rajamangala University of Technology Thanyaburi Pathumthani12110 Thailand School of Science University of Phayao Phayao56000 Thailand Department of Social Science Faculty of Liberal Arts 3 Rajamangala University of Technology Thanyaburi Pathumthani12110 Thailand Research Center for Interneural Computing China Medical University Hospital China Medical University Taichung40447 Taiwan Department of Applied Mathematics National Sun Yat-sen University Kaohsiung 80424 Taiwan

In this study, we introduce two recent algorithms designed to solve the split variational inclusion problem in Banach spaces by using two inertial extrapolations and self-adaptive step size technique. Theoretical analysis demonstrates that the inertial Mann-type self-adaptive algorithm (IMSA) and the inertial Halpern-type self-adaptive algorithm (IHSA) generate strong convergence under the specified conditions. To validate their performance, numerical experiments are conducted to show the effectiveness of the proposed algorithms in both finite-dimensional and infinite-dimensional spaces. Furthermore, practical applications are highlighted through data classification tasks using the heart disease and diabetes datasets. © 2025 Elsevier B.V.

关键词： Adaptive algorithms

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GFSOM: Genetic Feature Selection for Ontology Matching 12th

GFSOM: Genetic Feature Selection for Ontology Matching

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12th International Conference on Genetic and Evolutionary computing, ICGEC 2018

作者： Belhadi, Hiba Akli-Astouati, Karima Djenouri, Youcef Lin, Jerry Chun-Wei Wu, Jimmy Ming-Tai Algiers Bab Ezzouar Algeria Trondheim Norway Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway College of Computer Science and Engineering Shandong University of Technology Shandong China

ISBN: (纸本)9789811358401

This paper studies the ontology matching problem and proposes a genetic feature selection approach for ontology matching (GFSOM), which exploits the feature selection using the genetic approach to select the most appropriate properties for the matching process. Three strategies are further proposed to improve the performance of the designed approach. The genetic algorithm is first performed to select the most relevant properties, and the matching process is then applied to the selected properties instead of exploring all properties of the given ontology. To demonstrate the usefulness and accuracy of the GFSOM framework, several experiments on DBpedia ontology database are conducted. The results show that the ontology matching process benefits from the feature selection and the genetic algorithm, where GFSOM outperforms the state-of-the-art ontology matching approaches in terms of both the execution time and quality of the matching process. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Semantic Web

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Human-centered artificial intelligence in education: Seeing the invisible through the visible

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computers and Education: Artificial Intelligence 2021年 2卷

作者： Yang, Stephen J.H. Ogata, Hiroaki Matsui, Tatsunori Chen, Nian-Shing Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Academic Center for Computing and Media Studies Kyoto University Yoshidahonmachi Sakyo Ward Kyoto606-8501 Japan School of Human Sciences Waseda University 1-104 Totsukamachi Shinjuku-ku Tokyo169-8050 Japan Department of Applied Foreign Languages National Yunlin University of Science and Technology 123 University Road Section 3 Douliou CityYunlin64002 Taiwan

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives. © 2021 The Author(s)

关键词： Artificial intelligence

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Quantum Statistical Transport Phenomena in Memristive computing Architectures

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Physical Review applied 2021年第5期15卷 054030-054030页

作者： Christopher N. Singh Brian A. Crafton Mathew P. West Alex S. Weidenbach Keith T. Butler Allan H. MacDonald Arjit Raychowdury Eric M. Vogel W. Alan Doolittle L.F.J. Piper Wei-Cheng Lee Department of Physics Applied Physics and Astronomy Binghamton University Binghamton New York 13902 USA Materials Science and Technology Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA SciML Scientific Computing Department Rutherford Appleton Laboratory Didcot OX110QX United Kingdom Department of Physics University of Texas at Austin Austin Texas 78712-1081 USA WMG University of Warwick Coventry CV4 7AL United Kingdom

The advent of reliable nanoscale memristive components is promising for next-generation compute-in-memory paradigms; however, the intrinsic variability in these devices has prevented widespread adoption. Here, we show coherent electron wave functions play a pivotal role in the nanoscale transport properties of these emerging nonvolatile memories. By characterizing both filamentary and nonfilamentary memristive devices as disordered Anderson systems, the switching characteristics and intrinsic variability arise directly from the universality of electron transport in disordered media. Our framework suggests that localization phenomena in nanoscale solid-state memristive systems are directly linked to circuit-level performance. We discuss how quantum conductance fluctuations in the active layer set a lower bound on device variability. This finding implies that there is a fundamental quantum limit on the reliability of memristive devices and that electron coherence will play a decisive role in surpassing or maintaining Moore’s law with these systems.

