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arXiv 2023年

作者： Inagaki, Kohdai Ishihara, Keita Hotta, Tomoki Seki, Yuichi Takeda, Takahito Ishida, Tatsuhiro Ootsuki, Daiki Kawasaki, Ikuto Fujimori, Shin-Ichi Tanaka, Masaaki Anh, Le Duc Kobayashi, Masaki Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Graduate School of Human and Environmental Studies Kyoto University Kyoto606-8501 Japan Materials Sciences Research Center Japan Atomic Energy Agency Sayo-gun Hyogo679-5148 Japan Center for Spintronics Research Network The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Saitama Kawaguchi332-0012 Japan

Diamond-type structure allotrope α-Sn is attracting much attention as a topological Dirac semimetal (TDS). In this study, we demonstrate that α-Sn undergoes a phase transition to another allotrope β-Sn with superconductivity at low temperature by irradiating with a focused Ga ion beam (FIB). To clarify the transition mechanism, we performed X-ray photoemission spectroscopy (XPS) measurements on an α-Sn thin film irradiated with FIB and an as-grown α-Sn thin film. The XPS results suggest that the local annealing, which is one of the side effects of FIB, causes the transformation from α-Sn into β-Sn. Furthermore, the difference in the chemical states between α-Sn and β-Sn can be quantitatively explained by the crystal structures rather than the degree of metallicity reflecting the conductivity. These results propose a new way of fabricating TDS/superconductor in-plane heterostructures based on α-Sn and β-Sn. © 2023, CC BY.

关键词： Thin films

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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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Reconfiguring k-Path Vertex Covers

arXiv

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arXiv 2019年

作者： Hoang, Duc A. Suzuki, Akira Yagita, Tsuyoshi Graduate School of Informatics Kyoto University Japan Graduate School of Information Sciences Tohoku University Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

A vertex subset I of a graph G is called a k-path vertex cover if every path on k vertices in G contains at least one vertex from I. The k-Path Vertex Cover Reconfiguration (k-PVCR) problem asks if one can transform one k-path vertex cover into another via a sequence of k-path vertex covers where each intermediate member is obtained from its predecessor by applying a given reconfiguration rule exactly once. We investigate the computational complexity of k-PVCR from the viewpoint of graph classes under the well-known reconfiguration rules: TS, TJ, and TAR. The problem for k = 2, known as the Vertex Cover Reconfiguration (VCR) problem, has been well-studied in the literature. We show that certain known hardness results for VCR on different graph classes can be extended for k-PVCR. In particular, we prove a complexity dichotomy for k-PVCR on general graphs: on those whose maximum degree is three (and even planar), the problem is PSPACE-complete, while on those whose maximum degree is two (i.e., paths and cycles), the problem can be solved in polynomial time. Additionally, we also design polynomial-time algorithms for k-PVCR on trees under each of TJ and TAR. Moreover, on paths, cycles, and trees, we describe how one can construct a reconfiguration sequence between two given k-path vertex covers in a yes-instance. In particular, on paths, our constructed reconfiguration sequence is shortest. Copyright © 2019, The Authors. All rights reserved.

关键词： Polynomial approximation

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Uncertainty-Aware Score Distribution Learning for Action Quality Assessment

Uncertainty-Aware Score Distribution Learning for Action Qua...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yansong Tang Zanlin Ni Jiahuan Zhou Danyang Zhang Jiwen Lu Ying Wu Jie Zhou Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Electrical and Computer Engineering Department Northwestern University Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA). Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Moreover, under the circumstance where finer-grained score labels are available (e.g., difficulty degree of an action or multiple scores from different judges), we further devise a multi-path uncertainty-aware score distribution learning (MUSDL) method to explore the disentangled components of a score. In order to demonstrate the effectiveness of our proposed methods, We conduct experiments on two AQA datasets containing various Olympic actions. Our approaches set new state-of-the-arts under the Spearman's Rank Correlation (i.e., 0.8102 on AQA-7 and 0.9273 on MTL-AQA).

关键词： Videos Uncertainty Games Gaussian distribution Task analysis Quality assessment Training

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Uncertainty-aware Score Distribution Learning for Action Quality Assessment

arXiv

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arXiv 2020年

作者： Tang, Yansong Ni, Zanlin Zhou, Jiahuan Zhang, Danyang Lu, Jiwen Wu, Ying Zhou, Jie Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua Shenzhen International Graduate School Tsinghua University China Electrical and Computer Engineering Department Northwestern University

Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA). Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Moreover, under the circumstance where fine-grained score labels are available (e.g., difficulty degree of an action or multiple scores from different judges), we further devise a multi-path uncertainty-aware score distributions learning (MUSDL) method to explore the disentangled components of a score. We conduct experiments on three AQA datasets containing various Olympic actions and surgical activities, where our approaches set new state-of-the-arts under the Spearman’s Rank Correlation.1 Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

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On Preventing Symbolic Execution Attacks by Low Cost Obfuscation

On Preventing Symbolic Execution Attacks by Low Cost Obfusca...

