检索结果-内蒙古大学图书馆

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

引用

Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

关键词：

来源：评论

学校读者我要写书评

暂无评论

Chinese wall security policies information flows in business cloud 3

Chinese wall security policies information flows in business...

引用

3rd IEEE International Conference on Big data, IEEE Big data 2015

作者： Lin, Tsau-Young T. Y. Institute of Data Science and Computing San Jose State University and GrC Society San JoseCA95192 United States

ISBN: (纸本)9781479999255

A Business Cloud is defined to be a collection of company datasets that are stored on the «Cloud». For simplicity, we have assumed: Each company only has one dataset. There are information flows among these datasets. Within such an environment Chinese Wall Security Policy (CWSP) is revisited. Based on the «physical» view of Brewer and Nash, the Chinese Wall policy that regulates «Conflict of Interest» binary Relation, denoted by CIR among company datasets, is investigated. CIR has found to be anti-reflexive, symmetric and anti-transitive, in other words, the compliment of CIR is an equivalence relation. The main theorem states that, from the owner (a company) view, a dataset under CWSP will neve flow into enemies hands (companies in conflicts). More generally, if CIR is anti-reflexive and symmetric, then there are Aggressive Chinese Wall that surrounds the company datasets of those companies who are in conflict, and a Conservative Chinese Wall that that surrounds the company datasets whose owner are friends;note that the set friends is a subset of «not in conflict». The main theorem states that no information of those company datasets that are stored within the Aggressive Chinese Wall may never penetrate the Wall, similar conclusion for Conservative Chinese Wall. However those company datasets that are outside these two walls may penetrate the two walls. © 2015 IEEE.

关键词： Set theory

来源：评论

学校读者我要写书评

暂无评论

A new evolving clustering algorithm for online data streams 10

A new evolving clustering algorithm for online data streams

引用

10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016

作者： Bezerra, Clauber Gomes Costa, Bruno Sielly Jales Guedes, Luiz Affonso Angelov, Plamen Parvanov Campus EaD Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Campus Natal - Zona Norte Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Brazil Lancaster University Data Science Group School of Computing and Communications LancasterLA1 4WA United Kingdom Department of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781509025831

In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations. Instead of the traditional concept of clusters, the data is grouped in the form of granular unities called data clouds, which are structures with no pre-defined shape or set boundaries. TEDA-Cloud is a fully autonomous and self-evolving algorithm that can be used for data clustering of online data streams and applications that require real-time response. Since it is fully autonomous, TEDA-Cloud is able to "start from scratch" (from an empty knowledge basis), create, update and merge data clouds, in a fully autonomous manner, without requiring any user-defined parameters (e.g. number of clusters, size, radius) or previous training. Moreover, TEDA-Cloud, unlike most of the traditional statistical approaches, does not rely on a specific data distribution or on the assumption of independence of data samples. The results, obtained from multiple data sets that are very well known in literature, are very encouraging. ©2016 IEEE.

关键词： Clustering algorithms

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

A dynamic microtask scheduling approach for SLO based human-augmented computing

A dynamic microtask scheduling approach for SLO based human-...

引用

International Conference on Recent Advances in Information Technology (RAIT)

作者： Koushik Sinha Pratham Majumder Geetha Manjunath Dept. of Computer Science Southern Illinois University Carbondale IL USA Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Data Analytics Research Lab Xerox Research Center India Bangalore India

ISBN: (纸本)9781479985807

Current machine algorithms for qualitative analysis of unstructured data (in the form of social media posts, audio and video, among others) do not perform well and show low accuracies due to the need for human-like intelligence. However, machines are easily scalable, fast and the quality of their output is predictable. On the other hand, though humans are much better than machine algorithms at image and video analysis, natural language text and speech processing, they are unfortunately unpredictable, slower and can be erroneous or even malicious as computing agents. Therefore, a task execution engine which can enable human-augmented cloud computing by intelligently orchestrating machine and human computing resources would be able to provide richer and superior analytics on unstructured data than either of the two types of computing agents in isolation. We believe that a key aspect of enabling such analytics would be to provide guaranteed service level objectives, in terms of accuracy, time and budget In this paper, we present a microtask scheduler with integrated service level objectives (SLO) management. With this goal, we have introduced two new decision parameters: H-M ratio and microtask completion rate. An early prototype has been built and validated through simulation with actual performance data collected from anonymous crowd workers on Amazon Mechanical Turk. Machine computation was done using Hewlett Packard's Autonomy IDOL while ground truth was established through the use of known, expert workers. To the best of our knowledge, ours is the first work that attempts to simultaneously attempt to address the three SLO parameters of accuracy, budget and deadline for data-parallel microtasks.

