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DPA-2: a large atomic model as a multi-task learner

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

NTIRE 2021 Challenge on Image Deblurring

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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： Image enhancement

Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers

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Cell Stem Cell 2025年

作者： Luo, Tao Liu, Cong Cheng, Tao Zhao, Guo-Qin Huang, Ying Luan, Jing-Yun Guo, Junyu Liu, Xiang Wang, Yi-Fan Dong, Yang Xiao, Yu He, Enhui Sun, Rui-Zhen Chen, Xiuyu Chen, Jiekai Ma, Jun Megason, Sean Ji, Junfeng Xu, Peng-Fei Center of Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital Zhejiang University School of Medicine Zhejiang Hangzhou 310058 China Nanhu Brain-Computer Interface Institute Zhejiang Hangzhou 311100 China Center for Genetic Medicine the Fourth Affiliated Hospital of School of Medicine and International School of Medicine Zhejiang University Yiwu 322000 China Institute of Genetics Zhejiang University School of Medicine Zhejiang Hangzhou 310058 China Women's Hospital Zhejiang University School of Medicine Zhejiang Hangzhou 310006 China Precision Medicine Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou China Department of Histology and Embryology Harbin Medical University Heilongjiang Harbin 150081 China Center for Biomedical Digital Science Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences Guangzhou 510530 China Department of Systems Biology Harvard Medical School 200 Longwood Avenue Boston 02115 MA United States

Precise dorsal-ventral (D-V) patterning of the neural tube (NT) is essential for the development and function of the central nervous system. However, existing models for studying NT D-V patterning and related human diseases remain inadequate. Here, we present organizers derived from pluripotent stem cell aggregate fusion (“ORDER”), a method that establishes opposing BMP and SHH gradients within neural ectodermal cell aggregates. Using this approach, we generated NT organoids with ordered D-V patterning from both zebrafish and human pluripotent stem cells (hPSCs). Single-cell transcriptomic analysis revealed that the synthetic human NT organoids (hNTOs) closely resemble the human embryonic spinal cord at Carnegie stage 12 (CS12) and exhibit greater similarity to human NT than to mouse models. Furthermore, using the hNTO model, we demonstrated the critical role of WNT signaling in regulating intermediate progenitors, modeled TCTN2-related D-V patterning defects, and identified a rescue strategy. © 2025 Elsevier Inc.

关键词： BMP neural tube organoid pluripotent stem cells SHH

A framework and resource for global collaboration in non-human primate neuroscience

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Current Research in Neurobiology 2023年 4卷 100079页

作者： Hartig, Renée Klink, P. Christiaan Polyakova, Zlata Dehaqani, Mohammad-Reza A. Bondar, Igor Merchant, Hugo Vanduffel, Wim Roe, Anna Wang Nambu, Atsushi Thirumala, M. Shmuel, Amir Kapoor, Vishal Gothard, Katalin M. Evrard, Henry C. Basso, Michele A. Petkov, Christopher I. Mitchell, Anna S. Max Planck Institute for Biological Cybernetics Max-Planck-Ring 11 Tübingen 72076 Germany Functional and Comparative Neuroanatomy Laboratory Werner Reichardt Centre for Integrative Neuroscience Eberhard Karls University of Tübingen Otfried-Müller-Straße 25 Tübingen 72076 Germany Department of Psychiatry and Psychotherapy University Medical Center Johannes Gutenberg-University Mainz 55131 Germany Nathan S. Kline Institute for Psychiatric Research Center for Biomedical Imaging and Neuromodulation Orangeburg New York 10962 United States Department of Vision and Cognition Netherlands Institute for Neuroscience Royal Netherlands Academy of Arts and Sciences Meibergdreef 47 Amsterdam 1105 BA Netherlands Laboratory of Visual Brain Therapy Sorbonne Université Institut National de la Santé et de la Recherche Médicale Centre National de la Recherche Scientifique Institut de la Vision Paris F-75012 France Experimental Psychology Helmholtz institute Utrecht University Heidelberglaan 1 Utrecht 3584 CS Netherlands Center for Human Nature Artificial Intelligence and Neuroscience Hokkaido University Sapporo 060-0812 Japan School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Brain and Cognitive Sciences Cell Science Research Center Royan Institute for Stem Cell Biology and Technology ACECR Tehran Iran Institute of Higher Nervous Activity and Neurophysiology Russian Academy of Sciences Moscow Russian Federation Institute of Neurobiology Universidad Nacional Autónoma de México Campus Juriquilla Department of Neurophysiology Querétaro 76230 Mexico Laboratory for Neuro- and Psychophysiology Department of Neurosciences KU Leuven Medical School Leuven 3000 Belgium Leuven Brain Institute KU Leuven Leuven 3000 Belgium Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlest

