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24th International Conference on computers in Education - Workshop, ICCE 2016

作者： Tscholl, Michael Rajendran, Ramkumar Biswas, Gautam Goldberg, Benjamin S. Sottilare, Robert A. Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems Vanderbilt University 1025 16th Avenue South NashvilleTN37212 United States U.S. Army Research Laboratory - Human Research and Engineering Simulation and Training Technology Center OrlandoFL32826 United States

ISBN: (纸本)9789869401210

As the development and use of Open-Ended Learning Environments (OELEs) continues to increase, supporting students' learning in these environments with Intelligent Tutoring is rapidly becoming an important area of research. Many existing learning environments guide students in step-by-step processes to reach their learning goal;consequently, data preprocessing is well defined. In OELEs, in contrast, students may achieve task goals through multiple pathways, and there exist multiple ways to assess performance. We present a simulation OELE designed to teach students decision-making in a complex problem solving task. To provide Intelligent Tutoring Support, we are required to track performance along several dimensions. We present our approach to extract data for performance assessments that can be leveraged to provide Intelligent Tutoring Support. We generalize our approach and present guidelines applicable for similar OELEs. Copyright 2016 Asia-Pacific Society for computers in Education. All rights reserved.

关键词： Students

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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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Author Correction: A shared neural basis underlying psychiatric comorbidity

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Nature medicine 2023年第9期29卷 2375页

作者： Chao Xie Shitong Xiang Chun Shen Xuerui Peng Jujiao Kang Yuzhu Li Wei Cheng Shiqi He Marina Bobou M John Broulidakis Betteke Maria van Noort Zuo Zhang Lauren Robinson Nilakshi Vaidya Jeanne Winterer Yuning Zhang Sinead King Tobias Banaschewski Gareth J Barker Arun L W Bokde Uli Bromberg Christian Büchel Herta Flor Antoine Grigis Hugh Garavan Penny Gowland Andreas Heinz Bernd Ittermann Hervé Lemaître Jean-Luc Martinot Marie-Laure Paillère Martinot Frauke Nees Dimitri Papadopoulos Orfanos Tomáš Paus Luise Poustka Juliane H Fröhner Ulrike Schmidt Julia Sinclair Michael N Smolka Argyris Stringaris Henrik Walter Robert Whelan Sylvane Desrivières Barbara J Sahakian Trevor W Robbins Gunter Schumann Tianye Jia Jianfeng Feng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai China. Faculty of Psychology Technische Universität Dresden Dresden Germany. School of Health Sciences The University of Manchester Manchester UK. Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London London UK. Clinical and Experimental Sciences Faculty of Medicine University of Southampton Southampton UK. Department of Psychology College of Science Northeastern University Boston MA USA. Department of Psychology MSB Medical School Berlin Berlin Germany. Department of Psychological Medicine Section for Eating Disorders Institute of Psychiatry Psychology and Neuroscience King's College London London UK. South London and Maudsley NHS Foundation Trust London UK. Centre for Population Neuroscience and Stratified Medicine (PONS) Department of Psychiatry and Neuroscience Charité Universitätsmedizin Berlin Berlin Germany. Department of Psychiatry and Psychotherapy Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin Humboldt-Universität zu Berlin and Berlin Institute of Health Berlin Germany. Department of Education and Psychology Freie Universität Berlin Berlin Germany. Psychology Department B44 University Rd University of Southampton Southampton UK. School of Medicine Center for Neuroimaging Cognition and Genomics National University of Ireland (NUI) Galway Ireland. Department of Child and Adolescent Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany. Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London UK. Discipline of Psychiatry School of Medicine and Trinity College Institute of Neuroscience T

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Performance optimization of Jacobi stencil algorithms based on POWER8 architecture

Performance optimization of Jacobi stencil algorithms based ...

