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Search for Axionlike Dark Matter Using Solid-State Nuclear Magnetic Resonance

Physical Review Letters 2021年第14期126卷 141802-141802页

作者： Deniz Aybas Janos Adam Emmy Blumenthal Alexander V. Gramolin Dorian Johnson Annalies Kleyheeg Samer Afach John W. Blanchard Gary P. Centers Antoine Garcon Martin Engler Nataniel L. Figueroa Marina Gil Sendra Arne Wickenbrock Matthew Lawson Tao Wang Teng Wu Haosu Luo Hamdi Mani Philip Mauskopf Peter W. Graham Surjeet Rajendran Derek F. Jackson Kimball Dmitry Budker Alexander O. Sushkov Department of Physics Boston University Boston Massachusetts 02215 USA Department of Electrical and Computer Engineering Boston University Boston Massachusetts 02215 USA Helmholtz-Institut GSI Helmholtzzentrum für Schwerionenforschung 55128 Mainz Germany Johannes Gutenberg-Universität Mainz 55128 Mainz Germany The Oskar Klein Centre for Cosmoparticle Physics Department of Physics Stockholm University AlbaNova 10691 Stockholm Sweden Nordita KTH Royal Institute of Technology and Stockholm University Roslagstullsbacken 23 10691 Stockholm Sweden Department of Physics Princeton University Princeton New Jersey 08544 USA State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics and Center for Quantum Information Technology Peking University Beijing 100871 China Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai 201800 China School of Earth and Space Exploration Arizona State University Tempe Arizona 85287 USA Stanford Institute for Theoretical Physics Stanford University Stanford California 94305 USA Department of Physics and Astronomy The Johns Hopkins University Baltimore Maryland 21218 USA Department of Physics California State University–East Bay Hayward California 94542-3084 USA Department of Physics University of California Berkeley California 94720-7300 USA Photonics Center Boston University Boston Massachusetts 02215 USA

We report the results of an experimental search for ultralight axionlike dark matter in the mass range 162–166 neV. The detection scheme of our Cosmic Axion Spin Precession Experiment is based on a precision measurement of Pb207 solid-state nuclear magnetic resonance in a polarized ferroelectric crystal. Axionlike dark matter can exert an oscillating torque on Pb207 nuclear spins via the electric dipole moment coupling gd or via the gradient coupling gaNN. We calibrate the detector and characterize the excitation spectrum and relaxation parameters of the nuclear spin ensemble with pulsed magnetic resonance measurements in a 4.4 T magnetic field. We sweep the magnetic field near this value and search for axionlike dark matter with Compton frequency within a 1 MHz band centered at 39.65 MHz. Our measurements place the upper bounds |gd|<9.5×10−4 GeV−2 and |gaNN|<2.8×10−1 GeV−1 (95% confidence level) in this frequency range. The constraint on gd corresponds to an upper bound of 1.0×10−21 e cm on the amplitude of oscillations of the neutron electric dipole moment and 4.3×10−6 on the amplitude of oscillations of CP-violating θ parameter of quantum chromodynamics. Our results demonstrate the feasibility of using solid-state nuclear magnetic resonance to search for axionlike dark matter in the neV mass range.

关键词： Particle dark matter Axions Ferroelectrics Relaxor ferroelectrics Dark matter detectors Nuclear magnetic resonance Precision measurements

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Lithography-free, planar IR polarization filters and converters via biaxial phonons in a-MoO3 flakes integrated into Fabry-Perot cavities

arXiv

引用

arXiv 2020年

作者： Dereshgi, Sina Abedini Folland, Thomas G. Murthy, Akshay A. Song, Xianglian Tanriover, Ibrahim Dravid, Vinayak P. Caldwell, Joshua D. Aydin, Koray Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States International Institute for Nanotechnology Northwestern University EvanstonIL60208 United States International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology of Ministry of Education Engineering Technology Research Center for 2D material Information Function Devices and Systems of Guangdong Province College of Optoelectronic Engineering Shenzhen University Shenzhen518060 China Center Northwestern University EvanstonIL60208 United States

