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2016 Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches, CoCo 2016

作者： Heinrich, Stefan Weber, Cornelius Wermter, Stefan Xie, Ruobing Lin, Yankai Liu, Zhiyuan Department of Informatics Knowledge Technology Group University of Hamburg Vogt-Koelln Str. 30 Hamburg22527 Germany Department of Computer Science and Technology Natural Language Processing Lab Tsinghua University Room 4-506 FIT Building Beijing100084 China

The human brain as one of the most complex dynamic systems enables us to communicate and externalise information by natural language. Despite extensive research, human-like communication with interactive robots is not yet possible, because we have not yet fully understood the mechanistic characteristics of the crossmodal binding between language, actions, and visual sensation that enable humans to acquire and use natural language. In this position paper we present vision- and action-embodied language learning research as part of a project investigating multi-modal learning. Our research endeavour includes to develop a) a cortical neural-network model that learns to ground language into crossmodal embodied perception and b) a knowledge-based teaching framework to bootstrap and scale-up the language acquisition to a level of language development in children of age up to two years. We embed this approach of internally grounding embodied experience and externally teaching abstract experience into the developmental robotics paradigm, by means of developing and employing a neurorobot that is capable of multisensory perception and interaction. The proposed research contributes to designing neuroscientific experiments on discovering crossmodal integration particularly in language processing and to constructing future robotic companions capable of natural communication. Copyright © 2016 for this paper by its authors.

关键词： Knowledge based systems

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Concurrent antiepileptic and antipsychotic use moderates lithium’s effects on regional brain volumes: a mega-analysis from the ENIGMA-Bipolar Disorder Working Group

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Neuroscience Applied 2022年 1卷

作者： S. King G. Tronchin L. Nabulsi S.I. Thomopoulos E. Fontana J. Radua K. Sim O. Gruber L. Yatham U. Dannlowski T. Kircher I. Nenadic F. Stein K. Brosch F. Howells B.C.M. Haarman E. Pomarol-Clotet E. Vieta M. Landen D. Cannon C. McDonald Centre For Neuroimaging- Cognition And Genomics Nicog- Clinical Neuroimaging Laboratory- Ncbes Galway Neuroscience Centre- National University Of Ireland Galway School Of Medicine Galway Ireland Imaging Genetics Center- Mark And Mary Stevens Neuroimaging And Informatics Institute- University Of Southern California- Usa. Keck School Of Medicine Los Angeles- California United States Fondazione Irccs Ca’ Granda Ospedale Maggiore Policlinico Department Of Neurosciences And Mental Health Milan Italy Yong Loo Lin- National University Of Singapore- Singapore School Of Medicine Singapore Singapore Institut D'investigacions Biomèdiques August Pi I Sunyer Idibaps Centro De Investigación Biomédica En Red De Salud Mental Cibersam Barcelona Spain University Of British Columbia- British Columbia- Canada- Department Of Psychiatry Vancouver Canada Early Psychosis: Interventions And Clinical-detection (Epic) Lab Department Of Psychosis Studies Institute Of Psychiatry Psychology & Neuroscience King's College London London Uk West Region- Institute Of Mental Health- Singapore- Psychiatric Hospital West Region Singapore Section For Experimental Psychopathology And Neuroimaging Department Of General Psychiatry Heidelberg University Heidelberg Baden-wuerttemberg Germany University Of Münster Institute For Translational Psychiatry Münster Germany University Of Marburg Department Of Psychiatry And Psychotherapy Marbug Germany University Of Cape Town Neuroscience Institute Cape Town South Africa University Of Cape Town Department Of Psychiatry And Mental Health Cape Town South Africa Institut D’investigacions Biomèdiques August Pi I Sunyer Idibaps Centro De Investigación Biomédica En Red De Salud Mental Cibersam Barcelona Spain University Medical Center Groningen- University Of Groningen Department Of Psychiatry Groningen Netherlands- The Fidmag Germanes Hospitalàries Research Foundation Barcelona Spain Cibersam Madrid Spain Barcelona Bipolar Disorders And Depressive Unit- Hospital Clin

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Automatic classification of bright retinal lesions via deep network features

arXiv

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arXiv 2017年

作者： Sadek, Ibrahim Elawady, Mohamed El Rahman Shabayek, Abd Image and Pervasive Access Lab CNRS UMI 2955 Singapore Télécom ParisTech Institut Mines Télécom France Universite Jean Monnet CNRS UMR 5516 Laboratoire Hubert Curien Saint-EtienneF-42000 France Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Department of Computer Science Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Eye protection

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SHREC'16 Track: Partial Shape Queries for 3D object retrieval 9

SHREC'16 Track: Partial Shape Queries for 3D object retrieva...

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9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

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Evaluation of the model for individual behavior rate estimate: Social network data

Evaluation of the model for individual behavior rate estimat...

