检索结果-内蒙古大学图书馆

Feature extraction for hyperspectral imagery: The evolution from shallow to deep

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Rasti, Behnood Hong, Danfeng Hang, Renlong Ghamisi, Pedram Kang, Xudong Chanussot, Jocelyn Benediktsson, Jon Atli Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Univ. Grenoble Alpes CNRS Grenoble INP GIPSAlab Grenoble38000 France Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany College of Electrical and Information Engineering Hunan University Changsha410082 China Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Changsha410082 China Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK GrenobleF-38000 France Faculty of Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland

The final version of the paper can be found in IEEE Geoscience and Remote Sensing Magazine. Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral information that can accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to the conventional techniques (the so-called curse of dimensionality) for accurate analysis of hyperspectral images. Feature extraction, as a vibrant field of research in the hyperspectral community, evolved through decades of research to address this issue and extract informative features suitable for data representation and classification. The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques. This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers, willing to explore novel investigations on this challenging topic. In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification. Additionally, this paper compares 15 advanced techniques with an emphasis on their methodological foundations in terms of classification accuracies. Furthermore, to push this vibrant field of research forward an impressive amount of codes and libraries is shared at: https://***/Behnoo

关键词： Feature extraction

Transfer metric learning: Algorithms, applications and outlooks

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Luo, Yong Wen, Yonggang Duan, Ling-Yu Tao, Dacheng School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Electronics Engineering and Computer Science Institute of Digital Media Peking University China UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label information (such as class labels or pair/triplet constraints) to achieve satisfactory performance. However, the label information may be insufficient in real-world applications due to the high-labeling cost, and DML may fail in this case. Transfer metric learning (TML) is able to mitigate this issue for DML in the domain of interest (target domain) by leveraging knowledge/information from other related domains (source domains). Although achieved a certain level of development, TML has limited success in various aspects such as selective transfer, theoretical understanding, handling complex data, big data and extreme cases. In this survey, we present a systematic review of the TML literature. In particular, we group TML into different categories according to different settings and metric transfer strategies, such as direct metric approximation, subspace approximation, distance approximation, and distribution approximation. A summarization and insightful discussion of the various TML approaches and their applications will be presented. Finally, we indicate some challenges and provide possible future directions. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

Effective refractive error coverage in adults: a systematic review and meta-analysis of updated estimates from population-based surveys in 76 countries modelling the path towards the 2030 global target

学校读者我要写书评

暂无评论

The Lancet. Global health 2025年

作者： Rupert Richard Alexander Bourne Maria Vittoria Cicinelli David A Selby Tabassom Sedighi Ian H Tapply Ian McCormick Jost B Jonas Mohammad H Abdianwall Mukharram M Bikbov Tasanee Braithwaite Matthew J Burton Vera Carneiro Robert J Casson Ching-Yu Cheng Nathan G Congdon Catherine Creuzot-Garcher Leon B Ellwein Mohammad Hassan Emamian Akbar Fotouhi Timothy R Fricke David S Friedman João M Furtado Ronnie George Noopur Gupta Xiaotong Han Hassan Hashemi Mingguang He Abba Hydara Aiko Iwase Gyulli Kazakbaeva Rajiv B Khandekar Rohit C Khanna Fatima Kyari Luisa C Luque Srinivas Marmamula Andreas Müller Vinay Nangia Kovin S Naidoo Jacqueline Ramke Paisan Ruamviboonsuk Solange R Salomão Hugh R Taylor Yih C Tham Fotis Topouzis Rohit Varma Lingam Vijaya Ningli Wang Ya Xing Wang Tien Y Wong Hua Yan Seth R Flaxman Stuart Keel Serge Resnikoff Vision & Eye Research Institute Anglia Ruskin University Cambridge UK. Electronic address: rb@rupertbourne.co.uk. Department of Ophthalmology San Raffaele Hospital Milan Italy. Data Science and its Applications (DSA) Research Department German Research Centre for Artificial Intelligence (DFKI) Kaiserslautern Germany. Vision & Eye Research Institute Anglia Ruskin University Cambridge UK. North West Anglia NHS Foundation Trust Peterborough UK. London School of Hygiene & Tropical Medicine London UK. Rothschild Foundation Hospital Paris France. Bayazid Roshan University of Nangarhar Faculty of Medicine Jalalabad Afghanistan. Ufa Eye Research Institute Ufa Russia. Centre of Translational Medicine King's Health Partners London UK. WHO Geneva Switzerland. The University of Adelaide School of Public Health Adelaide Australia. Centre for Innovation & Precision Eye Health Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore. Queen's University Belfast UK. Department of Ophthalmology University Hospital of Dijon Dijon France. National Eye Institute Bethesda MD USA. Shahroud University of Medical Sciences Shahroud Iran. Tehran University of Medical Sciences Tehran Iran. University of New South Wales School of Optometry and Vision Science Sydney NSW Australia. Department of Ophthalmology Harvard Medical School Boston MA USA. University of São Paulo São Paulo Brazil. Medical Research Foundation Chennai India. All India Institute of Medical Sciences New Delhi India. State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangzhou China. NOOR Ophthalmology Research Center Tehran Iran. The University of Melbourne Melbourne VIC Australia. Sheikh Zayed Regional Eye Care Centre Banjul The Gambia. Japan Glaucoma Society Japan. Department of Ophthalmology & Vision Science Faculty of Medicine University of British Columbia Vancouver BC Canada. LV Prasad Eye Institute Hyderabad