Exploiting polaritons in natural vdW materials has been successful in achieving extreme light confinement and low-loss optical interactions and enabling inherent simplified device integration. Recently, a-MoO3 has been reported as a semiconducting biaxial vdW material capable of sustaining naturally anisotropic phonon polariton modes in IR. In this study, we investigate the polarization-dependent optical characteristics of cavities formed using a-MoO3 to extend the degrees of freedom in the design of IR photonic components by tailoring the in-plane anisotropy of this material. Polarization-dependent absorption over 80% in a multilayer Fabry-Perot structure with a-MoO3 is reported without the need for nanoscale fabrication on the a-MoO3. We observe coupling between the a-MoO3 optical phonons and the Fabry-Perot cavity resonances. Using cross-polarized reflectance spectroscopy we show that the strong birefringence results in 15% of the total power converted into the orthogonal polarization with respect to incident wave. These findings can open new avenues in the quest for polarization filters and low-loss, integrated planar IR photonics and in dictating polarization control. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

引用

SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Correction to: The ELFIN Mission

引用

Space Science Reviews 2020年第6期216卷 1-2页

作者： Angelopoulos, V. Tsai, E. Bingley, L. Shaffer, C. Turner, D. L. Runov, A. Li, W. Liu, J. Artemyev, A. V. Zhang, X.-J. Strangeway, R. J. Wirz, R. E. Shprits, Y. Y. Sergeev, V. A. Caron, R. P. Chung, M. Cruce, P. Greer, W. Grimes, E. Hector, K. Lawson, M. J. Leneman, D. Masongsong, E. V. Russell, C. L. Wilkins, C. Hinkley, D. Blake, J. B. Adair, N. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Capitelli, M. R. Castro, R. Chao, G. Chung, N. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B. W. Eldin, S. Fitzgibbon, L. Flemming, A. Fox, I. Frederick, D. M. Gilbert, A. Gildemeister, A. Gonzalez, A. Hesford, B. Jha, S. Kang, N. King, J. Krieger, R. Lian, K. Mao, J. McKinney, E. Miller, J. P. Norris, A. Nuesca, M. Palla, A. Park, E. S. Y. Pedersen, C. E. Qu, Z. Rozario, R. Rye, E. Seaton, R. Subramanian, A. Sundin, S. R. Tan, A. Turner, W. Villegas, A. J. Wasden, M. Wing, G. Wong, C. Xie, E. Yamamoto, S. Yap, R. Zarifian, A. Zhang, G. Y. Earth Planetary and Space Sciences Department University of California Los Angeles USA Institute of Geophysics and Planetary Physics University of California Los Angeles USA Tyvak Nano-Satellite Systems Inc. Irvine USA Johns Hopkins University Applied Physics Laboratory Laurel USA Department of Astronomy and Center for Space Physics Boston University Boston USA Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los Angeles USA GFZ German Research Centre for Geosciences Potsdam Germany Saint Petersburg State University St. Petersburg Russia Northrop Grumman Aerospace Systems Redondo Beach USA Raytheon Space and Airborne Systems El Segundo USA The Aerospace Corporation El Segundo USA Millenium Space Systems El Segundo USA Aptiv Agoura Hills USA Physics and Astronomy Department University of California Los Angeles USA The Boeing Company Long Beach USA SF Motors Santa Clara USA SpaceX Hawthorne USA Planet Labs Inc. San Francisco USA Computer Science Department Henry Samueli School of Engineering University of California Los Angeles USA Electrical and Computer Engineering Department Henry Samueli School of Engineering University of California Los Angeles USA Jet Propulsion Laboratory Pasadena USA Mercedes-Benz Research and Development North America Long Beach USA Epic Systems Corporation Verona USA California State Polytechnic University Pomona USA Economics Department University of California Los Angeles USA Experior Laboratories Oxnard USA Mathematics Department University of California Los Angeles USA Qualcomm San Diego USA

Correction to: Space Sci. Rev. (2020) 216: 103 Affiliation 2 in this article is the wrong affiliation that instead should read: “Institute of Geophysics and Planetary Physics, University of California, Los Angeles, C...