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ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Toshiki Seto Akito Monden Zeynep Yücel Yuichiro Kanzaki Graduate School of National Science and Technology Okayama University Okayama Japan Department of Human-Oriented Information Systems Engineering National Institute of Technology Kumamoto College Kumamoto Japan

While various software obfuscation techniques have been proposed to protect software, new types of threats keep emerging such as the symbolic execution attacks. Such attacks automatically analyze programs and are not accounted for by many of the existing obfuscation methods. Nevertheless, several methods against symbolic execution attacks exist such as linear obfuscation methods relying on Collatz conjuncture or obfuscation methods based on one-way hash functions. However, these methods bear several issues. Namely, linear obfuscation is weak against manual analysis due to its deterministic output. On the other hand, SHA-1 requires significant computational cost; and thus, it can be applied to only a limited number of targets. Therefore, in this research, we propose to employ a combination of several computationally cheap (arithmetic) obfuscating operations for preventing symbolic execution attacks. Through an experiment using angr and KLEE as symbolic execution tools, we demonstrate that obfuscation operation using array reference, bit rotation and XOR effectively prevents symbolic execution attacks at a low computational cost.

关键词： Password Indium phosphide III-V semiconductor materials Tools Computational efficiency Software Hash functions

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Relationship analysis between puzzle-like programming game and achievement result after learning the basic of programming

Relationship analysis between puzzle-like programming game a...

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2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018

作者： Matsumoto, Shimpei Yamagishi, Shuichi Kashima, Tomoko Department of Information Systems and Management Faculty of Applied Information Science Hiroshima Institute of Technology Graduate School of Engineering 2-1-1 Miyake Saeki-ku Hi-roshima731-5193 Japan Department of Information and Systems Engineering Faculty of Engineering Kindai University 1 Takaya Umenobe Higashi-Hiroshima City Hiroshima739-2116 Japan

ISBN: (纸本)9789881404886

Japan Electronics and information technology Industries Association develops a puzzle-like programming game “Algologic” aiming to experience the concept of an algorithm for inexperienced programming learners. This is simple puzzle game aiming at solving a problem by automatically controlling a robot. The user designs an autonomous robot by selecting some of the instruction blocks, arranging the blocks in an appropriate order, and giving them to the robot. Before university students learn the basic of programming, with this programming puzzle game, we conducted a test to determine whether algorithms each student gave to the robot were correct or not. Likewise, after students have learned the basic of programming, we conducted a comprehension test to clarify the reachability. In this paper, we aim to investigate the relation between the comprehension of Algologic and the achievement degree of students after learning of programming and report the analysis result. Analysis results revealed that the results of Algologic test and the achievement results after learning programming were significantly in a positive relationship. © 2018 Newswood Limited. All rights reserved.

关键词： Robot programming

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Silverrush X: Machine learning-aided selection of 9,318 laes at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and chorus survey data

arXiv

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arXiv 2021年

作者： Ono, Yoshiaki Itoh, Ryohei Shibuya, Takatoshi Ouchi, Masami Harikane, Yuichi Yamanaka, Satoshi Inoue, Akio K. Amagasa, Toshiyuki Miura, Daichi Okura, Maiki Shimasaku, Kazuhiro Iwata, Ikuru Taniguchi, Yoshiaki Fujimoto, Seiji Iye, Masanori Jaelani, Anton T. Kashikawa, Nobunari Kikuchihara, Shotaro Kikuta, Satoshi Kobayashi, Masakazu A.R. Kusakabe, Haruka Lee, Chien-Hsiu Liang, Yongming Matsuoka, Yoshiki Momose, Rieko Nagao, Tohru Nakajima, Kimihiko Tadaki, Ken-Ichi Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba277-8582 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Kitami Institute of Technology 165 Koen-cho Kitami Hokkaido090-8507 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan University of Tokyo Kashiwa Chiba277-8583 Japan Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Research Center for Space and Cosmic Evolution Ehime University Bunkyo-cho Matsuyama Ehime790-8577 Japan Department of Physics School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki305-8573 Japan Center for Computational Sciences University of Tsukuba Ibaraki305-8577 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Research Center for the Early Universe Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan The Open University of Japan 2-11 Wakaba Mihama-ku Chiba Chiba261-8586 Japan Cosmic DAWN Center Copenhagen Denmark Niels Bohr Institute University of Copenhagen Lyngbyvej 2 CopenhagenDK-2100 Denmark Astronomy Research Division and Bosscha Observatory FMIPA Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Department of Physics Kindai University 3-4-1 Kowakae Higashi-Osaka Osaka577-8502 Japan Faculty of Natural Sciences National Institute of Technology Kure College 2-2-11 Agaminami Kure Hiroshima737-8506 Japan Observatoire de Genève Université de G

We present a new catalog of 9,318 Lyα emitter (LAE) candidates at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 that are photometrically selected by the SILVERRUSH program with a machine learning technique from large area (up to 25.0 deg2) imaging data with six narrowband filters taken by the Subaru Strategic Program with Hyper Suprime-Cam (HSC SSP) and a Subaru intensive program, Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS). We construct a convolutional neural network that distinguishes between real LAEs and contaminants with a completeness of 94% and a contamination rate of 1%, enabling us to efficiently remove contaminants from the photometrically selected LAE candidates. We confirm that our LAE catalogs include 177 LAEs that have been spectroscopically identified in our SILVERRUSH programs and previous studies, ensuring the validity of our machine learning selection. In addition, we find that the object-matching rates between our LAE catalogs and our previous results are 80-100% at bright NB magnitudes of 24 mag. We also confirm that the surface number densities of our LAE candidates are consistent with previous results. Our LAE catalogs will be made public on our project webpage. Copyright © 2021, The Authors. All rights reserved.

关键词： Galaxies

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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