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69th International Astronautical Congress: #InvolvingEveryone, IAC 2018

作者： Knorr, Wolfram Valera, Serge Schiller, Daniel Mazeau, Sophie Airbus Defence and Space GmbH Project Data Management Germany Engineering - Space Systems Claude-Dornier-StraÃŸe Immenstaad88090 Germany Engineering and Quality Directorate - Systems Department Keplerlaan 1 PO Box 299 AG Noordwijk2200 Netherlands Deutsches Zentrum fÃ¼r Luft- und Raumfahrt DLR - Space Administration Department for Project Support RD-RU KÃ¶nigswnterer StraÃŸe 522-524 Bonn53227 Germany Engineering Quality Methods and System Support Office 18 avenue Edouard Belin Toulouse Cedex 931401 France

ECSS has established a coherent set of some 130 standards in the domains space project management, engineering, product assurance and sustainability, which hold roughly 25.000 requirements. These are made applicable, in total or part, in business agreements for European space development programs. In order to ease the requirements flow down from top level customer through the entire supply chain and their verification control, all standards have been transferred into the common requirements management tool IBM DOORS Â®. However, development and maintenance of the ECSS standards today occurs widely still in Microsoft Office Â® tools, mostly MS-Word, because it is done by working groups composed of members throughout the complete institutional/industrial supply chain, who often are not familiar with DOORS. This process of developing standards in Word and transferring them into DOORS has brought many drawbacks, which triggered ECSS to investigate in state-of-the-art alternatives. It is believed that once the new requirements management tool â€œE-RMSâ€¥ (ECSS Requirements Management system) has been established as new standard tool for managing ECSS requirements, this will pave the way for a broader application beyond ECSS, e.g. for managing spacecraft development requirements in general. Copyright © 2018 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Supply chains

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting system (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Brain state instability as a biomarker of Alzheimer’s disease progression: A dynamic functional network connectivity study

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Mohammad S.E. Sendi Elaheh Zendehrouh Robyn L Miller Elizabeth C. Mormino David H Salat Vince D Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Georgia State University Atlanta GA USA Stanford University School of Medicine Palo Alto CA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Boston MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA

Background Finding a biomarker predicting the Alzheimer’s disease (AD) progression from a healthy stage to mild cognitive impairment (MCI), mild dementia (MD) and further to AD is an essential step toward early medical intervention. In recent years, dynamic functional network connectivity (dFNC) from resting state-fMRI, which estimates brain states during the scan, revealed excellent knowledge about AD progression's underlying mechanism. The current study explored whether the AD brain produces similar and stable dFNC states across different scanning sessions and introduced dFNC state (or brain) instability as a potential biomarker of AD progression. Method The data from the longitudinal Open Access Series of Imaging Studies (OASIS)-3 cohort were used in this study. The dataset contains two rs-fMRI sessions on the same day of 1048 healthy controls or HC (clinical dementia rating sum of boxes or CDR-SOB=0, age= 70.80± 8.57), 193 MCI (4.5=0.2). We used Group-independent component analysis to extract independent components of the whole-brain for each subject. Next, we applied the sliding window with Pearson correlation to estimate dFNC of each individual in each session. Subsequently, k-means clustering (k=3, L1-norm as a distance metric, and 1000 iterations) separately was applied to put the dFNCs of each group and session in three distinct states. Pearson correlation between the cluster (state) centroid was used to assess the similarity of state across two sessions. Result We found that HC and MCI groups generate similar dFNC states in both sessions in which The correlation between inter-session “matched” states for HCs and MCIs was more than 0.99 (p<0.0001). But, MD patients did not generate similar brain states across two sessions. This points to a degradation in the stabi

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Monitoring data quality for telehealth systems in the presence of missing data

arXiv

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arXiv 2018年

作者： Mahmood, Tahir Wittenberg, Philipp Zwetsloot, Inez Maria Wang, Hailiang Tsui, Kwok Leung Department of System Engineering and Engineering Management City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong Department of Mathematics and Statistics Helmut Schmidt University Hamburg Germany School of Data Science City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

