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Journal articles on the topic 'Tagging'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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1

Mursyit, Mohammad, Aji Prasetya Wibawa, Ilham Ari Elbaith Zaeni, and Harits Ar Rosyid. "Pelabelan Kelas Kata Bahasa Jawa Menggunakan Hidden Markov Model." Mobile and Forensics 2, no. 2 (August 29, 2020): 71–83. http://dx.doi.org/10.12928/mf.v2i2.2450.

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Part of Speech Tagging atau POS Tagging adalah proses memberikan label pada setiap kata dalam sebuah kalimat secara otomatis. Penelitian ini menggunakan algoritma Hidden Markov Model (HMM) untuk proses POS Tagging. Perlakuan untuk unknown words menggunakan Most Probable POS-Tag. Dataset yang digunakan berupa 10 cerita pendek berbahasa Jawa terdiri dari 10.180 kata yang telah diberikan tagsetBahasa Jawa. Pada penelitian ini proses POS Tagging menggunakan dua skenario. Skenario pertama yaitu menggunakan algoritma Hidden Markov Model (HMM) tanpa menggunakan perlakuan untuk unknown words. Skenario yang kedua menggunakan HMM dan Most Probable POS-Tag untuk perlakuan unknown words. Hasil menunjukan skenario pertama menghasilkan akurasi sebesar 45.5% dan skenario kedua menghasilkan akurasi sebesar 70.78%. Most Probable POS-Tag dapat meningkatkan akurasi pada POS Tagging tetapi tidak selalu menunjukan hasil yang benar dalam pemberian label. Most Probable POS-Tag dapat menghilangkan probabilitas bernilai Nol dari POS Tagging Hidden Markov Model. Hasil penelitian ini menunjukan bahwa POS Tagging dengan menggunakan Hidden Markov Model dipengaruhi oleh perlakuan terhadap unknown words, perbendaharaan kata dan hubungan label kata pada dataset.  Part of Speech Tagging or POS Tagging is the process of automatically giving labels to each word in a sentence. This study uses the Hidden Markov Model (HMM) algorithm for the POS Tagging process. Treatment for unknown words uses the Most Probable POS-Tag. The dataset used is in the form of 10 short stories in Javanese consisting of 10,180 words which have been given the Javanese tagset. In this study, the POS Tagging process uses two scenarios. The first scenario is using the Hidden Markov Model (HMM) algorithm without using treatment for unknown words. The second scenario uses HMM and Most Probable POS-Tag for treatment of unknown words. The results show that the first scenario produces an accuracy of 45.5% and the second scenario produces an accuracy of 70.78%. Most Probable POS-Tag can improve accuracy in POS Tagging but does not always produce correct labels. Most Probable POS-Tag can remove zero-value probability from POS Tagging Hidden Markov Model. The results of this study indicate that POS Tagging using the Hidden Markov Model is influenced by the treatment of unknown words, vocabulary and word label relationships in the dataset.
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2

Rafferty, Pauline. "Tagging." KNOWLEDGE ORGANIZATION 45, no. 6 (2018): 500–516. http://dx.doi.org/10.5771/0943-7444-2018-6-500.

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3

Müller-Prove, Matthias. "Taxonomien und Folksonomien – Tagging als neues HCI-Element (Taxonomies and Folksonomies – Tagging as a New HCI Element)." i-com 6, no. 1 (May 2007): 14–18. http://dx.doi.org/10.1524/icom.2007.6.1.14.

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Community-Portale bieten immer häufiger kollaborative Tagging-Systeme zur zusätzlichen Auszeichnung ihrer Informationen an. Damit etabliert sich gerade ein neues Interface-Element, das das Potenzial zu einem erweiterten Umgang mit den Daten der gesamten digitalen Welt hat. Dieser Artikel beschäftigt sich mit den interaktiven Aspekten des Taggings, sowie mit der semantischen Ebene in Abgrenzung zum etablierten Wissensbegriff.
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4

Kang, Sin-Jae. "Learning Tagging Ontology from Large Tagging Data." Journal of Korean Institute of Intelligent Systems 18, no. 2 (April 25, 2008): 157–62. http://dx.doi.org/10.5391/jkiis.2008.18.2.157.

