检索结果-内蒙古大学图书馆

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kangkan Wang Jin Xie Guofeng Zhang Lei Liu Jian Yang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China ZJU-SenseTime Joint Lab of 3D Vision

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

This work addresses the problem of 3d human pose and shape estimation from a sequence of point clouds. Existing sequential 3d human shape estimation methods mainly focus on the template model fitting from a sequence of depth images or the parametric model regression from a sequence of RGB images. In this paper, we propose a novel sequential 3d human pose and shape estimation framework from a sequence of point clouds. Specifically, the proposed framework can regress 3d coordinates of mesh vertices at different resolutions from the latent features of point clouds. Based on the estimated 3d coordinates and features at the low resolution, we develop a spatial-temporal mesh attention convolution (MAC) to predict the 3d coordinates of mesh vertices at the high resolution. By assigning specific attentional weights to different neighboring points in the spatial and temporal domains, our spatial-temporal MAC can capture structured spatial and temporal features of point clouds. We further generalize our framework to the real data of human bodies with a weakly supervised fine-tuning method. The experimental results on SURREAL, Human3.6M, dFAUST and the real detailed data demonstrate that the proposed approach can accurately recover the 3d body model sequence from a sequence of point clouds.

关键词： Three-dimensional displays Solid modeling Shape Biological system modeling Estimation Spatial resolution Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Randomized comparison of AI enhanced 3d printing and traditional simulations in hepatobiliary surgery

引用

NPJ digital medicine 2025年第1期8卷 293页

作者： Linqian Li Shujie Cheng Jinghua Li Jihong Yang Hongguang Wang Bin dong Xiaoping Yin Hongyun Shi Shuo Gao Feng Gu Zhe Han Zhi Chen Jisen Zhao Quan Zhang Jie-Zhi Cheng Yuan Wang Fengwei Tan Ke Zhang Surgical Department Affiliated Hospital of Hebei University Baoding Hebei Province China. Basic Research Key Laboratory of General Surgery for Digital Medicine Baoding Hebei Province China. Institute of Life Science and Green Development Hebei University Baoding Hebei Province China. 3D Image and 3D Printing Center Affiliated Hospital of Hebei University Baoding Hebei Province China. Surgical Department Affiliated Hospital of Hebei University Baoding Hebei Province China. chengshuj@***. Basic Research Key Laboratory of General Surgery for Digital Medicine Baoding Hebei Province China. chengshuj@***. Institute of Life Science and Green Development Hebei University Baoding Hebei Province China. chengshuj@***. Surgical Department Affiliated Hospital of Hebei University Baoding Hebei Province China. ***@***. Basic Research Key Laboratory of General Surgery for Digital Medicine Baoding Hebei Province China. ***@***. Institute of Life Science and Green Development Hebei University Baoding Hebei Province China. ***@***. Department of Hepatobiliary Surgery Affiliated Hospital of Hebei University Baoding Hebei Province China. Department of Hepatobiliary Surgery National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. wanghongguang301@***. Information Center of Affiliated Hospital of Hebei University Baoding Hebei Province China. United Imaging Intelligence Shanghai China. Department of Surgery National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. tanfengwei@***. Surgical Department Affiliated Hospital of Hebei University Baoding Hebei Province China. 93391@***. Basic Research Key Laboratory of General Surgery for Digital Medicine Baoding Hebei Province China. 93391@qq.com. Institute of Life Science and Green Development Hebei University Ba

We employed a three-phase approach, culminating in a randomized controlled trial, to assess the efficacy of 3d-printed liver models in hepatobiliary surgical planning. Phase one involved developing and selecting 35 optimal 3dP models based on timeliness, cost, precision, and alignment with digital simulations. Phase two utilized deep learning algorithms to optimize the 3d reconstruction process, significantly enhancing efficiency and accuracy compared to manual segmentation. In phase three, a randomized controlled trial with 64 patients compared surgical outcomes between those planned with AI-enhanced physical 3dP models and those with traditional digital simulations. Results demonstrated that 3dP models were produced rapidly (3.52 h at $152 each) with high precision, AI-assisted reconstruction reduced processing time (303.5 vs. 557 min), and patients using AI-enhanced physical 3dP models experienced less intraoperative blood loss. Integrating deep learning with 3d printing offers a cost-effective, scalable method to enhance surgical planning and outcomes in hepatobiliary surgery.