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International Conference on Soft Computing and Measurements (SCM)

作者： Alena V. Suvorova Alexander L. Tulupyev Theoretical and Interdisciplinary Computer Science Lab (TICS Lab) St. Petersburg Institute for Informatics and Automation of the RAS (SPIIRAS) St. Petersburg Russia Russia Computer Science Department St. Petersburg State University St. Petersburg Russia

ISBN: (纸本)9781467389204

The paper described the Bayesian belief network model for individual behavior rate estimate based on data about the last episodes of that behavior. For model evaluation we used data from social network VKontakte about episodes of publishing posts. The sample size is 1123 users with 160555 posts in total for the half-year period. Rate values were discretized and form eight classes. There was no statistical difference between estimated rate distribution in the group and the real rate distribution; individual rate estimates showed 91% classification accuracy. Hence, the model allows behavior rate estimating on the base of data about limited number of episodes that is often an important issue, for example, in epidemiological studies.

关键词： Data models Computational modeling Social network services Bayes methods Publishing Human immunodeficiency virus Random variables

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Energy efficient context based forwarding strategy in named data networking of things 3

Energy efficient context based forwarding strategy in named ...

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3rd ACM International Conference on Information-Centric Networking, ACM-ICN 2016

作者： Lenord Melvix, J.S.M. Lokesh, Vikas Polyzos, George C. Dept. of Computer Science and Engineering Jacobs School of Engineering University of California San Diego San DiegoCA92093-0114 United States Mobile Multimedia Lab Dept. of Informatics School of Information Sciences and Technology Athens University of Economics and Business Evelpidon 47A Athens113 62 Greece

ISBN: (纸本)9781450344678

Named Data Networking (NDN) of Things has gathered interest among the Internet-of-Things (IoT) research community due to its hassle-free deployment and configurability. This approach to interconnecting devices provides the network with additional information about the data transported, which can be used for improved efficiency. This is one of the targets for mass IoT or Machine-to-Machine (M2M) communications modes of 5G. In this paper, we propose a context based forwarding strategy that makes forwarding decisions based on the tolerance to inaccuracies in data defined for the application requesting it. We also propose a modification to the hierarchical naming scheme by appending the name of the application generating the interest. In addition to faster context based forwarding, this modification simplifies the naming as it is easier to understand the context under which the interest was generated even with generic names. We demonstrate an improvement in energy efficiency of the system using the proposed technique compared to an IP based implementation. In addition, the proposed technique offers a platform to implement application level access control and Quality-of-Service support. ©2016 ACM.

关键词： Middleware

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Corrigendum to “An FPTAS for the parallel two-stage flowshop problem” [Theoret. Comput. Sci. 657 (2017) 64–72]

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Theoretical computer science 2017年 687卷 93-94页

作者： Jianming Dong Jueliang Hu Mikhail Y. Kovalyov Guohui Lin Taibo Luo Weitian Tong Xueshi Wang Yinfeng Xu Department of Mathematics Zhejiang Sci-Tech University Hangzhou Zhejiang 310018 China United Institute of Informatics Problems National Academy of Sciences of Belarus Surganova 6 220012 Minsk Belarus Department of Computing Science University of Alberta Edmonton Alberta T6G 2E8 Canada Business School Sichuan University Chengdu Sichuan 610065 China Department of Computer Sciences Georgia Southern University Statesboro GA 30458 USA State Key Lab for Manufacturing Systems Engineering Xi'an Shaanxi 710049 China

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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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Two-service analytical model for partially-shared elastic optical link spectrum

Two-service analytical model for partially-shared elastic op...

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IEEE Workshop on High Performance Switching and Routing

作者： Joobum Kim Shuyi Yan Andrea Fumagalli Eiji Oki Naoaki Yamanaka OpNeAR Lab The University of Texas at Dallas Richardson TX USA Department of Communication Engineering and Informatics The University of Electro-Communications Japan Department of Information and Computer Science Keio University Japan

ISBN: (纸本)9781479989515

Elastic Optical Networks (EONs) have the potential to improve the fiber spectrum utilization by allocating spectrum resources to multiple traffic requests proportionally to the amount of carried traffic. However, achieving high spectrum utilization in this elastic scenario is hindered by the resulting spectrum fragmentation. A number of studies have addressed and made attempts to mitigate spectrum fragmentation. Most of these studies are based on simulation techniques and target the overall blocking probability experienced by the offered traffic requests due to the lack of available spectrum resources. Some studies have also shown that blocking probability in EON can be uneven, i.e., high-rate circuit requests are more likely to be blocked when compared to low-rate requests due to the shortage of contiguously available spectrum resources. The contribution of this paper is to extend an existing Markov Chain (MC) model previously proposed by the authors to quantify blocking probability in a two-service elastic fiber link. The model extension accounts for a self-limited and partial sharing of the fiber spectrum to accommodate the two types of service. The MC model is used to quantify both the blocking probability and its fairness across the two types of service, documenting how the EON uneven blocking behavior can be significantly mitigated by performing partial (as opposed to full) sharing of the fiber spectrum.

关键词： Integrated circuit modeling Analytical models Markov processes Routing Conferences Resource management computer science

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Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

arXiv

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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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