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5

Hearn, William S., Kenneth H. Pollock, and Elizabeth N. Brooks. "Pre- and post-season tagging models: estimation of reporting rate and fishing and natural mortality rates." Canadian Journal of Fisheries and Aquatic Sciences 55, no. 1 (January 1, 1998): 199–205. http://dx.doi.org/10.1139/f97-243.

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Brownie et al. (1985, U.S. Fish Wildl. Serv. Resour. Publ. 156, p. 159) presented models for tag returns from multiple taggings of animals when tagging is done twice per year. Here, we present a reformulation of their model suitable for pre- and post-season fishery tag return studies. Under this model, it is possible to estimate fishing mortality, natural mortality, and reporting rate from the tag return data alone. (Under once-a-year tagging models, the reporting rate usually has to be estimated externally.) We consider two special cases: (i) a pulse fishery and (ii) a continuous fishery over part of the year. An artificial example and simulation results are presented to illustrate the methodology and the properties of the various estimators. Unlike for catch-based methods, the correlation between estimates of fishing mortality and natural mortality is moderate. While pre- and post-season tagging studies are likely to be difficult to run in practice, other methods of estimating reporting rate are also difficult to implement, and therefore, this approach may prove quite useful, especially in fisheries that have heavy exploitation rates.
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6

Bozzi, Nicola. "Tagging Aesthetics." A Peer-Reviewed Journal About 9, no. 1 (August 4, 2020): 70–81. http://dx.doi.org/10.7146/aprja.v9i1.121490.

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Social media have given social movements unprecedented tools for self-representation, however emancipatory identity politics are drowned out by the white noise of neoliberal self-branding practices. In response to this highly- aestheticised, de-politicised environment, we need a cultural re-negotiation of online categorisation. Rather than focusing on networks, this essay frames tagging as an everyday gesture of social media users that participates in the collective performance of identity. I argue this performance gives way to the materialisation of 'cultural avatars' – collective identity figures that lie beyond coherent representation and can reinforce reductive social stereotypes or inspire politically critical figurations. Apart from offering a cultural critique of tagging itself, the essay discusses a range of creative approaches to tagging that de-naturalise processes of online categorisation by drawing critical attention towards them.
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7

Marr, James F. "Electronic tagging." Nursing Standard 4, no. 9 (November 22, 1989): 54. http://dx.doi.org/10.7748/ns.4.9.54.s56.

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8

Brizzard, Bill. "Epitope tagging." BioTechniques 44, no. 5 (April 2008): 693–95. http://dx.doi.org/10.2144/000112841.

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9

Rubinstein, Daniel. "Tag, Tagging." Philosophy of Photography 1, no. 2 (December 1, 2010): 197–200. http://dx.doi.org/10.1386/pop.1.2.197_7.

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10

Wang, Meng, Bingbing Ni, Xian-Sheng Hua, and Tat-Seng Chua. "Assistive tagging." ACM Computing Surveys 44, no. 4 (August 2012): 1–24. http://dx.doi.org/10.1145/2333112.2333120.

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11

Plehn, Tilman, and Michael Spannowsky. "Top tagging." Journal of Physics G: Nuclear and Particle Physics 39, no. 8 (May 25, 2012): 083001. http://dx.doi.org/10.1088/0954-3899/39/8/083001.

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12

Wattenburg, B. "Fluorescent Tagging." Science 266, no. 5190 (December 2, 1994): 1461. http://dx.doi.org/10.1126/science.266.5190.1461.

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13

EVERTS, SARAH. "TAGGING CHOLESTEROL." Chemical & Engineering News Archive 89, no. 4 (January 24, 2011): 8. http://dx.doi.org/10.1021/cen-v089n004.p008a.

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14

Voas, Jeffrey, and Nir Kshetri. "Human Tagging." Computer 50, no. 10 (2017): 78–85. http://dx.doi.org/10.1109/mc.2017.3641646.