关键词：

来源：评论

学校读者我要写书评

暂无评论

3d and 4d Printing of Electromagnetic Metamaterials

引用

Engineering 2024年

作者： Ruxuan Fang Xinru Zhang Bo Song Zhi Zhang Lei Zhang Jun Song Yonggang Yao Ming Gao Kun Zhou Pengfei Wang Jian Lu Yusheng Shi State Key Laboratory of Materials Processing and Die & Mold Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China HP-NTU Digital Manufacturing Corporate Lab School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore 639798 Singapore Singapore Centre for 3D Printing School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore 639798 Singapore China Academy of Aerospace Science and Innovation Beijing 100094 China Department of Mechanical Engineering City University of Hong Kong Hong Kong 999077 China CityU-Shenzhen Futian Research Institute Shenzhen 518045 China

Electromagnetic devices have been widely used in the fields of information communication, medical treatment, electrical engineering, and national defense, and their properties are strongly dependent on the constituent electromagnetic materials. Conversely, electromagnetic metamaterials (EMMs), which are artificially engineered with distinctive electromagnetic properties, can overcome the limitations of natural materials owing to their structural advantages. Three-dimensional (3d) printing is the most effective technique for fabricating EMM devices with different geometric parameters and associated properties. However, conventional 3d-printed EMM devices may lack manufacturing flexibility and environmental adaptability to different physical stimuli, such as electric and magnetic fields. Four-dimensional (4d) printing is an ideal technique for schemes to integrate structural design with intelligent materials environmentally adaptive to external fields, for example, the printed components can change shape under electric stimulation. Given the rapid advancements in the EMM field, this paper first reviews typical EMM devices, their design theories, and underlying principles. Subsequently, it presents various EMM structural topologies and manufacturing technologies, emphasizing the feasibility of combining 3d and 4d printing. In addition, we highlight the important applications of EMMs and their future trends and the challenges associated with functional EMMs and additive manufacturing.

关键词： 3d printing 4d printing Metamaterials Electromagnetic properties Invisibility cloak

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Coupling effects of high magnetic field and annealing on the microstructure evolution and mechanical properties of additive manufactured Ti–6Al–4V” [Mater. Sci. Eng. A 824 (2021) 141815]

引用

Materials Science and Engineering: A 2021年 825卷

作者： Peixuan Li Ying Zhang William Yi Wang Yixuan He Jiaxiang Wang Minxian Han Jun Wang Liang Zhang Ruifeng Zhao Hongchao Kou Qian Ma Jinshan Li State Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an Shaan Xi Provence 710072 China Shanghai Engineering Research Center of 3D Printing Materials Shanghai 200437 China Center for Additive Manufacturing School of Engineering RMIT University Melbourne VIC 3000 Australia

来源：评论

学校读者我要写书评

暂无评论

Additive manufacturing of hydroxyapatite and its composite materials: A review

引用

Journal of Micromechanics and Molecular Physics 2020年第3期5卷

作者： Yan, Chunze Ma, Gao Chen, Annan Chen, Ying Wu, Jiamin Wang, Wei Yang, Shoufeng Shi, Yusheng State Key Laboratory of Material Processing and Die & Mould Technology School of Material Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Engineering Research Center of Ceramic Materials for Additive Manufacturing Ministry of Education Wuhan 430074 China Zhejiang Huake 3D Technology Co.Ltd Wenzhou 325004 China Department of Mechanical Engineering KU Leuven (University of Leuven) Leuven 3001 Belgium

Hydroxyapatite (HA) is a promising biomaterial for tissue engineering scaffolds due to its similar performance and composition to natural bone. However, the brittleness and poor toughness of pure HA limit its clinical application. Therefore, a lot of HA composites have been prepared to improve their mechanical performance. Fabricating complex and customized tissue engineering HA scaffolds have a very high requirement for manufacturing processes. It is difficult to fabricate ideal HA porous structures for artificial bone implants using traditional manufacturing processes, such as plasma spraying–sintering, and injection forming. Additive manufacturing (AM) could make three-dimensional physical parts with complex structures directly from computer-aided-design (CAd) models in a layer-by-layer way, and therefore show unique advantages in fabricating bone tissue engineering scaffolds with complex external shape and internal microporous structures. This paper reviews the state of the art for the preparation and AM process of HA and its composite materials, and raises the prospects for this research field. © World Scientific. All rights reserved.