BACKGROUND:In 2024, WHO included effective refractive error coverage (eREC) into the results framework of the 14th General Programme of Work, which sets a road map for global health and guides WHO's work between 2025 and 2028. eREC is a measure of both the availability and quality of refractive correction in a population. This study aimed to model global and regional estimates of eREC as of 2023 and evaluate progress towards the WHO global target of a 40 percentage-point absolute increase in eREC by 2030. METHODS:For this systematic review and meta-analysis, the Vision Loss Expert Group analysed data from 237 population-based eye surveys conducted in 76 countries since 2000, comprising 815 273 participants, to calculate eREC (met need / met need + undermet need + unmet need]) and the relative quality gap between eREC and REC ([REC - eREC] / REC × 100, where REC = [met + undermet need] / [met need + undermet need + unmet need]). An expert elicitation process was used to choose covariates for a Bayesian logistic regression model used to estimate eREC by country-age-sex grouping among adults aged 50 years and older. Country-age-sex group estimates were aggregated to provide estimates according to Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) super-regions. FINDINGS:Global eREC was estimated to be 65·8% (95% uncertainty interval [UI] 64·7-66·8) in 2023, 6 percentage points higher than in 2010 (eREC 59·8% [59·4-60·2]). There were marked differences in eREC between GBD super-regions in 2023, ranging from 84·0% (95% UI 83·0-85·0) in high-income countries to 28·3% (26·4-30·4) in sub-Saharan Africa. In all super-regions, eREC was lower in females than males, and decreased with increasing age among adults aged ≥50 years. Since 2000, the relative increase in eREC was 60·2% in sub-Saharan Africa, 45·7% in North Africa and the Middle East, 41·5% in southeast Asia, east Asia and Oceania, 40·3% in south Asia, 16·2% in Latin America and the Caribbean, 8·3% in ce

关键词：

An underwater image enhancement benchmark dataset and beyond

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Li, Chongyi Guo, Chunle Ren, Wenqi Cong, Runmin Hou, Junhui Kwong, Sam Tao, Dacheng Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China School of Electrical and Information Engineering Tianjin University Tianjin China Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://***/proj_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Image enhancement

Cosmological baryon spread and impact on matter clustering in CAMELS

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Gebhardt, Matthew Anglés-Alcázar, Daniel Borrow, Josh Genel, Shy Villaescusa-Navarro, Francisco Ni, Yueying Lovell, Christopher Nagai, Daisuke Davé, Romeel Marinacci, Federico Vogelsberger, Mark Hernquist, Lars Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Computational Cosmology Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom Centre for Astrophysics Research School of Physics Engineering & Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Department of Physics Yale University New HavenCT06520 United States Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Department of Physics & Astronomy University of the Western Cape Cape Town7535 South Africa South African Astronomical Observatories Observatory Cape Town7925 South Africa Dipartimento di Fisica e Astronomia Augusto Righi Universit‘a di Bologna via Gobetti 93/2 Bologna40129 Italy The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA United States