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15

LILLY, J. ROBERT. "Tagging Reviewed." Howard Journal of Criminal Justice 29, no. 4 (November 1990): 229–45. http://dx.doi.org/10.1111/j.1468-2311.1990.tb00685.x.

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16

d'Enfert, Christophe. "Pathogen tagging." Trends in Microbiology 6, no. 8 (August 1998): 304. http://dx.doi.org/10.1016/s0966-842x(98)01315-8.

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17

Zeilstra-Ryalls, Jill H., and Ronald L. Somerville. "Fragment tagging." Gene Analysis Techniques 7, no. 6 (October 1990): 151–59. http://dx.doi.org/10.1016/0735-0651(90)90029-f.

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18

Jarvik, Jonathan W., and Cheryl A. Telmer. "EPITOPE TAGGING." Annual Review of Genetics 32, no. 1 (December 1998): 601–18. http://dx.doi.org/10.1146/annurev.genet.32.1.601.

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19

Stacey, Tom. "Offender tagging." International Review of Law, Computers & Technology 4, no. 1 (January 1989): 197–200. http://dx.doi.org/10.1080/13600869.1989.9966288.

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20

Neff, Ellen P. "Tagging mitoribosomes." Lab Animal 49, no. 1 (December 18, 2019): 19. http://dx.doi.org/10.1038/s41684-019-0457-9.

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21

Zaborowski, Betsy A. "Beyond tagging." ACM SIGACCESS Accessibility and Computing, no. 77-78 (September 2003): 1. http://dx.doi.org/10.1145/1029014.1028631.

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22

C.A.P. "Transposon tagging." Plant Molecular Biology Reporter 6, no. 1 (March 1988): 4–5. http://dx.doi.org/10.1007/bf02675299.

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23

Wattenburg, Bill. "Fluorescent Tagging." Science 266, no. 5190 (December 2, 1994): 1461. http://dx.doi.org/10.1126/science.266.5190.1461.a.

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24

Bethany Halford. "Tagging tryptophan." C&EN Global Enterprise 102, no. 9 (March 25, 2024): 6. http://dx.doi.org/10.1021/cen-10209-scicon2.

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25

Wang, Zhe, Abbas Nasiraei-Moghaddam, Meral L. Reyhan, Subashini Srinivasan, J. Paul Finn, and Daniel B. Ennis. "Complementary radial tagging for improved myocardial tagging contrast." Magnetic Resonance in Medicine 73, no. 4 (May 13, 2014): 1432–40. http://dx.doi.org/10.1002/mrm.25259.

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26

Dhayalan, D., M. Queen Mary Vidya, and B. Gowri Priya. "Image Tagging With Social Assistance." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-Active Galaxy (June 30, 2018): 43–47. http://dx.doi.org/10.31142/ijtsrd14566.

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27

Munk, Timme Bisgaard, and Kristian Mørk. "Folksonomies, Tagging Communities, and Tagging Strategies–An Empirical Study." KNOWLEDGE ORGANIZATION 34, no. 3 (2007): 115–27. http://dx.doi.org/10.5771/0943-7444-2007-3-115.

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28

Bergado Rosado, Jorge A. "Synaptic Tagging and Emotional tagging, linking Physiology and Psychology." International Journal of Psychophysiology 108 (October 2016): 40. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.133.

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29

Ke, Xiayi, Marcos M. Miretti, John Broxholme, Sarah Hunt, Stephan Beck, David R. Bentley, Panos Deloukas, and Lon R. Cardon. "A comparison of tagging methods and their tagging space." Human Molecular Genetics 14, no. 18 (August 15, 2005): 2757–67. http://dx.doi.org/10.1093/hmg/ddi309.

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30

Yamaguchi, Yuto, Mitsuo Yoshida, Christos Faloutsos, and Hiroyuki Kitagawa. "Patterns in Interactive Tagging Networks." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (August 3, 2021): 513–22. http://dx.doi.org/10.1609/icwsm.v9i1.14616.

