Relevant bibliographies by topics / Robust Classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Robust Classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Robust Classification"

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Bertsimas, Dimitris, Jack Dunn, Colin Pawlowski, and Ying Daisy Zhuo. "Robust Classification." INFORMS Journal on Optimization 1, no. 1 (2019): 2–34. http://dx.doi.org/10.1287/ijoo.2018.0001.

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Chen, Li, Cencheng Shen, Joshua T. Vogelstein, and Carey E. Priebe. "Robust Vertex Classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 3 (2016): 578–90. http://dx.doi.org/10.1109/tpami.2015.2456913.

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Glendinning, R. H. "Robust shape classification." Signal Processing 77, no. 2 (1999): 121–38. http://dx.doi.org/10.1016/s0165-1684(99)00028-6.

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Addison, W. D., and R. H. Glendinning. "Robust image classification." Signal Processing 86, no. 7 (2006): 1488–501. http://dx.doi.org/10.1016/j.sigpro.2005.08.010.

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Zhang, Jun, Xiao Chen, Yang Xiang, Wanlei Zhou, and Jie Wu. "Robust Network Traffic Classification." IEEE/ACM Transactions on Networking 23, no. 4 (2015): 1257–70. http://dx.doi.org/10.1109/tnet.2014.2320577.

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Katz, Alan J., Michael T. Gately, and Dean R. Collins. "Robust Classifiers without Robust Features." Neural Computation 2, no. 4 (1990): 472–79. http://dx.doi.org/10.1162/neco.1990.2.4.472.

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We develop a two-stage, modular neural network classifier and apply it to an automatic target recognition problem. The data are features extracted from infrared and TV images. We discuss the problem of robust classification in terms of a family of decision surfaces, the members of which are functions of a set of global variables. The global variables characterize how the feature space changes from one image to the next. We obtain rapid training times and robust classification with this modular neural network approach.
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Lai, Yu-kun, Qian-yi Zhou, Shi-min Hu, Johannes Wallner, and Helmut Pottmann. "Robust Feature Classification and Editing." IEEE Transactions on Visualization and Computer Graphics 13, no. 1 (2007): 34–45. http://dx.doi.org/10.1109/tvcg.2007.19.

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Hubert, Mia, and Stephan Van der Veeken. "Robust classification for skewed data." Advances in Data Analysis and Classification 4, no. 4 (2010): 239–54. http://dx.doi.org/10.1007/s11634-010-0066-3.

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Kundur, D., D. Hatzinakos, and H. Leung. "Robust classification of blurred imagery." IEEE Transactions on Image Processing 9, no. 2 (2000): 243–55. http://dx.doi.org/10.1109/83.821737.

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Zhang, Lingling, Minnan Luo, Zhihui Li, et al. "Large-Scale Robust Semisupervised Classification." IEEE Transactions on Cybernetics 49, no. 3 (2019): 907–17. http://dx.doi.org/10.1109/tcyb.2018.2789420.

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Dissertations / Theses on the topic "Robust Classification"

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Ge, Zongyuan. "Robust fine-grained image classification." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/107700/1/Zongyuan_Ge_Thesis.pdf.

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This thesis tackles fine-grained image recognition, the task of sub-category or species classification. It explores general methods to improve fine-grained image classification including the use of generative models and deep convolutional neural networks leading to novel models such as a Mixture of deep convolution neural networks. This work led to 9 peer reviewed publications and a Best Paper Award.
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Podder, Mohua. "Robust genotype classification using dynamic variable selection." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1602.

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Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide –A, T, C or G – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Dr. Tebbutt's laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). The strength of this platform is its unique redundancy having multiple probes for a single SNP. Using this microarray platform, we have developed fully-automated genotype calling algorithms based
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Chu, Wei 1966. "Auditory-based noise-robust audio classification algorithms." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115863.

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The past decade has seen extensive research on audio classification algorithms which playa key role in multimedia applications, such as the retrieval of audio information from an audio or audiovisual database. However, the effect of background noise on the performance of classification has not been widely investigated. Motivated by the noise-suppression property of the early auditory (EA) model presented by Wang and Shamma, we seek in this thesis to further investigate this property and to develop improved algorithms for audio classification in the presence of background noise.<br>With respect
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Hu, Hong. "Accurate and robust algorithms for microarray data classification." University of Southern Queensland, Faculty of Sciences, 2008. http://eprints.usq.edu.au/archive/00006221/.

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[Abstract]Microarray data classification is used primarily to predict unseen data using a model built on categorized existing Microarray data. One of the major challenges is that Microarray data contains a large number of genes with a small number of samples. This high dimensionality problem has prevented many existing classification methods from directly dealing with this type of data. Moreover, the small number of samples increases the overfitting problem of Classification, as a result leading to lower accuracy classification performance. Another major challenge is that of the uncertainty of
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Tran, Brandon Vanhuy. "Building and using robust representations in image classification." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127912.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 115-131).<br>One of the major appeals of the deep learning paradigm is the ability to learn high-level feature representations of complex data. These learned representations obviate manual data pre-processing, and are versatile enough to generalize across tasks. However, they are not yet capable of fully capturing abstract, meaningful features of the data. For instance, the pervasiveness of adversarial examples--
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Siméoni, Oriane. "Robust image representation for classification, retrieval and object discovery." Thesis, Rennes 1, 2020. https://ged.univ-rennes1.fr/nuxeo/site/esupversions/415eb65b-d5f7-4be7-85e6-c2ecb2aba4dc.