关键词： Additive manufacturing Biomaterial HA composite materials Hydroxyapatite Tissue engineering scaffold

来源：评论

学校读者我要写书评

暂无评论

Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries (vol 13, 2728, 2023)

引用

SCIENTIFIC REPORTS 2023年第1期13卷 1-10页

作者： Sendra-Balcells, Carla Campello, Victor M. Torrents-Barrena, Jordina Ahmed, Yahya Ali Elattar, Mustafa Ohene-Botwe, Benard Nyangulu, Pempho Stones, William Ammar, Mohammed Benamer, Lamya Nawal Kisembo, Harriet Nalubega Sereke, Senai Goitom Wanyonyi, Sikolia Z. Temmerman, Marleen Gratacos, Eduard Bonet, Elisenda Eixarch, Elisenda Mikolaj, Kamil Tolsgaard, Martin Gronnebaek Lekadir, Karim Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain HP Inc. Barcelona Spain Obstetrics and Gynecology Department School of Medicine Suez University Suez Egypt Medical Imaging and Image Processing Center of Informatics Science Nile University Sheikh Zayed City Egypt Research and Development Division Intixel Cairo Egypt Department of Radiography School of Biomedical and Allied Health Sciences College of Health Sciences University of Ghana Accra Ghana Division of Midwifery and Radiography School of Health and Psychological Sciences University of London London UK Kamuzu University of Health Sciences Blantyre Malawi Department of Electrical Engineering Systems Laboratory of Engineering System and Telecommunication University of M’Hamed Bougara Boumerdes Algiers Algeria Obstetrics and Gynecology Department School of Medicine Algiers University Algiers Algeria Department of Radiology Mulago National Referral and Teaching Hospital Kampala Uganda Department of Radiology and Radiotherapy School of Medicine Makerere University College of Health Sciences Kampala Uganda Department of Obstetrics and Gynaecology Aga Khan University Hospital 3rd Parklands Avenue Nairobi Kenya Centre of Excellence in Women and Child Health Aga Khan University Nairobi Kenya BCNatal Fetal Medicine Research Center Hospital Clínic and Hospital Sant Joan de Déu Universitat de Barcelona Barcelona Spain Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain Centre for Biomedical Research on Rare Diseases (CIBER-ER) Barcelona Spain Barcelona Tech Universitat Politècnica de Catalunya Barcelona Spain Copenhagen Academy for Medical Education and Simulation and Department of Obstetrics Rigshospitalet Copenhagen Denmark

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in denmark in optimal conditions with 1008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to$0.92 \pm 0.04$and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the us

关键词：

来源：评论

学校读者我要写书评

暂无评论

Numerical modeling and experimental validation of thermal history and microstructure for additive manufacturing of an Inconel 718 product

引用

Progress in Additive Manufacturing 2018年第1-2期3卷 15-32页

作者： Promoppatum, Patcharapit Yao, Shi-Chune Pistorius, P. Chris Rollett, Anthony d. Coutts, Peter J. Lia, Frederick Martukanitz, Richard Department of Mechanical Engineering Carnegie Mellon University Pittsburgh PA United States Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh PA United States Center for Innovative Materials Processing through Direct Digital Deposition (CIMP-3D) Applied Research Laboratory Pennsylvania State University State College PA United States

