检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Rabault, Jean Taelman, Catherine Idžanović, Martina Hope, Gaute Nose, Takehiko Kristoffersen, Yngve Jensen, Atle Breivik, Øyvind Bryhni, Helge Thomas Hoppmann, Mario Demchev, Denis Korosov, Anton Johansson, Malin Eltoft, Torbjørn Dagestad, Knut-Frode Röhrs, Johannes Eriksson, Leif Moro, Marina Durán Rikardsen, Edel S.U. Waseda, Takuji Kodaira, Tsubasa Lohse, Johannes Desjonquères, Thibault Olsen, Sveinung Gundersen, Olav de Aguiar, Victor Cesar Martins Karlsen, Truls Babanin, Alexander Voermans, Joey Park, Jeong-Won Müller, Malte Norwegian Meteorological Institute IT Department Oslo0313 Norway UiT The Arctic University of Norway Dept. of Physics and Technology Tromsø9037 Norway Norwegian Meteorological Institute Division for Ocean and Ice Oslo0371 Norway Norwegian Meteorological Institute Division for Oceanography and Marine Meteorology Bergen5007 Norway The University of Tokyo Graduate School of Frontier Sciences Kashiwa277-8561 Japan University of Bergen Department of Earth Science Bergen5007 Norway University of Oslo Department of Mathematics Oslo0313 Norway University of Bergen Geophysical Institute Bergen5007 Norway Alfred-Wegener-Institut Helmholtz-Zentrum fuer Polar- und Meeresforschung Bremerhaven27580 Germany Department of Space Earth and Environment Chalmers University of Technology Gothenburg41296 Sweden Ocean and Sea Ice Remote Sensing Group Nansen Environmental and Remote Sensing Center Bergen5006 Norway Norwegian Meteorological Institute Remote sensing and data management division Oslo Norway University of Oslo Department of Geosciences Oslo0313 Norway Japan Agency for Marine-Earth Science and Technology Yokosuka237-0061 Japan Gothenburg University Gothenburg40508 Sweden Department of Infrastructure Engineering University of Melbourne Melbourne Australia Center of Remote Sensing and GIS Korea Polar Research Institute Incheon21990 Korea Republic of Norwegian Meteorological institute R&D Department Oslo0313 Norway

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms. © 2024, CC BY.

关键词： Water waves

来源：评论

学校读者我要写书评

暂无评论

Apercal - The Apertif calibration pipeline

arXiv

引用

arXiv 2021年

作者： Adebahr, Björn Schulz, R. Dijkema, T.J. Moss, V.A. Offringa, A.R. Kutkin, A. van der Hulst, J.M. Frank, B.S. Vilchez, N.P.E. Verstappen, J. Adams, E.K. de Blok, W.J.G. Denes, H. Hess, K.M. Lucero, D. Morganti, R. Oosterloo, T. Pisano, D.-J. Ivashina, M.V. van Cappellen, W.A. Connor, L.D. Coolen, A.H.W.M. Damstra, S. Loose, G.M. Maan, Y. Maccagni, F.M. Mika, A. Mulder, H. Oostrum, L.C. Orrú, E. Smits, R. van der Schuur, D. van Leeuwen, J. Vohl, D. Wijnholds, S.J. Ziemke, J. Bochum Germany ASTRON Dwingeloo Netherlands CSIRO Astronomy and Space Science Sydney Australia University of Sydney Sydney Australia Cavendish Laboratory Cambridge United Kingdom Kapteyn Astronomical Institute University of Groningen Groningen Netherlands Department of Physics Virginia Tech BlacksburgVA United States Astro Space Center of Lebedev Physical Institute Profsoyuznaya Str. 84/32 Moscow117997 Russia 2 Fir Street Observatory 7925 South Africa Inter-University Institute for Data Intensive Astronomy Department of Astronomy University of Cape Town Private Bag X3 Rondebosch 7701 South Africa University of Cape Town University Private Bag Rondebosch Cape Town7701 South Africa Department of Physics and Astronomy West Virginia University White Hall Box 6315 MorgantownWV26506 United States Dept. of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Anton Pannekoek Institute University of Amsterdam Postbus 94249 Amsterdam1090 GE Netherlands Rijksuniversiteit Groningen Center for Information Technology P.O. Box 11044 Groningen9700 CA Netherlands Netherlands eScience Center Science Park 140 Amsterdam1098 XG Netherlands INAF Osservatorio Astronomico di Cagliari Via della Scienza 5 CA Selargius09047 Italy