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Convergence of a Distributed Least Squares

arXiv

引用

arXiv 2019年

作者： Xie, Siyu Zhang, Yaqi Guo, Lei The Department of Electrical and Computer Engineering Wayne State University DetroitMI48202 United States Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Science University of Chinese Academy of Sciences Beijing100049 China

In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors. Our main task is to generalize the well-known theoretical results on the traditional LS to the current distributed case by establishing both the upper bound of the accumulated regrets of the adaptive predictor and the convergence of the distributed LS estimator, with the following key features compared with the existing literature on distributed estimation: Firstly, our theory does not need the previously imposed independence, stationarity or Gaussian property on the system signals, and hence is applicable to stochastic systems with feedback. Secondly, the cooperative excitation condition introduced and used in this paper for the convergence of the distributed LS estimate is the weakest possible one, which shows that even if any individual sensor cannot estimate the unknown parameter by the traditional LS, the whole network can still fulfill the estimation task by the distributed *** Codes 93E24, 93E35, 93E10 Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Hamiltonian Hopping for Efficient Chiral Mode Switching in Encircling Exceptional Points

引用

Physical Review Letters 2020年第18期125卷 187403-187403页

作者： Aodong Li Jianji Dong Jian Wang Ziwei Cheng John S. Ho Dawei Zhang Jing Wen Xu-Lin Zhang C. T. Chan Andrea Alù Cheng-Wei Qiu Lin Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology No. 516 Jungong Road Shanghai 200093 China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun 130012 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong 999077 China Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.

关键词： Open quantum systems & decoherence PT-symmetric quantum mechanics Photonics Waveguides

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Editor-in-Chief

引用

IEEE Access 2024年 12卷 i-xi页

作者： Derek Abbott Sonia Aissa Anuradha Annaswamy Nirwan Ansari Jun Cai Kun-Shan Chen Yixin Chen J.-C. Chiao Ayman El-Baz Shaikh Fattah Hoay Gooi Maya Gokhale Chih-Lin Ⅰ Giuseppe Iannaccone Yumi Iwashita Bruce Jacob Abbas Jamalipour Bahram Javidi Weihua Jiang Okyay Kaynak Hulya Kirkici Germano Lambert-Torres Shengtao Li Michal Mrozowski Michele Nappi Dalma Novak Hugo Proença Yi Qian Zhihua Qu Susanto Rahardja Mehrdad Saif Ali Serpengüzel Peter H. Siegel Ashitey Trebi-Ollennu Daniela Tuninetti Ge Wang Keith A. Wear Andreas Weisshaar C. Patrick Yue Enrico Zio School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Institut National de la Recherche Scientifique (INRS) Québec City Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Concordia University Montreal Canada Guilin University of Technology Guilin China Washington University St. Louis MO USA Southern Methodist University Dallas TX USA University of Louisville/Bioengineering Department Louisville KY USA Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Nanyang Technological University Jurong West Singapore Lawrence Livermore National Laboratory Livermore CA USA China Mobile Research Institute Beijing China University of Pisa Pisa Italy NASA-JPL California Institute of Technology Pasadena CA USA Florida Polytechnic University Lakeland FL USA The University of Sydney Sydney NSW Australia University of Connecticut Storrs CT USA Nagaoka University of Technology Nagaoka Niigata Japan Department of Electrical and Electronic Engineering Boğaziçi University Istanbul Turkey University of South Alabama Mobile AL USA Gnarus Institute Itajuba Brazil Xi’an Jiaotong University Xi’an China Gdansk University of Technology Gdansk Poland University of Salerno Fisciano Italy Octane Wireless Hanover MD USA University of Beira Interior Covilhã Portugal Department of Electrical and Computer Engineering University of NebraskaLincoln Lincoln NE USA University of Central Florida Orlando Florida USA Singapore Institute of Technology 10 Dover Dr Singapore Infocomm Technology Cluster 10 Dover Dr Singapore University of Windsor Windsor Canada Department of Physics Koç University Istanbul Turkey California Institute of Technology (CalTech) Pasadena California USA NASA-JPL Mobility and Robotics Systems S