A product-scale part was additively manufactured from Inconel 718 by laser powder-bed fusion. The thermal and microstructural behavior was experimentally examined to reveal physical characteristics while a high fidelity numerical model was developed to predict characteristics throughout the part volume. Three physical characteristics were considered in the present study: (1) thermal evolution during the build, (2) melt pool configuration, and (3) the final microstructure as-deposited. Thermal simulations were performed by finite element calculation while the microstructure was predicted from the calculated thermal history and existing theoretical correlations. Predicted results were thoroughly confirmed through comparison with experimental measurements. Ultimately, the present work aims to illustrate the integration of the computational method as tools to provide manufacturing qualification for part production by AM. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Additive manufacturing Finite element modeling Inconel 718 Large-scale modeling Microstructure Selective laser melting

来源：评论

学校读者我要写书评

暂无评论

Technology Roadmap for Flexible Sensors

引用

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex dagdeviren, Canan dauskardt, Reinhold H. di, Chong-an dickey, Michael d. duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, daniil Khademhosseini, Ali Kim, dae-Hyeong Kim, Il-doo Kireev, dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

来源：评论

学校读者我要写书评

暂无评论

Square Kilometre Array Science data Challenge 3a: foreground removal for an EoR experiment

arXiv

引用

arXiv 2025年

作者： Bonaldi, A. Hartley, P. Braun, R. Purser, S. Acharya, A. Ahn, K. Aparicio Resco, M. Bait, O. Bianco, M. Chakraborty, A. Chapman, E. Chatterjee, S. Chege, K. Chen, X. Chen, Z. Conaboy, L. Cruz, M. darriba, L. de Santis, M. denzel, P. diao, K. Feron, J. Finlay, C. Gehlot, B. Ghosh, S. Giri, S.K. Grumitt, R. Hong, S.E. Ito, T. Jiang, M. Jordan, C. Kim, S. Kim, M. Kim, J. Krishna, S.P. Kulkarni, A. López-Caniego, M. Labadie-García, I. Lee, H. Lee, d. Lee, N. Line, J. Liu, Y. Mao, Y. Mazumder, A. Mertens, F.G. Munshi, S. Nasirudin, A. Ni, S. Nistane, V. Norregaard, C. Null, d. Offringa, A. Oh, M. Oh, S.-H. Parkinson, d. Pritchard, J. Ruiz-Granda, M. Salvador López, V. Shan, H. Sharma, R. Trott, C. Yoshiura, S. Zhang, L. Zhang, X. Zheng, Q. Zhu, Z. Zuo, S. Akahori, T. Alberto, P. Allys, E. An, T. Anstey, d. Baek, J. Basavraj Brackenhoff, S. Browne, P. Ceccotti, E. Chen, H. Chen, T. Choudhuri, S. Choudhury, M. Coles, J. Cook, J. Cornu, d. Cunnington, S. das, S. de Lera Acedo, E. delouis, J.-M. deng, F. ding, J. Elahi, K.M.A. Fernandez, P. Fernández, C. Alcázar, A. Fernández Galluzzi, V. Gao, L.-Y. Garain, U. Garrido, J. Gendron-Marsolais, M.-L. Gessey-Jones, T. Ghorbel, H. Gong, Y. Guo, S. Hasegawa, K. Hayashi, T. Herranz, d. Holanda, V. Holloway, A.J. Hothi, I. Höfer, C. Jelić, V. Jiang, Y. Jiang, X. Kang, H. Kim, J.-Y. Koopmans, L.V. Lacroix, R. Lee, E. Leeney, S. Levrier, F. Li, Y. Liu, Y. Ma, Q. Meriot, R. Mesinger, A. Mevius, M. Minoda, T. Miville-deschenes, M.-A. Moldon, J. Mondal, R. Murmu, C. Murray, S. Nirmala, S.R. Niu, Q. Nunhokee, C. O’Hara, O. Pal, S.K. Pal, S. Park, J. Parra, M. Patra, N.N. Pindor, B. Remazeilles, M. Rey, P. Rubino-Martin, J.A. Saha, S. Selvaraj, A. Semelin, B. Shah, R. Shao, Y. Shaw, A.K. Shi, F. Shimabukuro, H. Singh, G. Sohn, B.W. Stagni, M. Starck, J.-L. Sui, C. Swinbank, J.d. Sánchez, J. SKA Observatory Jodrell Bank Lower Whitington MacclesfieldSK11 9FT United Kingdom Max-Planck Institute for Astrophysics Karl-Schwarzschild-Straße 1 Garching85748 Germany Department of Earth Sciences Chosun University Gwangju61452 Korea Republic of Universidad Europea de Madrid Madrid28670 Spain Observatoire de Sauverny Versoix1290 Switzerland Department of Physics Trottier Space Institute McGill University 3600 rue University MontréalQCH3A 2T8 Canada School of Physics and Astronomy The University of Nottingham University Park NottinghamNG7 2RD United Kingdom Department of Physics and Astronomy University of the Western Cape Cape Town Bellville7535 South Africa Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Astronomy The University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Avda. de los Castros s/n SantanderE-39005 Spain Glorieta de la Astronomía s/n Granada18008 Spain Saint-Imier Switzerland Centre for Artificial Intelligence ZHAW Zurich University of Applied Sciences Technikumstrasse 71 Winterthur8400 Switzerland Department of Astronomy Tsinghua University Beijing100084 China Département de Physique Théorique Center for Astroparticle Physics Université de Geneve 24 quai Ernest Ansermet Geneve 41211 Switzerland Nordita KTH Royal Institute of Technology Stockholm University Hannes Alfv´ens väg 12 StockholmSE-106 91 Sweden Korea Astronomy and Space Science Institute Daejeon34055 Korea Republic of Astronomy Campus University of Science and Technology Daejeon34055 Korea Republic of Kumamoto University International Research Organization for Advanced Science and Technology 2-39-1 Kurokami Chuo-ku Kumamoto860-8555 Japan National Key Laboratory of Radar Signal Processing