Apertif (APERture Tile In Focus) is one of the Square Kilometre Array (SKA) pathfinder facilities. The Apertif project is an upgrade to the 50-year-old Westerbork Synthesis Radio Telescope (WSRT) using phased-array feed technology. The new receivers create 40 individual beams on the sky, achieving an instantaneous sky coverage of 6.5 square degrees. The primary goal of the Apertif Imaging Survey is to perform a wide survey of 3500 square degrees (AWES) and a medium deep survey of 350 square degrees (AMES) of neutral atomic hydrogen (up to a redshift of 0.26), radio continuum emission and polarisation. Each survey pointing yields 4.6 TB of correlated data. The goal of Apercal is to process this data and fully automatically generate science ready data products for the astronomical community while keeping up with the survey observations. We make use of common astronomical software packages in combination with Python based routines and parallelisation. We use an object oriented module-based approach to ensure easy adaptation of the pipeline. A Jupyter notebook based framework allows user interaction and execution of individual modules as well as a full automatic processing of a complete survey observation. If nothing interrupts processing, we are able to reduce a single pointing survey observation on our five node cluster with 24 physical cores and 256 GB of memory each within 24h keeping up with the speed of the surveys. The quality of the generated images is sufficient for scientific usage for 44 % of the recorded data products with single images reaching dynamic ranges of several thousands. Future improvements will increase this percentage to over 80 %. Our design allowed development of the pipeline in parallel to the commissioning of the Apertif system. © 2021, CC BY.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

arXiv

引用

arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

The Effect of Relative Humidity on Eddy Covariance Latent Heat Flux Measurements and its Implication for Partitioning into Transpiration and Evaporation

SSRN

引用

SSRN 2022年

作者： Zhang, Weijie Jung, Martin Migliavacca, Mirco Poyatos, Rafael Miralles, Diego El-Madany, Tarek S. Galvagno, Marta Carrara, Arnaud Arriga, Nicola Ibrom, Andreas Mammarella, Ivan Papale, Dario Cleverly, Jamie Liddell, Michael J. Wohlfahrt, Georg Markwitz, Christian Mauder, Matthias Paul-Limoges, Eugenie Schmidt, Marius Wolf, Sebastian Brümmer, Christian Arain, M. Altaf Fares, Silvano Kato, Tomomichi Ardö, Jonas Oechel, Walter Hanson, Chad Korkiakoski, Mika Biraud, Sébastien Steinbrecher, Rainer Billesbach, Dave Montagnani, Leonardo Woodgate, William Shao, Changliang Carvalhais, Nuno Reichstein, Markus Nelson, Jacob A. Department of Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena Germany Faculty of Bioscience Engineering Ghent University Ghent Belgium Italy CREAF Cerdanyola del Vallès Spain Universitat Autònoma de Barcelona Cerdanyola del Vallès Spain Climate Change Unit Environmental Protection Agency of Aosta Valley Aosta Italy Fundación Centro de Estudios Ambientales del Mediterráneo Valencia Paterna46980 Spain Technical University of Denmark Dept. of Environment and Resource Management Kgs. Lyngby Denmark Faculty of Science University of Helsinki P.O. Box 68 Helsinki00014 Finland University of Tuscia - DIBAF Viterbo01100 Italy IAFES Viterbo01100 Italy College of Science and Engineering James Cook University CairnsQLD4878 Australia Department of Ecology University of Innsbruck Innsbruck Austria Bioclimatology University of Göttingen Göttingen Germany Technische Universität Dresden Faculty of Environmental Sciences Dresden Germany Department of Geography University of Zurich Zurich8057 Switzerland Agrosphere Institute IBG-3 Forschungszentrum Jülich GmbH Jülich52425 Germany Department of Environmental Systems Science ETH Zurich Zurich8092 Switzerland Thünen Institute of Climate-Smart Agriculture Braunschweig Germany School of Earth Environment and Society McMaster University HamiltonON Canada National Research Council of Italy Institute of Bioeconomy Rome Italy Research Faculty of Agriculture Hokkaido University Sapporo Japan Department of Physical Geography and Ecosystem Science Lund University Sweden Global Change Research Group San Diego State University San DiegoCA United States Department of Geography University of Exeter Exeter United Kingdom Department of Forests Ecosystems and Society College of Forestry Oregon State University CorvallisOR United States Climate Sciences Department Lawrence Berkeley National Laboratory BerkeleyCA United States Garmisch-Partenkirchen Germany University of Nebraska