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International Conference on Application Specific systems (ASAP), Architectures and Processors

作者： Jingheng Xu Haohuan Fu Lin Gan Yu Song Hongbo Peng Wen Shi Guangwen Yang Department of Computer Science & Technology Tsinghua University National Supercomputer Center at Wuxi Ministry of Education Key Lab. for Earth System Modeling Tsinghua University IBM China Systems and Technology Laboratory

In this paper we choose the widely used Jacobi stencil algorithm as our target program to evaluate the effectiveness of tuning techniques based on the latest POWER8 processor, thus to provide optimization guidelines t... 详细信息

ISBN: (纸本)9781509015047

关键词： Optimization Jacobian matrices Hardware Instruction sets Tuning computer architecture Software performance

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Resonant properties of dipole skyrmions in amorphous Fe/Gd multilayers

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Physical Review B 2017年第22期95卷 224405-224405页

作者： S. A. Montoya S. Couture J. J. Chess J. C. T. Lee N. Kent M.-Y. Im S. D. Kevan P. Fischer B. J. McMorran S. Roy V. Lomakin E. E. Fullerton Center for Memory and Recording Research University of California San Diego La Jolla California 92093 USA Department of Electrical and Computer Engineering University of California San Diego La Jolla California 92093 USA Space and Naval Warfare Systems Center - Pacific San Diego California 92152 USA Department of Physics University of Oregon Eugene Oregon 97401 USA Center for X-Ray Optics Lawrence Berkeley National Laboratory Berkeley California 94720 USA Physics Department University of California Santa Cruz California 94056 USA Department of Emerging Materials Science Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA

The dynamic response of dipole skyrmions in Fe/Gd multilayer films is investigated by ferromagnetic resonance measurements and compared to micromagnetic simulations. We detail thickness- and temperature-dependent studies of the observed modes as well as the effects of magnetic field history on the resonant spectra. Correlation between the modes and the magnetic phase maps constructed from real-space imaging and scattering patterns allows us to conclude that the resonant modes arise from local topological features such as dipole skyrmions but do not depend on the collective response of a close-packed lattice of these chiral textures. Using micromagnetic modeling, we are able to quantitatively reproduce our experimental observations which suggests the existence of localized spin-wave modes that are dependent on the helicity of the dipole skyrmion. We identify four localized spin-wave excitations for the skyrmions that are excited under either in-plane or out-of-plane rf fields. Lastly we show that dipole skyrmions and nonchiral bubble domains exhibit qualitatively different localized spin-wave modes.

关键词： Magnetic susceptibility Magnetism Micromagnetism Skyrmions Spin waves

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Trainable back-propagated functional transfer matrices

arXiv

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arXiv 2017年

作者： Cai, Cheng-Hao Xu, Yanyan Ke, Dengfeng Su, Kaile Sun, Jing Department of Computer Science University of Auckland 38 Princes Street Auckland1142 New Zealand School of Information Science and Technology Beijing Forestry University 35 Qing-Hua East Road Beijing100083 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhong-Guan-Cun East Road Beijing100190 China Institute for Integrated and Intelligent Systems Griffith University 170 Kessels Road NathanQLD4111 Australia

Connections between nodes of fully connected neural networks are usually represented by weight matrices. In this article, functional transfer matrices are introduced as alternatives to the weight matrices: Instead of using real weights, a functional transfer matrix uses real functions with trainable parameters to represent connections between nodes. Multiple functional transfer matrices are then stacked together with bias vectors and activations to form deep functional transfer neural networks. These neural networks can be trained within the framework of back-propagation, based on a revision of the delta rules and the error transmission rule for functional connections. In experiments, it is demonstrated that the revised rules can be used to train a range of functional connections: 20 different functions are applied to neural networks with up to 10 hidden layers, and most of them gain high test accuracies on the MNIST database. It is also demonstrated that a functional transfer matrix with a memory function can roughly memorise a non-cyclical sequence of 400 digits. Copyright © 2017, The Authors. All rights reserved.

关键词： Transfer matrix method

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Home energy management by handling life event: Can residents manage energy by knowing their life events?