Background: All-in-one station-based health monitoring devices are implemented in elder homes in Hong Kong to support the monitoring of vital signs of the elderly. During a pilot study, it was discovered that the systolic blood pressure was incorrectly measured during multiple weeks. A real-time solution was needed to identify future data quality issues as soon as possible. Methods: Control charts are an effective tool for real-time monitoring and signaling issues (changes) in data. In this study, as in other healthcare applications, many observations are missing. Few methods are available for monitoring data with missing observations. A data quality monitoring method is developed to signal issues with the accuracy of the collected data quickly. This method has the ability to deal with missing observations. A Hotelling's T-squared control chart is selected as the basis for our proposed method. Findings: The proposed method is retrospectively validated on a case study with a known measurement error in the systolic blood pressure measurements. The method is able to adequately detect this data quality problem. The proposed method was integrated into a personalized telehealth monitoring system and prospectively implemented in a second case study. It was found that the proposed scheme supports the control of data quality. Conclusions: data quality is an important issue and control charts are useful for real-time monitoring of data quality. However, these charts must be adjusted to account for missing data that often occur in healthcare context. Copyright © 2018, The Authors. All rights reserved.

关键词： data reduction

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Global optimal power flow over large-scale power transmission networks

arXiv

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arXiv 2018年

作者： Shi, Y. Tuan, H.D. Apkarian, P. Savkin, A.V. School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia CERT-ONERA Control System Department 2 avenua Edouard Belin Toulouse31055 France School of Electrical Engineering and Telecommunications University of New South Wales NSW2052 Australia

Optimal power flow (OPF) over power transmission networks poses challenging large-scale nonlinear optimization problems, which involve a large number of quadratic equality and indefinite quadratic inequality constraints. These computationally intractable constraints are often expressed by linear constraints plus matrix additional rank-one constraints on the outer products of the voltage vectors. The existing convex relaxation technique, which drops the difficult rank-one constraints for tractable computation, cannot yield even a feasible point. We address these computationally difficult problems by an iterative procedure, which generates a sequence of improved points that converge to a rank-one solution. Each iteration calls a semi-definite program. Intensive simulations for the OPF problems over networks with a few thousands of buses are provided to demonstrate the efficiency of our approach. The suboptimal values of the OPF problems found by our computational procedure turn out to be the global optimal value with computational tolerance less than 0.01%. Copyright © 2018, The Authors. All rights reserved.

关键词： Acoustic generators

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Thalamo-cortical structural co-variation networks are related to familial risk for schizophrenia in the context of lower nuclei volume estimates in patients: an ENIGMA study