As science and technology evolve, there is an increasing need for promotion of international scientific exchange. Collaborations, while offering substantial opportunities for scientists and benefit to society, also present challenges for those working with animal models, such as non-human primates (NHPs). Diversity in regulation of animal research is sometimes mistaken for the absence of common international welfare standards. Here, the ethical and regulatory protocols for 13 countries that have guidelines in place for biomedical research involving NHPs were assessed with a focus on neuroscience. Review of the variability and similarity in trans-national NHP welfare regulations extended to countries in Asia, Europe and North America. A tabulated resource was established to advance solution-oriented discussions and scientific collaborations across borders. Our aim is to better inform the public and other stakeholders. Through cooperative efforts to identify and analyze information with reference to evidence-based discussion, the proposed key ingredients may help to shape and support a more informed, open framework. This framework and resource can be expanded further for biomedical research in other countries. © 2023

关键词： Ethics Macaque Marmoset Regulations Requirements Welfare

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT

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arXiv 2020年

作者： Sun, Liang Mo, Zhanhao Yan, Fuhua Xia, Liming Shan, Fei Ding, Zhongxiang Shao, Wei Shi, Feng Yuan, Huan Jiang, Huiting Wu, Dijia Wei, Ying Gao, Yaozong Gao, Wanchun Sui, He Zhang, Daoqiang Shen, Dinggang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Department of Radiology Third Hospital of Jilin University Changchun China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology WuhanHubei China Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai China Department of Radiology Hangzhou First Peoples Hospital Zhejiang University School of Medicine HangzhouZhejiang China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Department of Radiology Qianjiang Central Hospital Jishou University School of Medicine Chongqing China

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Deep Multi-Model Fusion for Single-Image Dehazing

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Deep Multi-Model Fusion for Single-Image Dehazing

International Conference on computer vision (ICCV)

作者： Zijun Deng Lei Zhu Xiaowei Hu Chi-Wing Fu Xuemiao Xu Qing Zhang Jing Qin Pheng-Ann Heng South China University of Technology Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology CAS The Chinese University of Hong Kong State Key Laboratory of Subtropical Building Science Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Sun Yat-sen University The Hong Kong Polytechnic University CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology CAS

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

This paper presents a deep multi-model fusion network to attentively integrate multiple models to separate layers and boost the performance in single-image dehazing. To do so, we first formulate the attentional feature integration module to maximize the integration of the convolutional neural network (CNN) features at different CNN layers and generate the attentional multi-level integrated features (AMLIF). Then, from the AMLIF, we further predict a haze-free result for an atmospheric scattering model, as well as for four haze-layer separation models, and then fuse the results together to produce the final haze-free image. To evaluate the effectiveness of our method, we compare our network with several state-of-the-art methods on two widely-used dehazing benchmark datasets, as well as on two sets of real-world hazy images. Experimental results demonstrate clear quantitative and qualitative improvements of our method over the state-of-the-arts.

关键词： Atmospheric modeling Predictive models Scattering Computational modeling Neural networks computer vision Solid modeling

Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

VaTEST III: Validation of 8 Potential Super-Earths from TESS Data

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arXiv 2023年

作者： Mistry, Priyashkumar Prasad, Aniket Maity, Mousam Pathak, Kamlesh Gharat, Sarvesh Lekkas, Georgios Bhattarai, Surendra Kumar, Dhruv Lissauer, Jack J. Twicken, Joseph D. Soubkiou, Abderahmane Pozuelos, Francisco J. Jenkins, Jon Horne, Keith Giacalone, Steven Barkaoui, Khalid Timmermans, Mathilde Watkins, Cristilyn N. Sefako, Ramotholo Collins, Karen A. Ciardi, David R. Clark, Catherine A. Safonov, Boris S. Shporer, Avi Schlieder, Joshua E. Benkhaldoun, Zouhair Stockdale, Chris Ziegler, Carl Gilbert, Emily A. Jehin, Emmanuël Murgas, Felipe Crossfield, Ian J.M. Paegert, Martin Lund, Michael B. Narita, Norio Schwarz, Richard P. Goeke, Robert F. Fajardo-Acosta, Sergio B. Howell, Steve B. Tan, Thiam-Guan Barclay, Thomas Kawai, Yugo Department of Physics Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India School of Physics Faculty of Science University of New South Wales Kensington SydneyNSW2052 Australia Department of Earth and Space Science Indian Institute of Space Science and Technology Thiruvananthapuram India Department of Physics Presidency University West Bengal Kolkata700073 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Computer Science International Hellenic University Kavala65404 Greece Department of Physics Indian Institute of Science Education and Research Kolkata West Bengal Mohanpur741246 India Department of Physics National Institute of Technology Tripura Agartala799046 India NASA Ames Research Center Moffett FieldCA94035 United States SETI Institute Mountain ViewCA94043 United States Oukaimeden Observatory High Energy Physics and Astrophysics Laboratory Cadi Ayyad University Marrakech Morocco Astrobiology Research Unit Université de Liège 19C Allée du 6 Août Liège4000 Belgium Glorieta de la Astronomía s/n Granada18008 Spain SUPA Physics and Astronomy University of St. Andrews FifeKY16 9SS United Kingdom Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Earth Atmospheric and Planetary Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Calle Vía Láctea s/n Tenerife La Laguna38200 Spain Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States South African Astronomical Observatory P.O. Box 9 Observatory Cape Town7935 South Africa NASA Exoplanet Science Institute IPAC California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Sternberg Astronomical Institute Lomonosov Moscow State University Russia Depart

NASA’s all-sky survey mission, the Transiting Exoplanet Survey Satellite (TESS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TESS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting Exoplanets using Statistical Tools (VaTEST). Our dedicated effort is focused on the confirmation and characterization of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilization of the statistical validation tool known as TRICERATOPS, we have successfully discovered eight potential super-Earths. These planets bear the designations: TOI-238b (1.61+0.09–0.10 R⊗), TOI-771b (1.42+0.11–0.09 R⊗), TOI-871b (1.66+0.11–0.11 R⊗), TOI-1467b (1.83+0.16–0.15 R⊗), TOI-1739b (1.69+0.10–0.08 R⊗), TOI-2068b (1.82+0.16–0.15 R⊗), TOI-4559b (1.42+0.13–0.11 R⊗), and TOI-5799b (1.62+0.19–0.13 R⊗). Among all these planets, six of them fall within the region known as’keystone planets,’ which makes them particularly interesting for study. Based on the location of TOI-771b and TOI-4559b below the radius valley we characterized them as likely super-Earths, though radial velocity mass measurements for these planets will provide more details about their characterization. It is noteworthy that planets within the size range investigated herein are absent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between Earth and Neptune. © 2023, CC BY.

关键词： Satellites

Position based catheterization and angiography simulation 6

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Position based catheterization and angiography simulation

6th IEEE International Conference on Serious Games and Applications for Health, SeGAH 2018

作者： Zuo, Zhihao Si, Weixin Qian, Weixin Yinling Liao, Xiangyun Heng, Pheng-Ann Department of Information Engineering Wuhan University of Technology Wuhan China Guangdong Provincial Key Laboratory of Machine Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781538662984

Effective and safe performance of cardiovascular interventions requires excellent catheter / guidewire manipulation skills. These skills are currently mainly gained through an apprenticeship on real patients, which may not be safe or cost-effective. computer simulation offers an alternative for core skills training. However, replicating the physical behaviour of real instruments navigated through blood vessels is a challenging task. In this work, we adopt the position based method to fast and robust model the dynamic behavior of catheter / guidewire and contrast medium propagation in vascular interventional radiology. Specifically, we simulate the elastic rods according to the cosserat theory and the contrast medium propagation is controlled by an advection-diffusion equation. Besides, we employ bounding volume hierarchy (BVH) to achieve fast and accurate collision detection between vessels and rods and compute the response with the Coulomb's law. Experimental results demonstrate the visual plausibility, robustness and efficiency of our method in interactive catheterization simulation. © 2018 IEEE.

关键词： Catheters