关键词： Manuals

来源：评论

学校读者我要写书评

暂无评论

Convolutional neural networks for transient candidate vetting in large-scale surveys

arXiv

引用

arXiv 2017年

作者： Gieseke, Fabian Bloemen, Steven van den Bogaard, Cas Heskes, Tom Kindler, Jonas Scalzo, Richard A. Ribeiro, Valério A.R.M. van Roestel, Jan Groot, Paul J. Yuan, Fang Möller, Anais Tucker, Brad E. Institute for Computing and Information Sciences Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Department of Computer Science University of Copenhagen Sigurdsgade 41 Copenhagen2200 Denmark Department of Astrophysics IMAPP Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands NOVA Optical InfraRed Instrumentation Group Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands Institute of Cognitive Science University of Osnabrück Wachsbleiche 27 Osnabrück49090 Germany Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Australia Centre for Translational Data Science University of Sydney DarlingtonNSW2008 Australia CIDMA Departamento de Física Universidade de Aveiro Campus de Santiago Aveiro3810-193 Portugal Instituto de Telecomunicações Campus de Santiago Aveiro3810-193 Portugal Department of Physics and Astronomy Botswana International University of Science and Technology Private Bay 16 Palapye Botswana

Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional preprocessing steps – eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3% of all "real"and 99.7% of all"bogus"instances on a test set containing 1,942"bogus"and 227 "real" instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all. Copyright © 2017, The Authors. All rights reserved.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

Fermi and Swift Observations of Grb 190114C: Tracing The Evolution of High-Energy Emission From Prompt to Afterglow

arXiv

引用

arXiv 2019年

作者： Ajello, M. Arimoto, M. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Burns, E. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Chiaro, G. Ciprini, S. Cohen-Tanugi, J. Costantin, D. Cutini, S. D’Ammando, F. DeKlotz, M. de la Torre Luque, P. de Palma, F. Desai, A. Lalla, N. Di Venere, L. Di Dirirsa, F. Fana Fegan, S.J. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Gill, R. Giordano, F. Giroletti, M. Granot, J. Green, D. Grenier, I.A. Grondin, M.-H. Guiriec, S. Hays, E. Horan, D. Jóhannesson, G. Kocevski, D. Kovacevic, M. Kuss, M. Larsson, S. Latronico, L. Lemoine-Goumard, M. Li, J. Liodakis, I. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Malyshev, D. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. McEnery, J.E. Mereu, I. Meyer, M. Michelson, P.F. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Omodei, N. Orienti, M. Orlando, E. Palatiello, M. Paliya, V.S. Paneque, D. Pei, Z. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Ryde, F. Parkinson, P.M. Saz Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tajima, H. Takagi, K. Takahashi, M.N. Tak, D. Thayer, J.B. Thompson, D.J. Torres, D.F. Troja, E. Valverde, J. van Klaveren, B. Wood, K. Yassine, M. Zaharijas, G. Bhat, P.N. Briggs, M.S. Cleveland, W. Giles, M. Goldstein, A. Hui, M. Malacaria, C. Preece, R. Roberts, O. Veres, P. von Kienlin, A. Cenko, S.B. O’Brien, P. Beardmore, A.P. Lien, A. Osborne, J.P. Tohuvavohu, A. D’Elia, V. Da, A. Perri, M. Gropp, J. Klingler, N. Capalbi, M. Tagliaferri, G. Stamatikos, M. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Kanazawa Ishikawa920-1192 Department of Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 Italy Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany NASA Goddard Space Flight Center GreenbeltMD20771 United States NASA Postdoctoral Program Fellow United States In