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An In-Depth Look at TOI-3884b: a Super-Neptune Transiting a M4 Dwarf with Persistent Star Spot Crossings

arXiv

引用

arXiv 2023年

作者： Libby-Roberts, Jessica E. Schutte, Maria Hebb, Leslie Kanodia, Shubham Cañas, Caleb I. Stefánsson, Guðmundur Lin, Andrea S.J. Mahadevan, Suvrath Parts, Winter Powers, Luke Wisniewski, John Bender, Chad F. Cochran, William D. Diddams, Scott A. Everett, Mark E. Gupta, Arvind F. Halverson, Samuel Kobulnicky, Henry A. Kowalski, Adam F. Larsen, Alexander Monson, Andrew Ninan, Joe P. Parker, Brock A. Ramsey, Lawrence W. Robertson, Paul Schwab, Christian Swaby, Tera N. Terrien, Ryan C. Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Homer L. Dodge Department of Physics and Astronomy University of Oklahoma 440 W. Brooks Street NormanOK73019 United States Physics Department Hobart and William Smith Colleges 300 Pulteney Street GenevaNY14456 United States Department of Astronomy Cornell University 245 East Ave IthacaNY14850 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Department of Physics & Astronomy George Mason University 4400 University Drive MS 3F3 FairfaxVA22030 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States McDonald Observatory Department of Astronomy The University of Texas Austin United States Center for Planetary Systems Habitability The University of Texas Austin United States Electrical Computer & Energy Engineering University of Colorado 425 UCB BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Time and Frequency Division National Institute of Standards and Technology 325 Broadway BoulderCO80305 United States NSF’s National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 Uni

We perform an in-depth analysis of the recently validated TOI-3884 system, an M4 dwarf star with a transiting super-Neptune. Using high precision light curves obtained with the 3.5 m Apache Point Observatory and radial velocity observations with the Habitable-zone Planet Finder (HPF), we derive a planetary mass of 32.6+7.3−7.4M⨁ and radius of 6.4 ± 0.2 R⨁. We detect a distinct star spot crossing event occurring just after ingress and spanning half the transit for every transit. We determine this spot feature to be wavelength-dependent with the amplitude and duration evolving slightly over time. Best-fit star spot models show that TOI-3884b possesses a misaligned (λ = 75 ± 10◦) orbit which crosses a giant pole-spot. This system presents a rare opportunity for studies into the nature of both a misaligned super-Neptune and spot evolution on an active mid-M dwarf. © 2023, CC BY.

关键词： Orbits

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Exponential convergence for distributed smooth optimization under the restricted secant inequality condition

arXiv

引用

arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Johansson, Karl H. Chai, Tianyou Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the distributed smooth optimization problem in which the objective is to minimize a global cost function formed by a sum of local smooth cost functions, by using local information exchange. The standard assumption for proving exponential/linear convergence of first-order methods is the strong convexity of the cost functions, which does not hold for many practical applications. In this paper, we first show that the continuous-time distributed primal-dual gradient algorithm converges to one global minimizer exponentially under the assumption that the global cost function satisfies the restricted secant inequality condition. This condition is weaker than the strong convexity condition since it does not require convexity and the global minimizers are not necessary to be unique. We then show that the discrete-time distributed primal-dual algorithm constructed by using the Euler's approximation method converges to one global minimizer linearly under the same condition. The theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Continuous time systems

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

引用

arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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