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arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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Ultrafast circuit protection for unmanned systems

Ultrafast circuit protection for unmanned systems

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2019 International Conference on Communications, Information System, and computer engineering, CISCE 2019

作者： Kao, Chih-Hong Huang, Sy-Ruen He, Bo-Rong Lin, Hsiung-Cheng School of Computer Science Minnan Normal University Lab of Granular Computing ZhangZhou Fujjan China Department of Electrical Engineering Feng Chia University Taichung Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering Chin-Yi University of Technology Taichung Taiwan

ISBN: (纸本)9781728136813

With increasing dependence on precision electronic equipment, self-protection is becoming essential. The protection circuits commonly used before were fuses and circuit breakers which, however, they are not enough for the current need of high-speed protection circuits and automated engineering logic. In this paper, we develop a new ultrahigh-speed protection circuit, which is over exceeded 20 times faster than traditional circuits and has the ability to self-recover when any failure occurs. With the new circuit developed, it is not necessary to do manual restart and will meet the future needs of protection for electronic precision equipment. This design provides greater convenience and reliability for the unmanned equipment for the future. © 2019 IEEE.

关键词： Timing circuits

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Online Handbook of Argumentation for AI: Volume 3

arXiv

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arXiv 2022年

作者： Bengel, Lars Bezou-Vrakatseli, Elfia Blümel, Lydia Castagna, Federico D'Agostino, Giulia Odekerken, Daphne Patil, Minal Suresh Robinson, Jordan Wu, Hao Xydis, Andreas Artificial Intelligence Group University of Hagen Germany King’s College London United Kingdom Artificial Intelligence Group University of Hagen Germany School of Computer Science University of Lincoln United Kingdom Institute of Argumentation Linguistics and Semiotics Università della Svizzera Italiana Switzerland Department of Information and Computing Sciences Utrecht University Netherlands National Police Lab AI Netherlands Umeå Universitet Sweden Department of Electrical Engineering and Electronics University of Liverpool United Kingdom University of Aberdeen United Kingdom Department of Informatics King’s College London United Kingdom

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI. The handbook’s goals are to: 1. Encourage collaboration and knowledge discovery between members of the argumentation community. 2. Provide a platform for PhD students to have their work published in a citable peer-reviewed venue. 3. Present an indication of the scope and quality of PhD research involving argumentation for AI. © 2022, CC BY.

关键词： Handbooks

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Minority Oversampling Using Sensitivity

Minority Oversampling Using Sensitivity

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International Joint Conference on Neural Networks (IJCNN)

作者： Jianjun Zhang Ting Wang Wing W. Y. Ng Witold Pedrycz Shuai Zhang Chris D. Nugent Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada School of Computing Ulster University Jordanstown United Kingdom

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The Synthetic Minority Oversampling Technique (SMOTE) is effective to handle imbalance classification problems. However, the random candidate selection of SMOTE may lead to severe overlap between classes and introduce new noise factors. Many variants of SMOTE have been proposed to relieve these problems by generating new examples in safe regions. Most of these methods generate new examples with existing minority examples without considering the negative impact that class imbalance have brought on these examples. In this paper, we handle the imbalance classification using Bayes' decision rule and propose a novel oversampling method, the Minority Oversampling using Sensitivity (MOSS). Candidates for new example generations are selected considering their sensitivity with respect to class imbalance. New examples are then generated by interpolating the candidate and one of its adjacent examples. Experiments on 30 datasets confirm the superiority of the MOSS against one baseline method and seven oversampling methods.

关键词： Sensitivity Noise measurement Computational intelligence Cyberspace computer science Gaussian distribution Classification algorithms

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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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Memory that never forgets:emerging nonvolatile memory and the implication for architecture design

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National Science Review 2018年第4期5卷 577-592页

作者： Guangyu Sun Jishen Zhao Matt Poremba Cong Xu Yuan Xie Center for Energy-Efficient Computing and Applications School of Electronics Engineering and Computer Science Peking University Department of Computer Engineering University of California at Santa Cruz Advanced Micro Devices Inc. Hewlett Packard Lab Department of Electrical and Computer Engineering University of California at Santa Barbara

In order to mitigate the problem of the ‘memory wall', various emerging nonvolatile memory(NVM)technologies have been proposed to replace the traditional ones. These emerging NVMs include spin torque transfer random access memory, PCRAM, resistive random access memory, racetrack memory, *** to traditional memory technologies, they have the advantages of near-zero standby power, high storage density and nonvolatility, which make them competitive for future memory hierarchy ***, it is inefficient to directly apply these NVMs in existing memory architectures. On the one hand,these NVMs have their own limitations, such as long write latency, high write energy, limited write numbers, etc. Thus, proper architecture modification is required to adopt them into the traditional memory hierarchy. On the other hand, the unique features of these NVMs allow new memory architectures in memory subsystems and also introduce new challenges to be solved at the same time. In this paper, we not only review various emerging NVMs but also study typical related work on these topics to investigate their implications for memory architecture design.

