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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

SVM-based deep stacking networks

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arXiv 2019年

作者： Wang, Jingyuan Feng, Kai Wu, Junjie MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science Engineering Beihang University Beijing100191 China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations School of Economics and Management Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

Progress in multibaryon spectroscopy

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arXiv 2019年

作者： Berkowitz, Evan Brantley, David McElvain, Kenneth Walker-Loud, André Chang, Chia Cheng Clark, M.A. Kurth, Thorsten Joó, Bálint Monge-Camacho, Henry Nicholson, Amy Rinaldi, Enrico Vranas, Pavlos Institut für Kernphysik Institute for Advanced Simulation Forschungszentrum Jülich Nuclear Science Division Lawrence Berkeley National Laboratory RIKEN NVIDIA Corporation National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory Jefferson Lab Department of Physics and Astronomy University of North Carolina RIKEN BNL Research Center Nuclear and Chemical Sciences Division Lawrence Livermore National Laboratory

Anchoring the nuclear interaction in QCD is a long-outstanding problem in nuclear physics. While the lattice community has made enormous progress in mesonic physics and single nucleon physics, continuum-limit physical-point multi-nucleon physics has remained out of reach. I will review CalLat’s strategy for multi-nucleon spectroscopy and our latest results. Copyright © 2019, The Authors. All rights reserved.

关键词：

The stochastic feynman-hellmann method

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arXiv 2019年

作者： Gambhir, Arjun Singh Brantley, David Vranas, Pavlos Berkowitz, Evan Chang, Chia Cheng Clark, M.A. Kurth, Thorsten Walker-Loud, André Monahan, Chris Nicholson, Amy Nuclear and Chemical Sciences Division Lawrence Livermore National Laboratory Institut fur Kernphysik Institute for Advanced Simulation Forschungszentrum Julich RIKEN 2-1 Hirosawa NVIDIA Corporation National Energy Research Scientific Computing Center Nuclear Science Division Lawrence Berkeley National Laboratory Institute for Nuclear Theory University of Washington Department of Physics and Astronomy University of North Carolina

The Feynman-Hellmann method, as implemented by Bouchard et al. [1612.06963], was recently employed successfully to determine the nucleon axial charge. A limitation of the method was the restriction to a single operator and a single momentum during the computation of each "Feynman-Hellmann" propagator. By using stochastic techniques to estimate the all-to-all propagator, we relax this constraint and demonstrate the successful implementation of this new method. We show reproduction of the axial charge on a test ensemble and non-zero momentum transfer points of the axial and vector form factors. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

Jacobi Spectral Collocation Method Based on Lagrange Interpolation Polynomials for Solving Nonlinear Fractional Integro-Differential Equations

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Advances in Applied Mathematics and Mechanics 2018年第6期10卷 1440-1458页

作者： Xingfa Yang Yin Yang Yanping Chen Jie Liu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsha 410082HunanChina Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtan 411105HunanChina School of Mathematical Sciences South China Normal UniversityGuangzhou 510631GuangdongChina Center for Research on Leading Technology of Special Equipment School of Mechanical and Electric EngineeringGuangzhou UniversityGuangzhou 510006GuangdongChina

In this paper,we study a class of nonlinear fractional integro-differential equations,the fractional derivative is described in the Caputo *** the properties of the Caputo derivative,we convert the fractional integro-differential equations into equivalent integral-differential equations of Volterra type with singular kernel,then we propose and analyze a spectral Jacobi-collocation approximation for nonlinear integro-differential equations of Volterra *** provide a rigorous error analysis for the spectral methods,which shows that both the errors of approximate solutions and the errors of approximate fractional derivatives of the solutions decay exponentially in L^(∞)-norm and weighted L^(2)-norm.