We quantify the cosmological spread of baryons relative to their initial neighboring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. We show that dark matter particles spread relative to their initial neighboring distribution owing to chaotic gravitational dynamics on spatial scales comparable to their host dark matter halo. In contrast, gas in hydrodynamic simulations spreads much further from the initial neighboring dark matter owing to feedback from supernovae (SNe) and Active Galactic Nuclei (AGN). We show that large-scale baryon spread is very sensitive to model implementation details, with the fiducial SIMBA model spreading ∼40% of baryons >1 Mpc away compared to ∼10% for the IllustrisTNG and ASTRID models. Increasing the efficiency of AGN-driven outflows greatly increases baryon spread while increasing the strength of SNe-driven winds can decrease spreading due to non-linear coupling of stellar and AGN feedback. We compare total matter power spectra between hydrodynamic and paired N-body simulations and demonstrate that the baryonic spread metric broadly captures the global impact of feedback on matter clustering over variations of cosmological and astrophysical parameters, initial conditions, and galaxy formation models. Using symbolic regression, we find a function that reproduces the suppression of power by feedback as a function of wave number (k) and baryonic spread up to k ∼ 10 ℎ Mpc−1 while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation. Copyright © 2023, The Authors. All rights reserved.

关键词： Active Galactic Nuclei

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

The Early Light Curve of SN 2023bee: Constraining Type Ia Supernova Progenitors the Apian Way

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Hosseinzadeh, Griffin Sand, David J. Sarbadhicary, Sumit K. Ryder, Stuart D. Jha, Saurabh W. Dong, Yize Bostroem, K. Azalee Andrews, Jennifer E. Hoang, Emily Janzen, Daryl Jencson, Jacob E. Lundquist, Michael Meza Retamal, Nicolas E. Pearson, Jeniveve Shrestha, Manisha Valenti, Stefano Wyatt, Samuel Farah, Joseph Howell, D. Andrew McCully, Curtis Newsome, Megan Gonzalez, Estefania Padilla Pellegrino, Craig Terreran, Giacomo Alzaabi, Muzoun Green, Elizabeth M. Gurney, Jessica L. Milne, Peter A. Ridenhour, Kaycee I. Smith, Nathan Robles, Paulina Soto Kwok, Lindsey A. Schwab, Michaela Gromadzki, Mariusz Buckley, David A.H. Itagaki, Koichi Hiramatsu, Daichi Chomiuk, Laura Lundqvist, Peter Haislip, Joshua Kouprianov, Vladimir Reichart, Daniel E. Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721-0065 United States Center for Cosmology and AstroParticle Physics Ohio State University 191 West Woodruff Avenue ColumbusOH43210-1117 United States School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Astrophysics and Space Technologies Research Centre Macquarie University SydneyNSW2109 Australia Department of Physics and Astronomy Rutgers The State University of New Jersey 136 Frelinghuysen Road PiscatawayNJ08854-8019 United States Department of Physics and Astronomy University of California Davis 1 Shields Avenue DavisCA95616-5270 United States Gemini Observatory 670 North A‘ohoku Place HiloHI96720-2700 United States Department of Physics & Engineering Physics University of Saskatchewan 116 Science Place SaskatoonSKS7N 5E2 Canada W. M. Keck Observatory 65-1120 Māmalahoa Highway KamuelaHI96743-8431 United States Department of Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195-0002 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117-5575 United States Department of Physics University of California Santa BarbaraCA93106-9530 United States Astronomical Observatory University of Warsaw Al. Ujazdowskie 4 Warszawa00-478 Poland South African Astronomical Observatory P.O. Box 9 Observatory7935 South Africa Department of Physics University of the Free State P.O. Box 339 Bloemfontein9300 South Africa Department of Astronomy University of Cape Town Private Bag X3 Rondebosch7701 South Africa Itagaki Astronomical Observatory Yamagata990-2492 Japan Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138-1516 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48

We present very early photometric and spectroscopic observations of the Type Ia supernova (SN Ia) 2023bee, starting about 8 hr after the explosion, which reveal a strong excess in the optical and nearest UV (U and UVW1) bands during the first several days of explosion. This data set allows us to probe the nature of the binary companion of the exploding white dwarf and the conditions leading to its ignition. We find a good match to the Kasen model in which a main-sequence companion star stings the ejecta with a shock as they buzz past. Models of double detonations, shells of radioactive nickel near the surface, interaction with circumstellar material, and pulsational delayed detonations do not provide good matches to our light curves. We also observe signatures of unburned material, in the form of carbon absorption, in our earliest spectra. Our radio nondetections place a limit on the mass-loss rate from the putative companion that rules out a red giant but allows a main-sequence star. We discuss our results in the context of other similar SNe Ia in the literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Detonation

Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

学校读者我要写书评

暂无评论

Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

Constraints on the photon polarisation in b → sγ transitions using Bs0→ ϕe⁺e⁻ decays