We present and analyse the results of the Science data challenge 3a (SdC3a, https://***/challenges/ foregrounds), an EoR foreground-removal community-wide exercise organised by the Square Kilometre Array Observatory (SKAO). The challenge ran for 8 months, from March to October 2023. Participants were provided with realistic simulations of SKA-Low data between 106 MHz and 196 MHz, including foreground contamination from extragalactic as well as Galactic emission, instrumental and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned from all corruptions, and the corresponding confidence levels. Here we describe the approaches taken by the 17 teams that completed the challenge, and we assess their performance using different metrics. The challenge results provide a positive outlook on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations. The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some cases there are some significant outliers. The smallest residual overall is 4.2+−204.2 × 10−4 K2h−3cMpc3 across all considered scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error being typically underestimated, sometimes very significantly. The most accurate error bars account for 60 ± 20% of the true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and improve error estimation. © 2025, CC BY.

关键词： Cosmology

来源：评论

学校读者我要写书评

暂无评论

Correction to: Prognostic factors for lymph node metastasis from upper gingival carcinomas

引用

Oral radiology 2022年第3期38卷 397页

作者： Mazen Aldosimani Rinus G Verdonschot Yuri Iwamoto Mitsuhiro Nakazawa Sanjay M Mallya Naoya Kakimoto Satoru Toyosawa Sven Kreiborg Shumei Murakami Department of Oral and Maxillofacial Radiology Osaka University Graduate School of Dentistry Suita Osaka Japan. Division of Radiology Department of Oral Medicine and Diagnostic Sciences College of Dentistry King Saud University Riyadh Kingdom of Saudi Arabia. Department of Oral and Maxillofacial Radiology Institute of Biomedical and Health Sciences Hiroshima University 1-2-3 Kasumi Minami-ku Hiroshima 734-8553 Japan. rinusverdonschot@***. Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. rinusverdonschot@***. Oral and Maxillofacial Surgery II Graduate School of Dentistry Osaka University Suita Japan. Section of Oral and Maxillofacial Radiology UCLA School of Dentistry Los Angeles CA USA. Department of Oral and Maxillofacial Radiology Institute of Biomedical and Health Sciences Hiroshima University 1-2-3 Kasumi Minami-ku Hiroshima 734-8553 Japan. Department of Oral Pathology Osaka University Graduate School of Dentistry Suita Osaka Japan. 3D Craniofacial Image Research Laboratory University of Copenhagen Copenhagen Denmark.

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：