关键词： Anderson localization Quantum transport Amorphous semiconductors Artificial neural networks Devices Transition metal oxides Density functional theory Linear response theory

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Optimal Parameter Configurations for Sequential Optimization of Variational Quantum Eigensolver

arXiv

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arXiv 2023年

作者： Endo, Katsuhiro Sato, Yuki Raymond, Rudy Wada, Kaito Yamamoto, Naoki Watanabe, Hiroshi C. 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Quantum Computing Center Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama223-8522 Japan Toyota Central R & D Labs. Inc. Koraku Mori Building 10F 1-4-14 Koraku Bunkyo-ku Tokyo112-0004 Japan IBM Quantum IBM Japan 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo103-8510 Japan Department of Computer Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama223-8522 Japan

Variational Quantum Eigensolver (VQE) is a hybrid algorithm for finding the minimum eigenvalue/vector of a given Hamiltonian by optimizing a parametrized quantum circuit (PQC) using a classical computer. Sequential optimization methods, which are often used in quantum circuit tensor networks, are popular for optimizing the parametrized gates of PQCs. This paper focuses on the case where the components to be optimized are single-qubit gates, in which the analytic optimization of a single-qubit gate is sequentially performed. The analytical solution is given by diagonalization of a matrix whose elements are computed from the expectation values of observables specified by a set of predetermined parameters which we call the parameter configurations. In this study, we first show that the optimization accuracy significantly depends on the choice of parameter configurations due to the statistical errors in the expectation values. We then identify a metric that quantifies the optimization accuracy of a parameter configuration for all possible statistical errors, named configuration overhead/cost or C-cost. We theoretically provide the lower bound of C-cost and show that, for the minimum size of parameter configurations, the lower bound is achieved if and only if the parameter configuration satisfies the so-called equiangular line condition. Finally, we provide numerical experiments demonstrating that the optimal parameter configuration exhibits the best result in several VQE problems. We hope that this general statistical methodology will enhance the efficacy of sequential optimization of PQCs for solving practical problems with near-term quantum devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv

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arXiv 2023年

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics Agh University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine Mevis Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan Forxai Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAB Canada Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

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Analysis of variance of graph-clique mining for scalable proof of work 14th

Analysis of variance of graph-clique mining for scalable pro...

引用

14th International Conference on Information Security and Cryptology, Inscrypt 2018

作者： Anada, Hiroaki Matsushima, Tomohiro Su, Chunhua Meng, Weizhi Kawamoto, Junpei Bag, Samiran Sakurai, Kouichi Department of Information Security University of Nagasaki Nagasaki Japan Department of Informatics Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Division of Computer Science University of Aizu Aizuwakamatsu Japan Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark School of Computing Science Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9783030142339

Recently, Bitcoin is becoming one of the most popular decentralized cryptographic currency technologies, and Bitcoin mining is a process of adding transaction records to Bitcoin’s public ledger of past transactions or blockchain. To obtain a bitcoin, the mining process involves compiling recent transactions into blocks and trying to solve a computationally difficult puzzle, e.g., proof of work puzzle. A proof of work allows miners the ability to quantify how much work a given proof contains. Basically, the required time for mining is decided in advance, but problems will occur if the value is large for dispersion. In this paper, we first accept that the required time between consecutive blocks follows the exponential distribution. That is, the variance is stable as long as the expected time is fixed. Then, we focus on the graph clique mining technique proposed by the literature, like Tromp (BITCOIN 2015) and Bag-Ruj-Sakurai (Inscrypt 2015), which is based on a computational difficulty problem of searching cliques of undirected graphs, where a clique is a subset of vertices. In particular, when the clique size is two, graph clique mining can be used to gain Bitcoins. The previous work also claimed that if the clique size is parameterized and increased, even if the expected time is fixed, the variance would not be stable. However, no qualitative or quantitative results were given to support their claim. Motivated by this issue, in this work, we propose a simple search algorithm for graph cliques mining, and perform a small scale evaluation on Bitcoin and Graph cliques’s solo mining to investigate the variance issue. © 2019, Springer Nature Switzerland AG.

关键词： Blockchain

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Optically Heralded Entanglement of Superconducting Systems in Quantum Networks

引用

Physical Review Letters 2021年第4期127卷 040503-040503页

作者： Stefan Krastanov Hamza Raniwala Jeffrey Holzgrafe Kurt Jacobs Marko Lončar Matthew J. Reagor Dirk R. Englund Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA U.S. Army Research Laboratory Computational and Information Sciences Directorate Adelphi Maryland 20783 USA Department of Physics University of Massachusetts at Boston Boston Massachusetts 02125 USA Rigetti Computing 775 Heinz Avenue Berkeley California 94710 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave frequencies at the receiver. However, the small microwave-optical coupling and added noise have proven formidable obstacles. Instead, we propose optical networking via heralding end-to-end entanglement with one detected photon and teleportation. This new protocol can be implemented on standard transduction hardware while providing significant performance improvements over transduction. In contrast to cascaded direct transduction, our scheme absorbs the low optical-microwave coupling efficiency into the heralding step, thus breaking the rate-fidelity trade-off. Moreover, this technique unifies and simplifies entanglement generation between superconducting devices and other physical modalities in quantum networks.

关键词： Entanglement production Optical & microwave phenomena Quantum entanglement Quantum networks Superconducting qubits

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