关键词： Spectral method nonlinear fractional derivative Volterra integro-differential equations Caputo derivative

Coupled-channel approach to Tcc+ including three-body effects

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arXiv 2021年

作者： Du, Meng-Lin Baru, Vadim Dong, Xiang-Kun Filin, Arseniy Guo, Feng-Kun Hanhart, Christoph Nefediev, Alexey Nieves, Juan Wang, Qian Institutos de Investigación de Paterna Apartado 22085 Valencia46071 Spain Institut für Theoretische Physik II Ruhr-Universität Bochum BochumD-44780 Germany Institute for Theoretical and Experimental Physics NRC Kurchatov Institute Moscow117218 Russia CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China Institute for Advanced Simulation Institut für Kernphysik Jülich Center for Hadron Physics Forschungszentrum Jülich JülichD-52425 Germany P.N. Lebedev Physical Institute The Russian Academy of Sciences Leninskiy Prospect 53 Moscow119991 Russia Moscow Institute of Physics and Technology Institutsky lane 9 Moscow Region Dolgoprudny141700 Russia Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou510006 China Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China

A coupled-channel approach is applied to the charged tetraquark state Tcc+ recently discovered by the LHCb Collaboration. The parameters of the interaction are fixed by a fit to the observed line shape in the three-body D0D0π+ channel. Special attention is paid to the three-body dynamics in the Tcc+ due to the finite life time of the D∗. An approach to the Tcc+ is argued to be self-consistent only if both manifestations of the three-body dynamics, the pion exchange between the D and D∗ mesons and the finite D∗ width, are taken into account simultaneously to ensure that three-body unitarity is preserved. This is especially important to precisely extract the pole position in the complex energy plane whose imaginary part is very sensitive to the details of the coupled-channel scheme employed. The D0D0 and D0D+ invariant mass distributions, predicted based on this analysis, are in good agreement with the LHCb data. The low-energy expansion of the D∗D scattering amplitude is performed and the low-energy constants (the scattering length and effective range) are extracted. The compositeness parameter of the Tcc+ is found to be close to unity, which implies that the Tcc+ is a hadronic molecule generated by the interactions in the D∗+D0 and D∗0D+ channels. Employing heavy-quark spin symmetry, an isoscalar D∗D∗ molecular partner of the Tcc+ with JP = 1+ is predicted under the assumption that the DD∗-D∗D∗ coupled-channel effects can be neglected. Copyright © 2021, The Authors. All rights reserved.

关键词： Hadrons

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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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

