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Scientific discovery in the age of artificial intelligence

NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Disentangling high-order mechanisms and high-order behaviours in complex systems

arXiv

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arXiv 2022年

作者： Rosas, Fernando E. Mediano, Pedro A.M. Luppi, Andrea I. Varley, Thomas F. Lizier, Joseph T. Stramaglia, Sebastiano Jensen, Henrik J. Marinazzo, Daniele Data Science Institute Imperial College London United Kingdom Centre for Psychedelic Research Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Psychology Queen Mary University of London United Kingdom University Division of Anaesthesia University of Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge United Kingdom Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Centre for Complex Systems Faculty of Engineering The University of Sydney Australia Dipartimento Interateneo di Fisica Università Degli Studi di Bari Aldo Moro Italy INFN Sezione di Bari Italy Department of Mathematics Imperial College London United Kingdom Institute of Innovative Research Tokyo Institute of Technology Japan Department of Data Analysis Ghent University Belgium

Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements, which can be modelled by hypergraphs and studied via topological data analysis. Following a separate line of enquiry, a broad literature has developed information-theoretic tools to characterize high-order interdependencies from observed data. While these could seem to be competing approaches aiming to address the same question, in this correspondence we clarify that this is not the case, and that a complete account of higher-order phenomena needs to embrace both. Copyright © 2022, The Authors. All rights reserved.

关键词： Large scale systems

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Correction: VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images

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BMC medical imaging 2024年第1期24卷 128页

作者： Anindita Saha Shahid Mohammad Ganie Pijush Kanti Dutta Pramanik Rakesh Kumar Yadav Saurav Mallik Zhongming Zhao Department of Computing Science and Engineering IFTM University Moradabad Uttar Pradesh India. AI Research Centre Department of Analytics School of Business Woxsen University Hyderabad Telangana 502345 India. School of Computer Applications and Technology Galgotias University Greater Noida Uttar Pradesh 203201 India. pijushjld@yahoo.co.in. Department of Computer Science & Engineering MSOET Maharishi University of Information Technology Lucknow Uttar Pradesh India. Department of Environmental Health Harvard T. H. Chan School of Public Health Boston MA USA. Center for Precision Health McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX 77030 USA. zhongming.zhao@uth.tmc.edu.

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An iris segmentation technique using black hole search method and canny edge detector

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AIP Conference Proceedings 2024年第1期2867卷

作者： Sani Sufyan Bature Danlami Muktar Sapi’ee Jamel Ibrahim M. Sulaiman Sukono Sukono Aceng Sambas Department of Computer Science Yusuf Maitama Sule University Kano Nigeria Faculty of Computer Science and Information Technology Universiti Tun Hussien On Malaysia Johor Malaysia Institute of Strategic Industrial Decision Modelling (ISIDM) School of Quantitative Sciences Universiti Utara Malaysia UUM Sintok 06010 Kedah Malaysia Department of Mathematics Universitas Padjadjaran Sumedang Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut 22200 Terengganu Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tasikmalaya Indonesia

Biometric is the most fundamental and efficient technique used in terms of recognizing individuals uniquely. As we have two types of biometric; one on physical and the other in terms behavioral characteristics. Iris segmentation is a crucial stage in iris recognition systems since it determines the accuracy of all future phases and can be segmented using a variety of techniques. There are different iris segmentation techniques, but the most widely used ones are the circular Hough transform and Daugman’s algorithm which are commonly employed in commercial and research problems involving iris recognition systems. In iris recognition system, the time it took to detect the iris and the accuracy with which it was located are both important aspects. Due to low in terms of accuracy we have suggested a different approach of iris segmentation based on the black hole search method and canny edge detector in this research. The proposed approach is tested using CASIA and MMU Database iris images. This technique is effective in terms of the time it takes to detect an eye’s iris, as it directly utilizes intensity and shape. Also, we can detect the iris of an eye much faster than the standard technique with the help of image processing.