关键词： non-volatile memory cache main memory energy efficiency

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Meta deformation network: Meta functionals for shape correspondence

arXiv

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arXiv 2020年

作者： Lu, Daohan Fang, Yi Multimedia and Visual Computing Lab New York University New York United States Tandon School of Engineering New York University New York United States Department of Electrical and Computer Engineering New York University Abu Dhabi United Arab Emirates College of Arts and Science New York University New York United States

We present a new technique named Meta Deformation Network for 3D shape matching via deformation, in which a deep neural network maps a reference shape onto the parameters of a second neural network whose task is to give the correspondence between a learned template and query shape via deformation. We categorize the second neural network as a meta-function, or a function generated by another function, as its parameters are dynamically given by the first network on a per-input basis. This leads to a straightforward overall architecture and faster execution speeds, without loss in the quality of the deformation of the template. We show in our experiments that Meta Deformation Network leads to improvements on the MPI-FAUST Inter Challenge over designs that utilized a conventional decoder design that has non-dynamic Copyright © 2020, The Authors. All rights reserved.

关键词： Deformation

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Facilitating temporal synchronous target selection through user behavior modeling

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019年第4期3卷 1-24页

作者： Zhang, Tengxiang Yi, Xin Wang, Ruolin Gao, Jiayuan Wang, Yuntao Yu, Chun Li, Simin Shi, Yuanchun Tsinghua University Institute of Computing Technology Chinese Academy of Sciences Kexueyuan South Road Beijing100084 China Key Laboratory of Pervasive Computing Ministry of Education Beijing Key Lab of Networked Multimedia Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University China Tsinghua University China Global Innovation eXchange Institute Key Laboratory of Pervasive Computing Ministry of Education Beijing Key Lab of Networked Multimedia Department of Computer Science and Technology Tsinghua University Shuangqing Road Beijing100081 China Department of Electrical and Computing Engineering Beihang University Beijing100081 China Tsinghua University Shuangqing Road Beijing100081 China UCLA United States

Temporal synchronous target selection is an association-free selection technique: users select a target by generating signals (e.g., finger taps and hand claps) in sync with its unique temporal pattern. However, classical pattern set design and input recognition algorithm of such techniques did not leverage users' behavioral information, which limits their robustness to imprecise inputs. In this paper, we improve these two key components by modeling users' interaction behavior. In the first user study, we asked users to tap a finger in sync with blinking patterns with various period and delay, and modeled their finger tapping ability using Gaussian distribution. Based on the results, we generated pattern sets for up to 22 targets that minimized the possibility of confusion due to imprecise inputs. In the second user study, we validated that the optimized pattern sets could reduce error rate from 23% to 7% for the classical Correlation recognizer. We also tested a novel Bayesian, which achieved higher selection accuracy than the Correlation recognizer when the input sequence is short. The informal evaluation results show that the selection technique can be effectively scaled to different modalities and sensing techniques. Copyright © 2019 held by the owner/author(s).

关键词： Taps

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Vector Symbolic Architectures as a computing Framework for Emerging Hardware

arXiv

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arXiv 2021年

作者： Kleyko, Denis Davies, Mike Frady, E. Paxon Kanerva, Pentti Kent, Spencer J. Olshausen, Bruno A. Osipov, Evgeny Rabaey, Jan M. Rachkovskij, Dmitri A. Rahimi, Abbas Sommer, Friedrich T. The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States The Intelligent Systems Lab Research Institutes of Sweden Kista16440 Sweden The Neuromorphic Computing Laboratory Intel Labs Santa ClaraCA95054 United States The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden The Department of Electrical Engineering and Computer Sciences The University of California BerkeleyCA94720 United States International Research and Training Center for Information Technologies and Systems Kyiv03680 Ukraine IBM Research – Zurich Rüschlikon8803 Switzerland

This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing. Copyright © 2021, The Authors. All rights reserved.

关键词： Vectors

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Leveraging randomized compiling for the quantum imaginary-time-evolution algorithm

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Physical Review Research 2022年第3期4卷 033140-033140页

作者： Jean-Loup Ville Alexis Morvan Akel Hashim Ravi K. Naik Marie Lu Bradley Mitchell John-Mark Kreikebaum Kevin P. O'Brien Joel J. Wallman Ian Hincks Joseph Emerson Ethan Smith Ed Younis Costin Iancu David I. Santiago Irfan Siddiqi Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Inst. for Quantum Computing and Dept. of Applied Mathematics University of Waterloo Waterloo Ontario Canada N2L 3G1 Quantum Benchmark Inc. Kitchener Ontario Canada N2H 5G5

Recent progress in noisy intermediate-scale quantum (NISQ) hardware shows that quantum devices may be able to tackle complex problems even without error correction. However, coherent errors due to the increased complexity of these devices is an outstanding issue. They can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like quantum imaginary time evolution are susceptible to these errors. This article presents the combination of both noise tailoring using randomized compiling and error mitigation with purification. We also show that cycle benchmarking gives an estimate of the reliability of the purification. We apply this method to the quantum imaginary time evolution of a transverse field Ising model and report an energy estimation error and a ground-state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.

关键词： Measurement-based quantum computing Quantum algorithms Quantum benchmarking Quantum computation Quantum control Quantum information processing Quantum simulation

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