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1. 2018 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 40
ISSN 0162-8828, 01/2019, Volume 41, Issue 1
Pattern analysis
Journal Article
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2. Full Text Corrigendum to “How important is it to consider target properties and hematocrit in bloodstain pattern analysis?” [FSI 266C (2016) 178–184]
by Kim, Sungu and Ma, Yuan and Agrawal, Prashant and Attinger, Daniel
Forensic Science International, ISSN 0379-0738, 06/2018, Volume 287, pp. 217 - 217
Pattern analysis | Hematocrit
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3. Bloodstain patterns
: identification, interpretation and application
by Wonder, Anita Y and Yezzo, Michele G
2015, ISBN 9780124159303
Bloodstain pattern analysis | Bloodstains | Forensic hematology
Web Resource
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4. Full Text 2017 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 39
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 01/2018, Volume 40, Issue 1, pp. 1 - 23
Presents the 2017 subject/author index for this publication. 
Pattern analysis | Indexing
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5. Full Text Advanced X-ray phase-contrast and dark-field imaging with the unified modulated pattern analysis (UMPA)
by Zdora, Marie-Christine and Thibault, Pierre and Deyhle, Hans and Vila-Comamala, Joan and Kuo, Willy and Rau, Christoph and Zanette, Irene
Microscopy and Microanalysis, ISSN 1431-9276, 08/2018, Volume 24, Issue S2, pp. 22 - 23
Pattern analysis | Medical imaging
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6. Bloodstain patterns
by MacDonell, Herbert Leon
1997, Rev. ed., xvi, 182
Bloodstains | Forensic hematology
Book
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7. Full Text Pattern of students’ conceptual change on magnetic field based on students’ mental models
by Hamid, Rimba and Widodo, Ari and Sopandi, Wahyu
AIP Conference Proceedings, ISSN 0094-243X, 05/2017, Volume 1848, Issue 1
Students understanding about natural phenomena can be identified by analyzing their mental model. Changes in students’ mental model are good indicator of... 
Students | Pattern analysis
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8. Is Spatial Context Privileged in the Neural Representation of Events?
by Halle R Dimsdale-Zucker and Jonathan Nicholas
The Journal of Neuroscience, ISSN 0270-6474, 07/2018, Volume 38, Issue 28, p. 6241
Dimsdale-Zucker and Nicholas examine whether spatial context privileged in the neural representation of events. They cite a study which examined the importance... 
Spatial discrimination | Representations | Pattern analysis | Activity patterns
Journal Article
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9. Full Text To better stand on the shoulder of giants
by Yan, Rui and Huang, Congrui and Tang, Jie and Zhang, Yan and Li, Xiaoming
Proceedings of the 12th ACM/IEEE-CS joint conference on digital libraries, ISSN 1552-5996, 06/2012, pp. 51 - 60
Usually scientists breed research ideas inspired by previous publications, but they are unlikely to follow all publications in the unbounded literature... 
digital libraries | influence prediction | citation pattern analysis
Conference Proceeding
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10. Closed frequent similar pattern mining: Reducing the number of frequent similar patterns without information loss
by Ansel Y Rodríguez-González and Fernando Lezama and Carlos A Iglesias-Alvarez and José Fco Martínez-Trinidad and Jesús A Carrasco-Ochoa and Enrique Munoz de Cote
Expert Systems with Applications, ISSN 0957-4174, 04/2018, Volume 96, p. 271
Frequent pattern mining is considered a key task to discover useful information. Despite the quality of solutions given by frequent pattern mining algorithms,... 
Algorithms | Data mining | Expert systems | Pattern analysis
Journal Article
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11. Full Text Selecting landscape metrics as indicators of spatial heterogeneity-Acomparison among Greek landscapes
by Plexida, Sofia G and Sfougaris, Athanassios I and Ispikoudis, Ioannis P and Papanastasis, Vasilios P
International Journal of Applied Earth Observation and Geoinformation, ISSN 1569-8432, 2014, Volume 26, Issue 1, pp. 26 - 35
This paper investigates the spatial heterogeneity of three landscapes along an altitudinal gradient anddifferent human land use. The main aim was the... 
Human land use | Landscape pattern analysis | Altitude | Landscape indicators
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12. Hybrid maize seed production yield associations with inbred line performance in multienvironment trials
by Arisnabarreta, Sebastian and Solari, Fernando
Crop Science, ISSN 0011-183X, 11/2017, Volume 57, Issue 6, pp. 3203 - 3216
Hybrid maize (Zea maize L.) seed is produced in Argentina from southern Buenos Aires (39 degrees 52 ' S) to Salta (25 degrees 25 ' S) provinces. Genotype x... 