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the method is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. The influence of these errors for different types of EC systems (e.g. open- or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of ET from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that high RH errors were most apparent during hours when RH was approximately 70% or higher. This underestimation was predominantly observed at sites using closed-path EC systems, with ET underestimations estimated at 58% when RH was higher than 90%. To assess the potential impact of high RH on ET and EC-based transpiration (T) estimates, ET was corrected using a machine learning based empirical approach and then partitioned into T and E using two different data-driven methods. Results showed opposite (increasing vs decreasing T-to-ET ratios, T/ET) responses owing to the contrasting ways the two methods model WUE. Analysis of T estimates based on three different ET correction approaches showed no clear improvement in correlation with independent sap flow based T. Overall, our results demonstrate the existence of high RH-related bias in water fluxes in the FLUXNET2015 dataset and suggest that the underestimation of ET is a significant source of uncertainty that must be reckoned with when estimating ecosystem T/ET and WUE. This work further demonstrates the need for high-quality, independent ecosystem water flux measurements to understand the uncertainties in EC-based ET and T estimates. © 2022, The Authors. All rights reserved.

关键词： Evapotranspiration

来源：评论

学校读者我要写书评

暂无评论

Preface

引用

Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2000年 1803卷 6-6页

作者： Cagnoni, Stefano Poli, Riccardo Li, Yun Smith, George Corne, David Oates, Martin Hart, Emma Lanzi, Pier Luca Boers, Egbert J. W. Paechter, Ben Fogarty, Terence C. University of Parma Department of Computer Engineering Parco delle Scienze 181/a Parma Italy The University of Birmingham School of Computer Science Edgbaston Birmingham United Kingdom University of Glasgow Electronics and Electrical Engineering Rankine Building Glasgow United Kingdom University of East Anglia Department of Information Systems Norwich United Kingdom University of Reading P.O. Box 225 Whiteknights Reading United Kingdom BT Labs at Adastral Park Dept. Complex Systems Laboratory Martlesham Heath Suffolk United Kingdom University of Edinburgh Division of Informatics 80 South Bridge Edinburgh United Kingdom Milan Polytechnic Department of Electronics and Informatics Piazza Leonard0 da Vinci 32 Milan Italy National Aerospace Laboratory NLR Data and Knowledge Systems Dept I.C.T. Division Anthony Fokkerweg 2 Amsterdam Netherlands Napier University School of Computing 219 Colinton Road Edinburgh United Kingdom South Bank University School of Computing Information Systems and Mathematics 103 Borough Road London United Kingdom

来源：评论

学校读者我要写书评

暂无评论

2022 Roadmap on Neuromorphic Computing and engineering

arXiv

引用

arXiv 2021年

作者： Christensen, Dennis Valbjørn Dittmann, Regina Linares-Barranco, Bernabé Sebastian, Abu Le Gallo, Manuel Redaelli, Andrea Slesazeck, Stefan Mikolajick, Thomas Spiga, Sabina Menzel, Stephan Valov, Ilia Milano, Gianluca Ricciardi, Carlo Liang, Shi-Jun Miao, Feng Lanza, Mario Quill, Tyler J. Keene, Scott T. Salleo, Alberto Grollier, Julie Marković, Danijela Mizrahi, Alice Yao, Peng Yang, J. Joshua Indiveri, Giacomo Strachan, John Paul Datta, Suman Vianello, Elisa Valentian, Alexandre Feldmann, Johannes Li, Xuan Pernice, Wolfram H.P. Bhaskaran, Harish Furber, Steve Neftci, Emre Scherr, Franz Maass, Wolfgang Ramaswamy, Srikanth Tapson, Jonathan Panda, Priyadarshini Kim, Youngeun Tanaka, Gouhei Thorpe, Simon Bartolozzi, Chiara Cleland, Thomas A. Posch, Christoph Liu, Shih-Chii Panuccio, Gabriella Mahmud, Mufti Mazumder, Arnab Neelim Hosseini, Morteza Mohsenin, Tinoosh Donati, Elisa Tolu, Silvia Galeazzi, Roberto Christensen, Martin Ejsing Holm, Sune Ielmini, Daniele Pryds, N. Department of Energy Conversion and Storage Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Peter Gruenberg Institute 7 Forschungszentrum Juelich GmbH Juelich52425 Germany JARA-FIT RWTH Aachen University Aachen52056 Germany CSIC Universidad de Sevilla Seville41092 Spain IBM Research Zurich Switzerland STMicroelectronics Agrate Italy Dresden01187 Germany Institute of Semiconductors and Microsystems TU Dresden Dresden Germany CNR-IMM Unit of Agrate Brianza via C. Olivetti 2 MB Agrate Brianza Italy Juelich Germany Torino Italy Department of Applied Science and Technology Politecnico di Torino Torino Italy National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China Thuwal23955-6900 Saudi Arabia Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Unité Mixte de Physique CNRS Thales Université Paris-Saclay Palaiseau91767 France Electrical and Computer Engineering Department University of Southern California Los AngelesCA United States Institute of Neuroinformatics University of Zurich ETH Zurich Switzerland Hewlett Packard Laboratories Hewlett Packard Enterprise San JoseCA United States Department of Electrical Engineering University of Notre Dame Notre DameIN United States CEA LETI Université Grenoble Alpes Grenoble France CEA LIST Université Grenoble Alpes Grenoble France Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom Institute of Physics University of Münster Heisenbergstr. 11 Heisenbergstr Münster48149 Germany Center for Soft Nanoscience University of Münster Münster48149 Germany The University of Manchester United Kingdom Department of Cognitive Sciences University of California Irvine IrvineCA United States Institute of Theoretica