We report on the observations of gamma-ray burst (GRB) 190114C by the Fermi Gamma-ray Space Telescope and the Neil Gehrels Swift Observatory. The prompt gamma-ray emission was detected by the Fermi Gamma-ray Burst Monitor (GBM), the Fermi Large Area Telescope (LAT), and the Swift Burst Alert Telescope (BAT) and the long-lived afterglow emission was subsequently observed by the GBM, LAT, Swift X-ray Telescope (XRT), and Swift UV Optical Telescope (UVOT). The early-time observations reveal multiple emission components that evolve independently, with a delayed power-law component that exhibits significant spectral attenuation above 40 MeV in the first few seconds of the burst. This power-law component transitions to a harder spectrum that is consistent with the afterglow emission observed by the XRT at later times. This afterglow component is clearly identifiable in the GBM and BAT light curves as a slowly fading emission component on which the rest of the prompt emission is superimposed. As a result, we are able to observe the transition from internal shock to external shock dominated emission. We find that the temporal and spectral evolution of the broadband afterglow emission can be well modeled as synchrotron emission from a forward shock propagating into a wind-like circumstellar environment. We estimate the initial bulk Lorentz factor using the observed high-energy spectral cutoff. Considering the onset of the afterglow component, we constrain the deceleration radius at which this forward shock begins to radiate in order to estimate the maximum synchrotron energy as a function of time. We find that even in the LAT energy range, there exist high-energy photons that are in tension with the theoretical maximum energy that can be achieved through synchrotron emission from a shock. These violations of the maximum synchrotron energy are further compounded by the detection of very high energy (VHE) emission above 300 GeV by MAGIC concurrent with our observations. We con

关键词： Stars

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Rendezvous based routing protocol for wireless sensor networks with mobile sink

引用

The Journal of Supercomputing 2016年第3期73卷 1189-1191页

作者： Suraj Sharma Deepak Puthal Sanjay Kumar Jena Albert Y. Zomaya Rajiv Ranjan Department of Computer Science and Engineering IIIT Bhubaneswar Khurda India CSIRO Data61 Sydney Australia Department of CSE National Institute of Technology Rourkela Rourkela India School of Information Technologies The University of Sydney Sydney Australia School of Computing Science Newcastle University Newcastle upon Tyne UK School of Computer Science China University of Geosciences Beijing China

来源：评论

学校读者我要写书评

暂无评论

Human mobility: Models and applications

arXiv

引用

arXiv 2017年

作者： Barbosa-Filho, Hugo Barthelemy, Marc Ghoshal, Gourab James, Charlotte R. Lenormand, Maxime Louail, Thomas Menezes, Ronaldo Ramasco, José J. Simini, Filippo Tomasini, Marcello Department of Physics & Astronomy Goergen Institute of Data Science University of Rochester RochesterNY United States Institut de Physique Théorique CEA CNRS-URA 2306 Gif-sur-YvetteF-91191 France Centre d'Etudes et de Mathématique Sociales EHESS Paris75006 France Department of Engineering Mathematics University of Bristol United Kingdom Irstea UMR TETIS 500 rue JF Breton Montpellier34093 France Campus UIB Palma de Mallorca07122 Spain CNRS UMR 8504 Géographie-cités 13 rue du four ParisF-75006 France BioComplex Laboratory School of Computing Florida Institute of Technology Melbourne United States

Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between shortrange and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications. Copyright © 2017, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals

引用

Procedia Computer science 2016年 88卷 176-184页

作者： Lijuan Duan Menghu Bao Jun Miao Yanhui Xu Juncheng Chen College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100124 China

Classification of motor imagery electroencephalogram (EEG) is one of the most important technologies for BCI. To improve the accuracy, this paper introduces a classification system based on Multilayer Extreme Learning Machine (ML-ELM). In the system, the combination of PCA and LDA is chosen as the method of feature extraction and the ML-ELM is used to classify. The ML-ELM has not only the advantage which ELM has but also better performance than ELM. In the experiment, our method is compared with the methods based on ELM, such as kernel-ELM, Constrained-ELM and V-ELM, and some state-of–the-art methods on the same dataset. The experimental results show that ML-ELM is much more suitable for motor imagery EEG data and has better performance than the others.

关键词： Electroencephalogram Classification Motor Imagery Extreme Learning Machine Multilayer Extreme Learning Machine

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：