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Biological psychiatry 2025年

作者： Annalisa Lella Linda A Antonucci Roberta Passiatore Loredana Bellantuono Pierluigi Selvaggi Teresa Popolizio Guido Di Sciascio Alessandro Saponaro Patrizia Ricci Mario Altamura Giuseppe Blasi Antonio Rampino Chris Vriend Vince D Calhoun Kelly Rootes-Murdy Aaron L Goldman Inmaculada Baeza Josefina Castro-Fornieles Gisela Sugranyes Elena De la Serna Edith Pomarol-Clotet Mar Fatjó-Vilas Raymond Salvador Andriana Karuk Paola Fuentes-Claramonte David C Glahn Amanda L Rodrigue John Blangero Lei Wang Taeyoung Lee Karolin E Einenkel Saskia Hamers Oliver Gruber Adrian Preda Young-Chul Chung Soyolsaikhan Odkhuu Corentin Vallée Paola Dazzan Machteld Marcelis Stijn Michielse Katharina Brosch Frederike Stein Igor Nenadić Benjamin Straube Florian Thomas-Odenthal Tilo Kircher Sean Carruthers Susan L Rossell Phillip J Sumner Tamsyn E Van Rheenen Caroline Demro Ian S Ramsay Scott R Sponheim Rebekka Lencer Susanne Meinert Tim Hahn Udo Dannlowski Dominik Grotegerd Mariateresa Ciccarelli Felice Iasevoli Giuseppe Pontillo Godfrey D Pearlson Derin Cobia Fabrizio Piras Nerisa Banaj Daniela Vecchio Marjolein E A Barendse Neeltje E M van Haren Hang Joon Jo Kang Sim Yann Quidé Melissa J Green Rachael Slate Giacomo Cecere Wolfgang Omlor Stephanie Homan Philipp Homan Sophia I Thomopoulos Jessica A Turner Theo G M van Erp Paul M Thompson Alessandro Bertolino Giulio Pergola Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy. Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy Lieber Institute for Brain Development Johns Hopkins Medical Campus Baltimore MD USA. Istituto Nazionale di Fisica Nucleare Bari Italy. Azienda Ospedaliero-Universitaria Consorziale Policlinico Bari Italy. Neuroradiology Unit Scientific Institute for Research Hospitalization and Health Care Casa Sollievo della Sofferenza 71013 San Giovanni Rotondo Foggia Italy. Department of Mental Health ASL Bari Bari Italy. Department of Mental Health ASL Brindisi Brindisi Italy. Department of Mental Health ASL Barletta Andria Trani BAT Italy. Department of Clinical and Experimental Medicine University of Foggia Italy. Amsterdam UMC Vrije Universiteit Amsterdam Department of Psychiatry and Department of Anatomy and Neuroscience de Boelelaan 1117 Amsterdam the Netherlands Compulsivity Impulsivity and Attention Amsterdam Neuroscience de Boelelaan 1117 Amsterdam the Netherlands. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia Institute of Technology Georgia State University Emory University Atlanta GA USA. Department of Child and Adolescent Psychiatry and Psychology 2021 SGR 01319 Institute of Neuroscience Hospital Clínic Barcelona Barcelona University of Barcelona Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) CIBERSAM Spain. FIDMAG Germanes Hospitalàries Research Foundation CIBERSAM ISCIII Barcelona Spain. Department of Psychiatry and Behavioral Sciences Boston Children's Hospital Boston MA USA Department of Psychiatry Harvard Medical School Boston MA USA Olin Neuropsychiatry Research Center Institute of Living Hartford CT USA. Department of Psychiatry Harvard Medical School Boston MA USA. Department of Human Genetics and South Texas Diabetes and Obesity Institute School of

BACKGROUND:Structural brain differences in the thalamus and the cortex have been widely reported in schizophrenia (SCZ) relative to neurotypical controls (NC). Most prior studies examined the thalamus as a whole, single region-of-interest. Additionally, findings in individuals at familial high-risk for schizophrenia (FHR) remain inconclusive. Here, we investigated whether local and network-wide thalamic-related structural alterations vary as a function of familial risk for schizophrenia. METHODS:Structural MRI scans were obtained from 5,197 participants (3,409 NC, 257 FHR, 1,531 SCZ) across 32 cross-sectional samples within the ENIGMA Consortium. Random-effects meta-analyses, and network analyses were conducted on (i) local thalamic alterations (volume estimates of seven thalamic subdivisions), and (ii) network-wide thalamic alterations (thickness and surface-related thalamo-cortical/cortico-cortical co-variation patterns) across groups (NC, FHR, SCZ). RESULTS:Individuals with SCZ showed significantly lower gray matter volume estimates in the anterior, pulvinar, medial, posterior, and ventral thalamic subdivisions compared to NC (q<0.05). FHR did not differ from NC. At the network-wide level, thalamo-cortical co-variations discriminated FHR from NC (q<0.05), with FHR showing intermediate co-variation between SCZ and NC. Cortico-cortical co-variation patterns revealed that SCZ and FHR shared similarly disconnected clustering configurations, distinct from NC (q<0.05). CONCLUSIONS:Results revealed lower thalamic volume estimates in SCZ but not in FHR, hence yielding no evidence of a familial risk trait, whereas thalamo-cortical and cortico-cortical co-variation estimates were associated with familial risk for SCZ These findings suggest that, once the thalamus is parsed into subdivisions, network-wide thalamo-cortical features may identify trait-dependent, neurobiological correlates of genetic risk for SCZ.