学校读者我要写书评

暂无评论

Journal of High Energy Physics 2025年第3期2025卷 1-23页

作者： Aaij, R. Abdelmotteleb, A.S.W. Abellan Beteta, C. Abudinén, F. Ackernley, T. Adefisoye, A.A. Adeva, B. Adinolfi, M. Adlarson, P. Agapopoulou, C. Aidala, C.A. Ajaltouni, Z. Akar, S. Akiba, K. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Aliouche, Z. Alvarez Cartelle, P. Amalric, R. Amato, S. Amey, J.L. Amhis, Y. An, L. Anderlini, L. Andersson, M. Andreianov, A. Andreola, P. Andreotti, M. Andreou, D. Anelli, A. Ao, D. Archilli, F. Argenton, M. Arguedas Cuendis, S. Artamonov, A. Artuso, M. Aslanides, E. Ataíde Da Silva, R. Atzeni, M. Audurier, B. Bacher, D. Bachiller Perea, I. Bachmann, S. Bachmayer, M. Back, J.J. Baladron Rodriguez, P. Balagura, V. Baldini, W. Balzani, L. Bao, H. Baptista de Souza Leite, J. Barbero Pretel, C. Barbetti, M. Barbosa, I.R. Barlow, R.J. Barnyakov, M. Barsuk, S. Barter, W. Bartolini, M. Bartz, J. Basels, J.M. Bashir, S. Bassi, G. Batsukh, B. Battista, P.B. Bay, A. Beck, A. Becker, M. Bedeschi, F. Bediaga, I.B. Behling, N.A. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Benane, G. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Bernet Andres, S. Bertolin, A. Betancourt, C. Betti, F. Bex, J. Bezshyiko, Ia. Bhom, J. Bieker, M.S. Biesuz, N.V. Billoir, P. Biolchini, A. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Blake, T. Blanc, F. Blank, J.E. Blusk, S. Bocharnikov, V. Boelhauve, J.A. Boente Garcia, O. Boettcher, T. Bohare, A. Boldyrev, A. Bolognani, C.S. Bolzonella, R. Bondar, N. Bordelius, A. Borgato, F. Borghi, S. Borsato, M. Borsuk, J.T. Bouchiba, S.A. Bovill, M. Bowcock, T.J.V. Boyer, A. Bozzi, C. Brea Rodriguez, A. Breer, N. Brodzicka, J. Brossa Gonzalo, A. Brown, J. Brundu, D. Buchanan, E. Buonaura, A. Buonincontri, L. Burke, A.T. Burr, C. Butter, J.S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Caillet, A.C. Calabrese, R. Calderon Ramirez, S. Calefice, L. Cali, S. Calvi, M. Calvo Gomez, M. Camargo Magalhaes, P. Cambon Bouzas, J.I. Campana, P. Campora Perez, D.H. Campoverde Quezada, A.F. Capelli, S. Capriotti, L. Caravaca-Mora, R. Carbone, A. Carcedo Salgad School of Physics and Astronomy Monash University Melbourne Australia Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Department of Engineering Physics Tsinghua University Beijing China Institute Of High Energy Physics (IHEP) Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Consejo Nacional de Rectores (CONARE) San Jose Costa Rica Université Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Université Paris-Saclay Centre d’Etudes de Saclay (CEA) IRFU Saclay France Gif-Sur-Yvette France Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Universität Bonn — Helmholtz-Institut für Strahlen und Kernphysik Bonn Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physikalisches Institut Albert-Ludwigs-Universität Freiburg Freiburg Germany Max-Planck-Institut für Kernphysik (MPIK) Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN

An angular analysis of the Bs0→ ϕe+e− decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of 9 fb−1 at centre... 详细信息

An angular analysis of the Bs0→ ϕe⁺e⁻ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of 9 fb⁻¹ at centre-of-mass energies of 7, 8 and 13 TeV. The analysis is performed in the very low dielectron invariant mass-squared region between 0.0009 and 0.2615 GeV²/c⁴. The longitudinal polarisation fraction of the ϕ meson is measured to be less than 11.5% at 90% confidence level. The ATReCP observable, which is related to the lepton forward-backward asymmetry, is measured to be 0.116 ± 0.155 ± 0.006, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, AT2 and ATImCP, which are sensitive to the virtual photon polarisation, are found to be −0.045 ± 0.235 ± 0.014 and 0.002 ± 0.247 ± 0.016, respectively. The results are consistent with Standard Model predictions. © The Author(s) 2025.

关键词： B Physics Flavour Changing Neutral Currents Hadron-Hadron Scattering Rare Decay