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How do users behave if they can tag each other in social networks?In this paper, we answer this question by studying the interactive tagging network constructed by Twitter lists. Twitter lists can be regarded as the tagging process; a user (i.e., tagger) creates a list with a name (i.e., tag) and adds other users (i.e., tagged users) into the list. This tagging network is by nature different from the resource tagging networks (e.g., Flickr and Delicious) because users on this network can tag each other. We address the following research questions: (RQ1) What is the common patterns and the difference between the interactive tagging network and the resource tagging networks? (RQ2) Do users tag each other on the interactive tagging network? And if so, to what extent? (RQ3) What is the difference between the two types of relationships on Twitter: who-tags-whom and who-follows-whom? By quantitatively studying million-scale networks, we found the pervasive patterns across the different tagging networks, and the interactive patterns within the interactive tagging network. This study sheds light on the underlying characteristics of the interactive tagging network, which is relevant to the social scientists and the system designers of the tagging systems.
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31

Spiranec, Sonja, and Mislav Borovac. "Expert vs. Novices Dimensions of Tagging Behaviour in an Educational Setting." Bilgi Dünyası 13, no. 1 (April 30, 2012): 1–16. http://dx.doi.org/10.15612/bd.2012.166.

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The organization and representation of information and knowledge have always been exclusively in the domain of professionals and experts. This has begun to change with the development of folksonomies as alternative, user-generated models of organizing information. The aim of this paper is to research the efficiency in tagging and folksonomy. The flexibility of tagging allows users to classify their collections of items in the ways that they find useful, but the personalized variety of terms can present challenges when searching and browsing. In order to determine the efficiency of tagging research evidence about the nature of tagging and tagging behaviour of specific user groups is needed. This paper contributes to research findings in this domain by presenting findings from a study exploring differences in expert and novices tagging. The research was conducted by giving freshman students, with no prior knowledge of tagging or indexing and therefore determined as novices, an article in the social bookmarking service Delicious. Based only on title, subtitle and abstract of the article every student was supposed to assign tags to that article and do the same after reading the whole article. The same procedure was repeated with postgraduate students from the Department of Information Sciences with sufficiently experience and knowledge in tagging and indexing. In this way differences or similarities between tagging by more advanced users and tagging by average / amateur users could be analyzed and compared. The research has surfaced differences in tag numbers and tag distributions. The findings indicate more precision and consistency in tagging of the expert group, indicating that education in tagging could raise the quality of folksonomies on the long term.
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32

Zebari, Hawar M. H., Hoger M. Hidayet, Assel A. I. Al-nakshabandi, and Nizar Hussein. "Pain Caused by Ear Tagging in Kids of Native Black Goats." Journal of Scientific Research in Medical and Biological Sciences 2, no. 1 (February 28, 2021): 19–29. http://dx.doi.org/10.47631/jsrmbs.v2i1.128.

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Purpose: The normal behavior of goat kids is influenced by painful husbandry procedures such as ear tagging, with kids reducing peripheral temperature and increase restlessness. The present study was designed to elucidate that pain caused by ear tagging affects peripheral temperatures and behavioral observations in Karadi kid goats. Study Design: Experimental Study Design. Subjects and Methods: Nineteen native black goat kids, aged 10-14 days, were used in this study as a sample. Eye and nasal temperatures were recorded before ear tagging process (control) and after ear tagging 5 times in 30 minutes. In addition, the behavior of each kid was observed for 30 minutes before and 30 minutes after ear tagging using focal sampling recorded with instantaneous time sampling to measure the duration and frequency of each behavior of the kids. Results: It was revealed that peripheral temperatures were significantly decreased for both eye (P<0.01) and ear (P<0.01) after ear tagging. The temperatures of both eye and ear increased directly after tagging and then decreased significantly (P<0.01) for 30 minutes. The proportion of time kid goats spent normal standing and suckling the dam’s teat decreased whereas the proportion of time spent head-shaking increased after tagging. In addition, abnormal standing was seen after ear tagging which was absent before the process of tagging. Significant differences were found between abnormal standing (P<0.01) and vocalization (P<0.01). While the differences between suckling, normal standing and head shaking were not significant. Conclusions: It is concluded that ear tagging causes considerable pain in kid goats.
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33

Hedden, Heather. "Tagging versus indexing." Indexer: The International Journal of Indexing 32, no. 2 (June 2014): 81–82. http://dx.doi.org/10.3828/indexer.2014.29.