关键词： Familial high risk for schizophrenia meta-analyses morphometric measures structural neuroimaging thalamic subdivisions thalamo-cortical networks

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Deep learning to represent sub-grid processes in climate models

arXiv

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arXiv 2018年

作者： Rasp, Stephan Pritchard, Michael S. Gentine, Pierre Meteorological Institute Ludwig-Maximilian-University Munich Germany Department of Earth System Science University of California IrvineCA United States Columbia University Department of Earth and Environmental Engineering Earth Institute Data Science Institute New YorkNY United States

The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly. The trained neural network then replaces the traditional sub-grid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multi-year simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system Model development could play a key role in reducing climate prediction uncertainty in the coming decade. © 2018, CC BY.

关键词： Climate models

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Negative feedback control of neuronal activity by microglia

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四川生理科学杂志 2020年第4期42卷 515-515页

作者： Ana Badimon Nash Family Department of Neuroscience Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Center for Glial Biology Icahn School of Medicine at Mount Sinai New York NY USA Ronald M. Loeb Center for Alzheimer’s Disease Icahn School of Medicine at Mount Sinai New York NY USA Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA Department of Anatomy and Molecular Cell Biology Nagoya University Graduate School of Medicine Nagoya Japan Division of System Neuroscience Kobe University Graduate School of Medicine Kobe Japan Department of Pharmacology University of Minnesota Minneapolis MN USA Center for Brain Immunology and Glia Department of Neuroscience University of Virginia Charlottesville VA USA Department of Surgery Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Department of Pharmacology Vanderbilt University Nashville TN USA Ann Romney Center for Neurologic Diseases Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Genetics and Genomic Sciences Icahn Institute of Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY USA Department of Biochemistry and Structural Biology University of Texas Health Science Center San Antonio TX USA Department of Physiology Feinberg School of Medicine Northwestern University Chicago IL USA Department of Anesthesia Beth Israel Deaconess Medical Center and Harvard Medical School Boston MA USA Department of Medicine Beth Israel Deaconess Medical Center and Harvard Medical School Boston MA USA Vanderbilt Brain Institute Vanderbilt University Nashville TN USA Vanderbilt Center for Addiction Research Vanderbilt University Nashville TN USA Department of Molecular Physiology and Biophysics Vanderbilt University Nashville TN USA Department of Psychiatry and Behavioral Sciences Vanderbilt Unive

Microglia,the brain's resident macrophages,help to regulate brain function by removing dying neurons,pruning non-functional synapses,and producing ligands that support neuronal *** we show that microglia are also critical modulators of neuronal activity and associated behavioural responses in *** respond to neuronal activation by suppressing neuronal activity,and ablation of microglia amplifies and synchronizes the activity of neurons,leading to *** of neuronal activation by microglia occurs in a highly region-specific fashion and depends on the ability of microglia to sense and catabolize extracellular ATP,which is released upon neuronal activation by neurons and *** triggers the recruitment of microglial protrusions and is converted by the microglial ATP/ADP hydrolysing ectoenzyme CD39 into AMP;AMP is then converted into adenosine by CD73,which is expressed on microglia as well as other brain *** sensing of ATP,the ensuing microglia-dependent production of adenosine,and the adenosine-mediated suppression of neuronal responses via the adenosine receptor A1R are essential for the regulation of neuronal activity and animal *** findings suggest that this microglia-driven negative feedback mechanism operates similarly to inhibitory neurons and is essential for protecting the brain from excessive activation in health and disease.

关键词： removing converted finding

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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Aggregation Signature for Small Object Tracking

arXiv

引用

arXiv 2019年

作者： Liu, Chunlei Ding, Wenrui Yang, Jinyu Murino, Vittorio Zhang, Baochang Han, Jungong Guo, Guodong School of Electrical and Information Engineering Beihang University Beijing China Unmanned System Research Institute Beihang University Beijing China School of Computer Science University of Birmingham British United Kingdom University of Verona Verona Italy Pattern Analysis and Computer Vision department Istituto Italiano di Tecnologia Genoa Italy School of Automation Science and Electrical Engineering Beihang University Beijing China Shenzhen Academy of Aerospace Technology Shenzhen China WMG Data Science Group University of Warwick CoventryCV4 7AL United Kingdom Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application

—Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://***/bczhangbczhang/. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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