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

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arXiv 2023年

作者： Alexeev, Yuri Amsler, Maximilian Barroca, Marco Antonio Bassini, Sanzio Battelle, Torey Camps, Daan Casanova, David Choi, Young Jai Chong, Frederic T. Chung, Charles Codella, Christopher Córcoles, Antonio D. Cruise, James Di Meglio, Alberto Duran, Ivan Eckl, Thomas Economou, Sophia Eidenbenz, Stephan Elmegreen, Bruce Fare, Clyde Faro, Ismael Fernández, Cristina Sanz Ferreira, Rodrigo Neumann Barros Fuji, Keisuke Fuller, Bryce Gagliardi, Laura Galli, Giulia Glick, Jennifer R. Gobbi, Isacco Gokhale, Pranav de la Puente Gonzalez, Salvador Greiner, Johannes Gropp, Bill Grossi, Michele Gull, Emanuel Healy, Burns Hermes, Matthew R. Huang, Benchen Humble, Travis S. Ito, Nobuyasu Izmaylov, Artur F. Javadi-Abhari, Ali Jennewein, Douglas Jha, Shantenu Jiang, Liang Jones, Barbara de Jong, Wibe Albert Jurcevic, Petar Kirby, William Kister, Stefan Kitagawa, Masahiro Klassen, Joel Klymko, Katherine Koh, Kwangwon Kondo, Masaaki Kürkçüoglu, Doga Murat Kurowski, Krzysztof Laino, Teodoro Landfield, Ryan Leininger, Matt Leyton-Ortega, Vicente Li, Ang Lin, Meifeng Liu, Junyu Lorente, Nicolas Luckow, Andre Martiel, Simon Martin-Fernandez, Francisco Martonosi, Margaret Marvinney, Claire Medina, Arcesio Castaneda Merten, Dirk Mezzacapo, Antonio Michielsen, Kristel Mitra, Abhishek Mittal, Tushar Moon, Kyungsun Moore, Joel Mostame, Sarah Motta, Mario Na, Young-Hye Nam, Yunseong Narang, Prineha Ohnishi, Yu-Ya Ottaviani, Daniele Otten, Matthew Pakin, Scott Pascuzzi, Vincent R. Pednault, Edwin Piontek, Tomasz Pitera, Jed Rall, Patrick Ravi, Gokul Subramanian Robertson, Niall Rossi, Matteo A.C. Rydlichowski, Piotr Ryu, Hoon Samsonidze, Georgy Sato, Mitsuhisa Saurabh, Nishant Sharma, Vidushi Sharma, Kunal Shin, Soyoung Slessman, George Steiner, Mathias Sitdikov, Iskandar Suh, In-Saeng Switzer, Eric D. Tang, Wei Thompson, Joel Todo, Synge Tran, Minh C. Trenev, Dimitar Trott, Christian Tseng, Huan-Hsin Tubman, Norm M. Tureci, Esin Valiñas, David García Vallecorsa, Sofia Wever, Christopher Wojciechowski, Konrad Wu, Xiaodi Yoo, Shinjae Yoshioka, Argonne National Laboratory LemontIL60439 United States Corporate Sector Research and Advance Engineering Robert Bosch GmbH Robert-Bosch-Campus 1 RenningenD-71272 Germany IBM Research RJ Rio de Janeiro20031-170 Brazil Centro Brasileiro de Pesquisas Físicas RJ Rio de Janeiro22290-180 Brazil CINECA via Magnanelli 6/3 BO Casalecchio di Reno40033 Italy Arizona State University TempeAZ United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Euskadi Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Physics Yonsei University Seoul03722 Korea Republic of University of Chicago ChicagoIL United States IBM Quantum IBM T.J. Watson Research Center Yorktown Heights NY10598 United States Cambridge Consultants part of Capgemini Invent Cambridge United Kingdom Geneva1211 Switzerland Virginia Tech BlacksburgVA24061 United States Los Alamos National Laboratory Los AlamosNM87545 United States Osaka University Osaka560-8531 Japan Department of Chemistry Chicago Center for Theoretical Chemistry University of Chicago ChicagoIL United States Fraunhofer ITWM Rheinland-Pfalz Kaiserslautern67663 Germany Infleqtion ChicagoIL60622 United States University of Illinois Urbana-Champaign United States University of Michigan Ann ArborMI48109 United States Research Office Oak Ridge National Laboratory One Bethel Valley Road Oak RidgeTN37831 United States Hyogo Kobe650-0047 Japan Chemical Physics Theory Group Department of Chemistry University of Toronto TorontoONM5S 3H6 Canada Department of Physical and Environmental Sciences University of Toronto Scarborough TorontoONM1C 1A4 Canada Brookhaven National Laboratory UptonNY United States Rutgers University New BrunswickNJ United States IBM Quantum Almaden Research Center San JoseCA95120 United States Applied Mathematics and Computational Research Division

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Characterization (materials science)

Effective range expansion for narrow near-threshold resonances

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arXiv 2021年

作者： Baru, Vadim Dong, Xiang-Kun Du, Meng-Lin Filin, Arseniy Guo, Feng-Kun Hanhart, Christoph Nefediev, Alexey Nieves, Juan Wang, Qian Institut für Theoretische Physik II Ruhr-Universität Bochum BochumD-44780 Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Zhong Guan Cun East Street 55 Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China Institutos de Investigación de Paterna C/Catedrático José Beltrán 2 Paterna ValenciaE-46980 Spain Institute for Advanced Simulation Institut für Kernphysik Jülich Center for Hadron Physics Forschungszentrum Jülich JülichD-52425 Germany P.N. Lebedev Physical Institute the Russian Academy of Sciences Leninskiy Prospect 53 Moscow119991 Russia Moscow Institute of Physics and Technology 141700 Institutsky lane 9 Dolgoprudny Moscow Region Russia Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou510006 China Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China

We discuss some general features of the effective range expansion, the content of its parameters with respect to the nature of the pertinent near-threshold states and the necessary modifications in the presence of coupled channels, isospin violations and unstable constituents. As illustrative examples, we analyse the properties of the χc1(3872) and Tcc+ states supporting the claim that these exotic states have a predominantly molecular nature. Copyright © 2021, The Authors. All rights reserved.

关键词： Expansion