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34

Dean, Jeffrey F. D. "Tagging all genes." Nature Biotechnology 22, no. 8 (August 2004): 961–62. http://dx.doi.org/10.1038/nbt0804-961.

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35

Monkman, Gareth. "Secure electronic tagging." Assembly Automation 20, no. 1 (March 2000): 24–27. http://dx.doi.org/10.1108/01445150010311626.

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36

Whalley, Katherine. "Emotional memory tagging." Nature Reviews Neuroscience 16, no. 3 (February 20, 2015): 122. http://dx.doi.org/10.1038/nrn3927.

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37

Sebastiani, P., R. Lazarus, S. T. Weiss, L. M. Kunkel, I. S. Kohane, and M. F. Ramoni. "Minimal haplotype tagging." Proceedings of the National Academy of Sciences 100, no. 17 (August 4, 2003): 9900–9905. http://dx.doi.org/10.1073/pnas.1633613100.

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38

Leslie, Mitch. "Tagging an organelle." Journal of Cell Biology 171, no. 2 (October 24, 2005): 195. http://dx.doi.org/10.1083/jcb1712fta2.

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39

Ikonomidou, V. N., and G. D. Sergiadis. "Multirate spamm tagging." IEEE Transactions on Biomedical Engineering 50, no. 9 (September 2003): 1045–51. http://dx.doi.org/10.1109/tbme.2003.814524.

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40

Ede, Nicholas J., and Zemin Wu. "Beyond Rf tagging." Current Opinion in Chemical Biology 7, no. 3 (June 2003): 374–79. http://dx.doi.org/10.1016/s1367-5931(03)00061-9.

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41

Stern, P. R. "Tagging + Recruitment = Learning." Science Signaling 1, no. 8 (February 26, 2008): ec75-ec75. http://dx.doi.org/10.1126/stke.18ec75.

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42

Borisov, G. "Combined b-tagging." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 417, no. 2-3 (November 1998): 384–90. http://dx.doi.org/10.1016/s0168-9002(98)00777-3.

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43

Kleiss, R., and W. J. Stirling. "Tagging the Higgs." Physics Letters B 200, no. 1-2 (January 1988): 193–99. http://dx.doi.org/10.1016/0370-2693(88)91135-5.

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44

Gilman, Neyda V. "Tagging Biomedical Information." Journal of Consumer Health On the Internet 16, no. 3 (July 2012): 324–32. http://dx.doi.org/10.1080/15398285.2012.701533.

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45

Zahn, L. M. "Tagging New Genes." Science 340, no. 6135 (May 23, 2013): 903. http://dx.doi.org/10.1126/science.340.6135.903-b.

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46

Raffel, Markus, Ricardo Hernandez-Rivera, Benjamin Heine, Andreas Schröder, and Karen Mulleners. "Density tagging velocimetry." Experiments in Fluids 51, no. 2 (February 20, 2011): 573–78. http://dx.doi.org/10.1007/s00348-011-1058-6.

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47

Molchanov, D. A., D. A. Kondrashkin, and D. P. Vetrov. "Relevance tagging machine." Machine Learning and Data Analysis 1, no. 13 (2015): 1877–87. http://dx.doi.org/10.21469/22233792.1.13.09.

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48

Feng, Jonathan L., Hitoshi Murayama, and James D. Wells. "ExtractingRbandRcwithout Flavor Tagging." Physical Review Letters 76, no. 18 (April 29, 1996): 3259–62. http://dx.doi.org/10.1103/physrevlett.76.3259.

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49

Holmquist, Lars Erik. "Tagging the world." Interactions 13, no. 4 (July 2006): 51. http://dx.doi.org/10.1145/1142169.1142201.

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50

Kramer, U., A. Hennemuth, and M. Fenchel. "Kardiales MR-Tagging." Der Radiologe 50, no. 6 (April 16, 2010): 532–40. http://dx.doi.org/10.1007/s00117-010-1989-6.

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