PATTERN-ANALYSIS | AGRONOMY | ENVIRONMENTS | BIPLOT ANALYSIS | GENOTYPE
Journal Article
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13. Full Text The Default Mode Network’s Role in Discrete Emotion
by Satpute, Ajay B and Lindquist, Kristen A
Trends in Cognitive Sciences, ISSN 1364-6613, 10/2019, Volume 23, Issue 10, pp. 851 - 864
Emotions are often assumed to manifest in subcortical limbic and brainstem structures. While these areas are clearly important for representing affect (e.g.,... 
emotion | memory | multivoxel pattern analysis | conceptualization | default mode network | abstraction
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14. Full Text OP12.11: Prediction of cervical changes and premature birth by cervical image pattern analysis using real‐time tissue elastography
by Nagai, R
Ultrasound in Obstetrics & Gynecology, ISSN 0960-7692, 09/2017, Volume 50, Issue S1, pp. 87 - 87
Infants (Premature) | Analysis | Image processing | Pattern analysis
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15. Full Text Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists
by Haenssle, H A and Fink, C and Schneiderbauer, R and Toberer, F and Buhl, T and Blum, A and Kalloo, A and Hassen, A Ben Hadj and Thomas, L and Enk, A and Uhlmann, L and Reader study level-I and level-II Groups
Annals of Oncology, ISSN 0923-7534, 08/2018, Volume 29, Issue 8, pp. 1836 - 1842
Abstract Background Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to... 
Melanocytic nevi | Dermoscopy | Computer algorithm | Deep learning convolutional neural network | Automated melanoma detection | Melanoma | METAANALYSIS | melanoma | PATTERN-ANALYSIS | CLASSIFICATION | deep learning convolutional neural network | ALGORITHMS | EPILUMINESCENCE MICROSCOPY | SKIN-LESIONS | IMPACT | ONCOLOGY | automated melanoma detection | IMAGES | MELANOCYTIC LESIONS | melanocytic nevi | computer algorithm | dermoscopy
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16. Full Text Shared states: Using MVPA to test neural overlap between self-focused emotion imagery and otherfocused emotion understanding
by Oosterwijk, Suzanne and Snoek, Lukas and Rotteveel, Mark and Barrett, Lisa Feldman and Steven Scholte, H
Social Cognitive and Affective Neuroscience, ISSN 1749-5016, 07/2017, Volume 12, Issue 7, pp. 1025 - 1035
Mentalizing | Multi-voxel pattern analysis | Emotion | Simulation
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17. Full Text Quality-guided phase unwrapping technique: Comparison of quality maps and guiding strategies
by Zhao, Ming and Huang, Lei and Zhang, Qican and Su, Xianyu and Asundi, Anand and Kemao, Qian
Applied Optics, ISSN 1559-128X, 11/2011, Volume 50, Issue 33, pp. 6214 - 6224
Quality-guided phase unwrapping is a widely used technique with different quality definitions and guiding strategies reported. It is thus necessary to do a... 
WINDOWED FOURIER-TRANSFORM | PROFILOMETRY | FRINGE-PATTERN-ANALYSIS | ALGORITHM | INTERFEROMETRY | RELIABILITY | OPTICS | OBJECT SHAPES
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18. Full Text Heterogeneity of neuroanatomical patterns in prodromal Alzheimer’s disease: links to cognition, progression and biomarkers
by Dong, Aoyan and Toledo, Jon B and Honnorat, Nicolas and Doshi, Jimit and Varol, Erdem and Sotiras, Aristeidis and Wolk, David and Trojanowski, John Q and Davatzikos, Christos and for the Alzheimer’s Disease Neuroimaging Initiative
Brain, ISSN 0006-8950, 03/2017, Volume 140, Issue 3, pp. 735 - 747
See Coulthard and Knight (doi: 10.1093/aww335 ) for a scientific commentary on this article. Individuals with clinical diagnoses of mild cognitive impairment... 
Neuroanatomical heterogeneity | Magnetic resonance imaging | Mild cognitive impairment | Pattern analysis | Dementia | pattern analysis | dementia | magnetic resonance imaging | mild cognitive impairment | neuroanatomical heterogeneity | Original
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19. EFP-tree: an efficient FP-tree for incremental mining of frequent patterns
by Razieh Davashi and Mohammad-Hossein Nadimi-Shahraki
International Journal of Data Mining, Modelling and Management, ISSN 1759-1163, 01/2019, Volume 11, Issue 2, pp. 144 - 166
Frequent pattern mining from dynamic databases where there are many incremental updates is a significant research issue in data mining. After incremental... 
Data mining | Pattern analysis | Algorithms
Journal Article
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20. Modifying iEclat Algorithm for Infrequent Patterns Mining
by Jusoh, Julaily Aida and Man, Mustafa
Advanced Science Letters, ISSN 1936-6612, 03/2018, Volume 24, Issue 3, p. 1876
Pattern mining has been extensively studied in research due to its successful application in several data mining scenarios. Association rules mining is a basic... 
Data mining | Pattern analysis | Algorithms
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