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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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On energy efficiency of liquid cooled HPC datacenters

On energy efficiency of liquid cooled HPC datacenters

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Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic systems (ITHERM)

作者： Michael K. Patterson Shankar Krishnan John M. Walters Technical Computing Group Systems Architecture and Path finding Intel Corporation Dupont Washington USA India Institute of Technology Mumbai India Walters Engineering LLC Hillsboro Oregon USA

HPC Data Center's performance and growth are now being limited by both cost and power. A cost-efficient data center and an energy-efficient data center are all too often mutually exclusive, but they do not have to be. Liquid cooling is one area that, when done right, can improve both costs and energy efficiency. The design for liquid cooling systems generally begins with the ASHRAE liquid cooling datacenter classes. These provide guidance to both datacenter facility cooling system designers and electronic equipment manufacturers by providing a common baseline and understanding of the interface conditions between the cooling and the IT equipment. Further, the liquid cooling classes also suggest possible cooling equipment for a given datacenter class. Due to the aforementioned cooling equipment prescription, perception exists that moving from W1/W2 class environments to W3 or W4 classes represent increased energy efficiency during IT equipment operation. In this paper we show this not to be the case universally and explore a more detailed, technical approach to optimizing both cost and energy efficiency. The range of parameters includes geographical and climate conditions, state of the existing data center cooling infrastructure (greenfield, retrofit, cluster change-out, expansion), and IT level liquid cooling architecture. Through this analysis we show that for energy efficient operation of the IT equipment there exists an optimum liquid operating temperature that can also provide the lowest TCO. This temperature can drive the right capital investment as well as reduce facility operational expense and IT operational expense. We also explore the impact on reliability, the controls architecture, use of efficiency metrics, cluster compute performance, and opportunities for energy re-use.

关键词： Liquid cooling Energy efficiency Poles and towers Temperature distribution Guidelines Meteorology

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Behavioral modeling of Wireless Sensor nodes using meta-data

Behavioral modeling of Wireless Sensor nodes using meta-data

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Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP)

作者： Hashan M Wanniarachchi Kasun S Perera M.D.J.S Goonatillake University of Colombo School of Computing Sri Lanka Database Technology Group Institute of Systems Architecture Dresden TU Germany

Many Wireless Sensor Network systems are deployed under extreme conditions. Thus, it's important to maximize their lifespan by optimal use of network's resources. Battery lifespan of a node is a crucial resource that needs to be used carefully. Energy aware routing & scheduled sensing have been introduced for careful use of battery. It is necessary to optimize battery usage by predicting it's future behavior. This will lead the users to take early decisions, thus minimizing network downtime. Hence, we explore the possibility of using meta-data on each node, to represent and predict node behavior using machine learning models. In this research, we use node voltage level as an indicator of the energy used, as voltage is proportional to available energy. Node energy consumption is modeled and predicted by ARIMA models using these voltage readings. We also classify nodes as high, medium & low use, with respect to its current and future usage, thus allowing user to take early decisions maximizing network throughput (lifetime). After evaluating predicted node behavior against a created base set of behavioral classifications, we achieved a 80% accuracy rate. Using different and increasing window sizes, we evaluated the validity of our model. In these experiments, our prediction method produced highly accurate results for all considered prediction windows. By being able to predict a node's energy consumption behavior at a higher accuracy rate, WSN users can make optimization decisions beforehand to increase the network lifetime.

关键词： Wireless sensor networks Predictive models Time series analysis Data models Energy consumption Accuracy Voltage measurement

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Corrigendum to "Urban rainwater harvesting systems: Research, implementation and future perspectives" [Water Res. 115 (2017) 195-209]

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Water research 2017年 121卷 386页

作者： Alberto Campisano David Butler Sarah Ward Matthew J Burns Eran Friedler Kathy DeBusk Lloyd N Fisher-Jeffes Enedir Ghisi Ataur Rahman Hiroaki Furumai Mooyoung Han Department of Civil Engineering and Architecture University of Catania Viale A. Doria 6 95125 Catania Italy. Electronic address: acampisa@dica.unict.it. Centre for Water Systems College of Engineering Mathematics and Physical Sciences University of Exeter Exeter EX4 4QF UK. Waterway Ecosystem Research Group School of Ecosystem and Forest Sciences University of Melbourne Burnley Australia. Department of Environmental Water & Agricultural Engineering Faculty of Civil and Environmental Engineering TechnioneIsrael Institute of Technology Haifa 32000 Israel. Department of Biological and Environmental Sciences Longwood University 201 High Street Farmville VA 23909 USA. Department of Civil Engineering University of Cape Town Private Bag X3 Rondebosch South Africa. Federal University of Santa Catarina Department of Civil Engineering Laboratory of Energy Efficiency in Buildings Florian_opoli SC Brazil. School of Computing Engineering and Mathematics University of Western Sydney Sydney Australia. Research Center for Water Environment Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan. Department of Civil and Environmental Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul South Korea.