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RETRACTED ARTICLE: Utilizing Index-Based Periodic High Utility Mining to Study Frequent Itemsets

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Arabian Journal for Science and Engineering 2021年第3期48卷 4123-4123页

作者： Setiawan, Roy Le, Dac-Nhuong Rajan, Regin Subramani, Thirukumaran Sharma, Dilip Kumar Ponnam, Vidya Sagar Kumar, Kailash Akbar Batcha, Syed Musthafa Dadheech, Pankaj Sengan, Sudhakar Department Management Universitas Kristen Petra Jawa Timur Indonesia School of Computer Science/Institute of Research and Development Duy Tan University Danang Vietnam Department of Computer Science and Engineering Adhiyamaan College of Engineering Hosur India Department of Computer Science and Engineering KL University Vijayawada India Jaypee University of Engineering and Technology Guna India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia Department of Information Technology M. Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Jaipur India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

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Reading race: AI recognizes patient's racial identity in medical images

arXiv

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arXiv 2021年

作者： Banerjee, I. Bhimireddy, A.R. Burns, J.L. Celi, L.A. Chen, L. Correa, R. Dullerud, N. Ghassemi, M. Gichoya, J.W. Huang, S. Kuo, P. Lungren, M.P. Price, B.J. Purkayastha, S. Pyrros, A.A. Oakden-Rayner, L. Okechukwu, C. Seyyed-Kalantari, L. Trivedi, H. Wang, R. Zaiman, Z. Zhang, H. Department of Biomedical Informatics Emory University 100 Woodruff Circle AtlantaGA30322 United States School of Informatics and Computing Indiana University Purdue University 535 W Michigan St. IT 475E IndianapolisIN46202 United States Institute for Medical Engineering and Science Massachusetts Institute of Technology 45 Carleton Street E25- 330 CambridgeMA02142 United States Department of Medicine Beth Israel Deaconess Medical Center 330 Brookline Avenue BostonMA02215 United States Department of Computer Science National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Department of Computer Science Emory University 201 Dowman Drive AtlantaGA30322 United States Department of Computer Science University of Toronto 214 College St TorontoONM5T 3A1 Canada Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Radiology Emory University 1364 Clifton Rd AtlantaGA30322 United States Stanford University School of Medicine 291 Campus Drive Palo AltoCA94305 United States Florida State University College of Medicine 1115 W Call St TallahasseeFL32304 United States Australian Institute for Machine Learning University of Adelaide AIML Building Lot Fourteen Cnr North Terrace and Frome Road AdelaideSA5000 Australia Department of Computer Science Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332 United States Lunenfeld-Tanenbaum Research Institute Sinai Health 600 University Ave TorontoONM5G 1X5 Canada Dupage Medical Group 40 S Clay St HinsdaleIL60521 United States

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. We conduct a comprehensive evaluation of AI's ability to recognize patient's racial identity from medical images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race - even from corrupted, cropped, and noised medical images - in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model ha

关键词： Medical imaging

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

arXiv

引用

arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Advances in quantum cryptography

arXiv

引用

arXiv 2019年

作者： Pirandola, S. Andersen, U.L. Banchi, L. Berta, M. Bunandar, D. Colbeck, R. Englund, D. Gehring, T. Lupo, C. Ottaviani, C. Pereira, J. Razavi, M. Shaari, J.S. Tomamichel, M. Usenko, V.C. Vallone, G. Villoresi, P. Wallden, P. Computer Science and York Centre for Quantum Technologies University of York YorkYO10 5GH United Kingdom CambridgeMA02139 United States Department of Physics Technical University of Denmark Fysikvej 2800 Kgs. Lyngby2800 Denmark I-50019 Italy Department of Computing Imperial College Kensington LondonSW7 2AZ United Kingdom Department of Mathematics University of York YorkYO10 5DD United Kingdom Department of Physics and Astronomy University of Sheffield SheffieldS3 7RH United Kingdom School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom Jalan Sultan Ahmad Shah Kuantan Pahang25200 Malaysia University Putra Malaysia 43400 UPM Serdang Selangor Malaysia Centre for Quantum Software and Information School of Software University of Technology Sydney SydneyNSW2007 Australia Department of Optics Palacky University 17. listopadu 50 Olomouc772 07 Czech Republic Dipartimento di Ingegneria dell’Informazione Universitá degli Studi di Padova Via Gradenigo 6B Padova35131 Italy School of Informatics University of Edinburgh 10 Crichton Street EdinburghEH8 9AB United Kingdom