Home energy management by handling life event: Can residents...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Noriyuki Kushiro Takahiro Kondo Yuki Ogawa Toshiyasu Higuma Faculty of Computer Science and System Engineering Kyushu Institute of Technology Fukuoka Japan Living Environment Systems Laboratory Mitsubishi Electric Corp. Kamakura Kanagawa Japan

This paper describes an attempt for establishing an intelligent home energy management algorithm, which can response to residents' life pattern. A long-term data collection system has been constructed to confirm feasibility of the algorithm. Capability of energy demand prediction on the life events and feasibility of energy management by handling life events are discussed on the field data.

关键词： Energy management Power demand Prediction algorithms Data collection Power measurement Energy consumption Cleaning

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A SDN-based Architecture for Horizontal Internet of Things Services

A SDN-based Architecture for Horizontal Internet of Things S...

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Ad-Hoc and Sensor Networks Symposium

作者： Yuhong Li Xiang Su Jukka Riekki Theo Kanter Rahim Rahmani State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Center for Ubiquitous Computing University of Oulu Department of Computer and Systems Sciences Stockholm University

ISBN: (纸本)9781479966653

The Internet of Things (IoT) architecture is expected to evolve into a horizontal model containing various open systems, integrated environments, and platforms. However, not much research effort has been devoted to developing architectures for horizontal IoT solutions so far. This paper presents an IoT architecture based on Software-Defined Networking (SDN). In this architecture, devices, gateways, and data are open and programmable to IoT application developers and service operators. Moreover, IoT data provision and interoperability are supported at different levels. We present an implementation of the proposed architecture. Our implementation shows that the proposed architecture enables rapid creation of IoT applications by reusing ready applications and data. The measurement and evaluation results demonstrate the feasibility of the proposed architecture.

关键词： Internet of Things (IoT) Horizontal solution Software-Defined Networking (SDN) Future network architecture

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Self-organization of dragon kings

arXiv

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arXiv 2017年

作者： Lin, Yuansheng Burghardt, Keith Rohden, Martin Noël, Pierre-André D'Souza, Raissa M. School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing100191 China Department of Computer Science University of California DavisCA95616 United States Department of Political Science University of California DavisCA95616 United States Department of Mechanical and Aerospace Engineering University of California DavisCA95616 United States Santa Fe Institute Santa FeNM87501 United States

The mechanisms underlying cascading failures are often modeled via the paradigm of self-organized criticality. Here we introduce a simple model where nodes self-organize to be either weak or strong to failure which captures the trade-off between degradation and reinforcement of nodes inherent in many network systems. If strong nodes cannot fail, this leads to power law distributions of failure sizes with so-called "Black Swan" rare events. In contrast, if strong nodes fail once a sufficient fraction of their neighbors fail, this leads to "Dragon Kings", which are massive failures caused by mechanisms distinct from smaller failures. In our model, we find that once an initial failure size is above a critical value, the Dragon King mechanism kicks in, leading to piggybacking system-wide failures. We demonstrate that the size of the initial failed weak cluster predicts the likelihood of a Dragon King event with high accuracy and we develop a simple control strategy which also reveals that a random upgrade can inadvertently make the system more vulnerable. The Dragon Kings observed are self-organized, existing throughout the parameter regime. Copyright © 2017, The Authors. All rights reserved.

关键词： Economic and social effects

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POMDP-lite for Robust Robot Planning under Uncertainty

POMDP-lite for Robust Robot Planning under Uncertainty

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IEEE International Conference on Robotics and Automation

作者： Min Chen Emilio Frazzoli David Hsu Wee Sun Lee the Department of Computer Science National University of Singapore Singapore 117417 Singapore. the Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA 02139 USA

ISBN: (纸本)9781467380270

The partially observable Markov decision process (POMDP) provides a principled general model for planning under uncertainty. However, solving a general POMDP is computationally intractable in the worst case. This paper introduces POMDP-lite, a subclass of POMDPs in which the hidden state variables are constant or only change deterministically. We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks. We develop a simple model-based Bayesian reinforcement learning algorithm to solve POMDP-lite models. The algorithm performs well on large-scale POMDP-lite models with up to 10~(20) states and outperforms the state-of-the-art general-purpose POMDP algorithms. We further show that the algorithm is near-Bayesian-optimal under suitable conditions.

关键词： Planning Uncertainty POMDP

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