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Interaction and architectural space

Interaction and architectural space

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32nd Annual ACM Conference on Human Factors in computing systems, CHI EA 2014

作者： Dalton, Nick Hölscher, Christoph Green, Keith Evan Mathew, Anijo Dalton, Ruth Schnädelbach, Holger Wiberg, Mikael Varoudis, Tasos Department of Computing Open University Milton Keyens United Kingdom Department of Humanities Social and Political Sciences ETH Zürich Zürich Switzerland Institute for Intelligent Materials Systems and Environments Clemson University Clemson SC United States Institute of Design Ilinois Institute of Technology Chicago IL United States Department of Architecture Northumbria University Newcastle United Kingdom School of Computer Science University of Nottingham Nottingham United Kingdom Department of Informatics Umeå University 901 87 Umeå Sweden Space Group University College London London United Kingdom

ISBN: (纸本)9781450324748

For many in the field of HCI, location and space are synonymous;yet, as we move from the mobile era to the ubiquitous era, computing becomes entangled with notions of space. This workshop critically examines the role of space in human-computer interfaces. The objective is to bring together diverse perspectives of space, drawing from architecture, philosophy, art, geography, design, dance, spatial-cognition, mathematics, computing, and still other domains, towards foregrounding space in theoretical discussions and explorations within the CHI community. Expected outcomes are the reporting of fresh insights into the impact and role of space in the interaction process.

关键词： Ubiquitous computing

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On the impact of the GOP size in a temporal H.264/AVC-to-SVC transcoder in baseline and main profile

On the impact of the GOP size in a temporal H.264/AVC-to-SVC...

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作者： Garrido-Cantos, R. De Cock, J. Martínez, J.L. Van Leuven, S. Cuenca, P. Garrido, A. Van De Walle, R. Albacete Research Institute of Informatics University of Castilla-La Mancha Campus Universitario s/n 02071 Albacete Spain Department of Electronics and Information Systems Multimedia Lab Ghent University Gaston Crommenlaan 8 bus 201 Ledeberg 9050 Ghent Belgium Architecture and Technology of Computing Systems Group Complutense University Ciudad Universitaria s/n 28040 Madrid Spain

Scalable video coding is a recent extension of the advanced video coding H.264/AVC standard developed jointly by ISO/IEC and ITU-T, which allows adapting the bitstream easily by dropping parts of it named layers. This adaptation makes it possible for a single bitstream to meet the requirements for reliable delivery of video to diverse clients over heterogeneous networks using temporal, spatial or quality scalability, combined or separately. Since the scalable video coding design requires scalability to be provided at the encoder side, existing content cannot benefit from it. Efficient techniques for converting contents without scalability to a scalable format are desirable. In this paper, an approach for temporal scalability transcoding from H.264/AVC to scalable video coding in baseline and main profile is presented and the impact of the GOP size is analyzed. Independently of the GOP size chosen, time savings of around 63 % for baseline profile and 60 % for main profile are achieved while maintaining the coding efficiency. © 2012 Springer-Verlag.

关键词： Scalable video coding

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Video transcoding for mobile digital television

Video transcoding for mobile digital television

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作者： Garrido-Cantos, R. De Cock, J. Martínez, J.L. Van Leuven, S. Garrido, A. Escuela Superior de Ingeniería Informática University of Castilla-La Mancha Edificio Infante Don Juan Manuel Avda. De España s/n 02071 Albacete Spain Department of Electronics and Information Systems Multimedia Lab Ghent University-IBBT Gaston Crommenlaan 8 bus 201 9050 Ledeberg Ghent Belgium Architecture and Technology of Computing Systems Group Complutense University Ciudad Universitaria s/n 28040 Madrid Spain

Mobile digital television is one of the new services introduced recently by telecommunications operators. Due to the possibilities of personalization and interaction provided, together with the increasing demand for this type of portable services, it will undoubtedly be a successful technology in the near future. Multimedia content is generally encoded by reducing the storage capacity necessary and bandwidth consumption in order to be transmitted. In order to adapt to the different characteristics of the networks and the varying capabilities of the devices, scalable video coding schemes have been proposed that provide temporal, spatial, and quality scalability, or a combination of these. Most of the existing video content is compressed using H.264/AVC, which is a single-layer codec, so these contents cannot benefit from the scalability tools due to the lack of intrinsic scalability provided in the bitstream at encoding time. This paper proposes a technique to convert from the single-layer H.264/AVC bitstream to a scalable bitstream with temporal scalability. Applying this approach, a reduction of 60% in coding complexity is achieved while maintaining the coding efficiency. © 2011 Springer Science+Business Media, LLC.

关键词： Scalable video coding

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Scalable mobile-to-mobile video communications based on an improved WZ-to-SVC transcoder

Scalable mobile-to-mobile video communications based on an i...

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18th International Conference on Multimedia Modeling, MMM 2012

作者： Corrales-Garcia, Alberto Martínez, José Luis Fernández-Escribano, Gerardo Quiles, Francisco Jose University of Castilla-La Mancha Campus Universitario Albacete 02071 Spain Architecture and Technology of Computing Systems Group Complutense University Ciudad Universitaria S/n Madrid 28040 Spain

ISBN: (纸本)9783642273544

Nowadays, video communications between mobile devices are one of the most demanded multimedia services. Since Wyner-Ziv coding provides low cost video encoding, it is a suitable codec to encode video with less resources. On the other hand, the video delivery provided by Scalable Video Coding covers the needs of a wide range of homogeneous networks and different devices. As a consequence, Wyner-Ziv to Scalable Video Coding transcoding can offer a suitable framework to support scalable video communications between low-cost devices. However, the complexity of the transcoder accumulates most of the complexity of both codecs and it must be reduced. In this paper, we introduced an improved Wyner-Ziv to Scalable Video Coding transcoding framework to support homogeneous mobile video communications. The experimental results show that the complexity is reduced around 83.5% without significant Rate-Distortion penalty. © 2012 Springer-Verlag.

关键词： Scalable video coding

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