Quantum cryptography is arguably the fastest growing area in quantum information science. Novel theoretical protocols are designed on a regular basis, security proofs are constantly improving, and experiments are gradually moving from proof-of-principle lab demonstrations to in-field implementations and technological prototypes. In this review, we provide both a general introduction and a state of the art description of the recent advances in the field, both theoretically and experimentally. We start by reviewing protocols of quantum key distribution based on discrete variable systems. Next we consider aspects of device independence, satellite challenges, and high rate protocols based on continuous variable systems. We will then discuss the ultimate limits of point-to-point private communications and how quantum repeaters and networks may overcome these restrictions. Finally, we will discuss some aspects of quantum cryptography beyond standard quantum key distribution, including quantum data locking and quantum digital signatures. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

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The breakthrough listen search for intelligent life near the Galactic Center I

arXiv

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arXiv 2021年

作者： Gajjar, Vishal Perez, Karen I. Siemion, Andrew P.V. Foster, Griffin Brzycki, Bryan Chatterjee, Shami Chen, Yuhong Cordes, James M. Croft, Steve Czech, Daniel DeBoer, David DeMarines, Julia Drew, Jamie Gowanlock, Michael Isaacson, Howard Lacki, Brian C. Lebofsky, Matt MacMahon, David H.E. Morrison, Ian S. Ng, Cherry de Pater, Imke Price, Danny C. Sheikh, Sofia Z. Suresh, Akshay Webb, Claire Worden, S. Pete Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Astronomy Columbia University 550 West 120th Street New YorkNY10027 United States Department of Astrophysics IMAPP Radboud University Nijmegen Netherlands SETI Institute Mountain ViewCA United States University of Malta Institute of Space Sciences and Astronomy Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Breakthrough Initiatives NASA Research Park Bld. 18 Moffett FieldCA94035 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States University of Southern Queensland ToowoombaQLD4350 Australia International Centre for Radio Astronomy Research Curtin Institute of Radio Astronomy Curtin University PerthWA6845 Australia Department of Astronomy and Astrophysics Pennsylvania State University University ParkPA16802 United States Massachusetts Institute of Technology CambridgeMA United States

A line-of-sight towards the Galactic Center (GC) offers the largest number of potentially habitable systems of any direction in the sky. The Breakthrough Listen program is undertaking the most sensitive and deepest targeted SETI surveys towards the GC. Here, we outline our observing strategies with Robert C. Byrd Green Bank Telescope (GBT) and Parkes telescope to conduct 600 hours of deep observations across 0.7–93 GHz. We report preliminary results from our survey for ETI beacons across 1–8 GHz with 7.0 and 11.2 hours of observations with Parkes and GBT, respectively. With our narrowband drifting signal search, we were able to place meaningful constraints on ETI transmitters across 1–4 GHz and 3.9–8 GHz with EIRP limits of ≥4×1018 W among 60 million stars and ≥5×1017 W among half a million stars, respectively. For the first time, we were able to constrain the existence of artificially dispersed transient signals across 3.9–8 GHz with EIRP ≥1×1014 W/Hz with a repetition period ≤4.3 hours. We also searched our 11.2 hours of deep observations of the GC and its surrounding region for Fast Radio Burst-like magnetars with the DM up to 5000 pc cm−3 with maximum pulse widths up to 90 ms at 6 GHz. We detected several hundred transient bursts from SGR J1745−2900, but did not detect any new transient burst with the peak luminosity limit across our observed band of ≥1031 erg s−1 and burst-rate of ≥0.23 burst-hr−1. These limits are comparable to bright transient emission seen from other Galactic radio-loud magnetars, constraining their presence at the GC. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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