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community. © 2021,

关键词： data transfer

来源：评论

学校读者我要写书评

暂无评论

Cover Picture: ConSurf: Using Evolutionary data to Raise Testable Hypotheses about Protein Function / JSmol and the Next-Generation Web-Based Representation of 3D Molecular Structure as Applied toProteopedia(Isr. J. Chem. 3-4/2013)

引用

Israel Journal of Chemistry 2013年第3‐4期53卷

作者： Gershon Celniker Guy Nimrod Haim Ashkenazy Fabian Glaser Eric Martz Itay Mayrose Tal Pupko Nir Ben-Tal Robert M. Hanson Jaime Prilusky Zhou Renjian Takanori Nakane Joel L. Sussman The Department of Biochemistry and Molecular Biology Tel Aviv University 69978 Tel Aviv (Israel) The Department of Cell Research and Immunology Tel Aviv University 69978 Tel Aviv (Israel) The Department of Molecular Biology and Ecology of Plants Tel Aviv University 69978 Tel Aviv (Israel) Present address: Biolojic Design Akiva Aria 25 Tel Aviv 6215425 (Israel) Bioinformatics Knowledge Unit The Lorry I. Lokey Interdisciplinary Center for Life Sciences and Engineering Technion – Israel Institute of Technology Haifa 32000 (Israel) Department of Microbiology University of Massachusetts Amherst MA (USA) Joel L. Sussman Dept. of Structural Biology and the Israel Structural Proteomics Center Weizmann Institute of Science Rehovot 76100 (Israel) phone +972-8-934-4531 fax: +972-8-934-6312 St. Olaf College Dept. of Chemistry Northfield MN 55057 (USA) R. M. H. and J. P. made equal contribution to this paper. Bioinformatics Unit Biological Services Weizmann Institute of Science Rehovot 76100 (Israel) Room 202 Building 38 Nianjiabang Road 425 Zhoupu Town Pudong District Shanghai 201318 (China) Dept. of Cell Biology Graduate School of Medicine Kyoto University Yoshidakonoe-cho Sakyo-ku Kyoto 606-8501 (Japan) Dept. of Structural Biology and the Israel Structural Proteomics Center Weizmann Institute of Science Rehovot 76100 (Israel) phone +972-8-934-4531 fax: +972-8-934-6312 Nir Ben-Tal The Department of Biochemistry and Molecular Biology Tel Aviv University 69978 Tel Aviv (Israel)

来源：评论

学校读者我要写书评

暂无评论

MICROVASCULATURE-ON-CHIP AS A RADIOBIOLOGICAL MODEL TO STUDY THE EFFECTS OF CARBON ION IRRADIATION

International Journal of Particle Therapy

引用

International Journal of Particle Therapy 2024年 12卷

作者： Luca Possenti Michela Magnoni Tommaso Giandini Alexandra Charalampopoulou Alfredo Mirandola Valentina Doldi Angelica Facoetti Chiara Arrigoni Maria Laura Costantino Ester Orlandi Nadia Zaffaroni Marco Pullia Matteo Moretti Tiziana Rancati Data Science Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Chemistry Materials And Chemical Engineering Dept. Politecnico di Milano Milan Italy Medical Physics Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy National Center for Oncological Hadrontherapy (CNAO) Pavia Italy University School for Advanced Studies IUSS Pavia Italy Molecular Pharmacology Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Rmt Lab Service Of Orthopaedics And Traumatology Ente Ospedaliero Cantonale Bellinzona Switzerland Euler Institute Biomedical Sciences Faculty Università della Svizzera Italiana Lugano Switzerland Department Of Clinical Surgical Diagnostic And Pediatric Sciences University of Pavia Pavia Italy Cell And Tissue Engineering Laboratory IRCCS Istituto Ortopedico Galeazzi Milan Italy

来源：评论

学校读者我要写书评

暂无评论

The large area detector of LOFT: The Large Observatory for X-ray Timing

引用

Proceedings of SPIE - The International Society for Optical engineering 2014年 9144卷

作者： Zane, S. Walton, D. Kennedy, T. Feroci, M. Den Herder, J.-W. Ahangarianabhari, M. Argan, A. Azzarello, P. Baldazzi, G. Barbera, M. Barret, D. Bertuccio, G. Bodin, P. Bozzo, E. Bradley, L. Cadoux, F. Cais, P. Campana, R. Coker, J. Cros, A. Del Monte, E. De Rosa, A. Di Cosimo, S. Donnarumma, I. Evangelista, Y. Favre, Y. Feldman, C. Fraser, G. Fuschino, F. Grassi, M. Hailey, M.R. Hudec, R. Labanti, C. Malcovati, P. Macera, D. Marisaldi, M. Martindale, A. Mineo, T. Muleri, F. Nowak, M. Orlandini, M. Pacciani, L. Perinati, E. Petracek, V. Pohl, M. Rachevski, A. Smith, P. Santangelo, A. Seyler, J.-Y. Schmid, C. Soffitta, P. Suchy, S. Tenzer, C. Uttley, P. Vacchi, A. Zampa, G. Zampa, N. Wilms, J. Winter, B. Mullard Space Science Laboratory UCL Holmbury St Mary Dorking SurreyRH56NT United Kingdom INAF-IAPS-Roma via Fosso del Cavaliere 100 Rome00133 Italy SRON Netherlands Institute of Space Research Utrecht Netherlands Politecnico di Milano Como Campus via Anzani 42 Como22100 Italy INAF HQ Viale del Parco Mellini 84 Rome00136 Italy ISDC Science Data Center for Astrophysics Ch. D'Ecogia 16 Versoix Geneva1290 Switzerland University of Bologna Dept. of Physics and INFN Section of Bologna V.le Berti Pichat 6/2 Bologna40127 Italy Institut de Recherche en Astrophysique et Planetologie IRAP 9 Avenue du Colonel Roche ToulouseBP44346 31028 France Centre National D'Etudes Spatiales Centre Spatial de Toulouse 18 Avenue Edouard Belin Toulouse CEDEX 9401 France DPNC Geneva University Quai Ernest-Ansermet 24 GenevaCH1211 Switzerland Laboratoire d'Astrophysique de Bordeaux Univ. Bordeaux CNRS UMR5804 FloiracBP 89 33270 France Space Research Centre Department of Physics and Astronomy University of Leicester LeicesterLE17RH United Kingdom INAF/IASF Bologna via Gobetti 101 Bologna40129 Italy University of Pavia via Ferrata 1 Pavia27100 Italy Czech Technical University in Prague Faculty of Electrical Engineering and Astronomical Institute Academy of Science of the Czech Republic Ondrejov Czech Republic INAF/IASF Palermo via Ugo la Malfa 153 Palermo90146 Italy MIT NE80-6077 77 Massachusetts Ave CambridgeMA02139 United States IAAT University of Tuebingen Sand 1 Tuebingen72076 Germany Czech Technical University in Prague Faculty of Nuclear Science Prague Czech Republic Istituto Nazionale di Fisica Nucleare INFN Sezione di Trieste Padriciano 99 TriesteI-34149 Italy Dr Remeis-Observatory and ECAP University of Erlangen-Nuremberg Sternwartstr. 7 Bamberg96049 Germany Astronomical Institute Anton Pannokoek University of Amsterdam Postbus 94249 AmsterdamGE1090 Netherlands Dipartimento di Fisica e Chimica Palermo University Via Archirafi

ISBN: (纸本)9780819496126

LOFT (Large Observatory for X-ray Timing) is one of the five candidates that were considered by ESA as an M3 mission (with launch in 2022-2024) and has been studied during an extensive assessment phase. It is specifically designed to perform fast X-ray timing and probe the status of the matter near black holes and neutron stars. Its pointed instrument is the Large Area Detector (LAD), a 10 m2-class instrument operating in the 2-30keV range, which holds the capability to revolutionise studies of variability from X-ray sources on the millisecond time scales. The LAD instrument has now completed the assessment phase but was not down-selected for launch. However, during the assessment, most of the trade-offs have been closed leading to a robust and well documented design that will be reproposed in future ESA calls. In this talk, we will summarize the characteristics of the LAD design and give an overview of the expectations for the instrument capabilities. © 2014 SPIE.

关键词： X ray detectors Buildings Economic and social effects Gamma rays Observatories Space telescopes Stars Timing circuits X rays Assessment phase Black holes Compact objects Large area detector Neutron stars Time scales Timing X ray sources

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：