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Les réseaux de neurones convolutifs (CNNs) ont été exploités avec succès pour la résolution de tâches dans le domaine de la vision par ordinateur tels que la classification, la segmentation d'image, la détection d'objets dans une image ou la recherche d'images dans une base de données. Typiquement, un réseau est entraîné spécifiquement pour une tâche et l'entraînement nécessite une très grande quantité d'images annotées. Dans cette thèse, nous proposons des solutions pour extraire le maximum d'information avec un minimum de supervision. D'abord, nous nous concentrons sur la tâche de classifica
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ALI, ARSLAN. "Deep learning techniques for biometric authentication and robust classification." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2910084.

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He, Jin. "Robust Mote-Scale Classification of Noisy Data via Machine Learning." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440413201.

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Carranza, Alarcón Yonatan Carlos. "Distributionally robust, skeptical inferences in supervised classification using imprecise probabilities." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2567.

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Les décideurs sont souvent confrontés au défi de prendre des décisions précises, sans avoir aucune connaissance de la quantité d’incertitudes que celles-ci peuvent contenir, et en prenant le risque de commettre des erreurs dommageables, voire dramatiques. Dans de telles situations, où l’incertitude est plus élevée due à des informations imparfaites, il peut être plutôt utile de fournir des décisions prudentes, sous la forme d’un ensemble de solutions possibles, plus fiables. Ce travail se concentre donc sur la prise de décisions (ou inférences) sceptiques (ou prudentes) et robustes dans des pr
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Szekely, Robin [Verfasser]. "Robust and nonparametric classification of gene expression data / Robin Szekely." Ulm : Universität Ulm, 2021. http://d-nb.info/1237750725/34.

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Books on the topic "Robust Classification"

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J, Bees W., American Society of Mechanical Engineers. Pressure Vessels and Piping Division., and Pressure Vessels and Piping Conference (1993 : Denver, Colo.), eds. Design analysis, robust methods, and stress classification: Presented at the 1993 Pressure Vessels and Piping Conference, Denver, Colorado, July 25-29, 1993. American Society of Mechanical Engineers, 1993.

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C, Becht, Seshadri R, Marriott D. L, American Society of Mechanical Engineers. Pressure Vessels and Piping Division., and Pressure Vessels and Piping Conference (1992 : New Orleans, La.), eds. Stress classification, robust methods, and elevated temperature design: Presented at the 1992 Pressure Vessels and Piping Conference, New Orleans, Louisiana, June 21-25, 1992. American Society of Mechanical Engineers, 1992.

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NATO Advanced Research Workshop on Real-time Object and Environment Measurement and Classification (1987 Maratea, Italy). Real-time object measurement and classification. Springer-Verlag, 1988.

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Cayer, Micheline. Vocabulaire de la robotique: Classification et système mécanique. Gouvernement du Québec, Office de la langue française, 1993.

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Jain, Anil K. Real-Time Object Measurement and Classification. Springer Berlin Heidelberg, 1988.

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Healey, Anthony J. Sonar signal acquisition and processing for identification and classification of ship hull fouling. Naval Postgraduate School, 1993.

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Maxson, Linda R. Molecular systematics of the frog genus Leptodactylus (Amphibia : Leptodactylidae): A contribution in celebration of the distinguished scholarship of Robert F. Inger on the occasion of his sixty-fifth birthday. Field Museum of Natural History, 1988.

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Marx, Hymen. Phylogeny of the viperine snakes (Viperinae): A contribution in celebration of the distinguished scholarship of Robert F. Inger on the occasion of his sixty-fifth birthday. Field Museum of Natural History, 1988.

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Marx, Hymen. Phylogeny of the Viperine snakes (Viperinae): Part I. Character analysis : a contribution in celebration of the distinguished scholarship of Robert F. Inger on the occasion of his sixty-fifth birthday. Field Museum of Natural History, 1988.

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Sidney, Sheldon. Konet︠s︡ sveta. Novosti, 1995.

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Book chapters on the topic "Robust Classification"

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Abe, Shigeo. "Robust Function Approximation." In Pattern Classification. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_16.

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Abe, Shigeo. "Robust Pattern Classification." In Pattern Classification. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_8.

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Bini, Matilde, and Bruno Bertaccini. "Robust Fuzzy Classification." In Data Analysis and Classification. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_45.

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Sebastiani, Paola, and Marco Ramoni. "Robust Bayesian classification." In COMPSTAT. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57678-2_61.

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Atkinson, Anthony C., Marco Riani, and Andrea Cerioli. "Robust Clustering for Performance Evaluation." In Data Analysis and Classification. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_43.

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Alshemali, Basemah, Alta Graham, and Jugal Kalita. "Toward Robust Image Classification." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29513-4_35.

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Alzami, Farrikh, Daxing Wang, Zhiwen Yu, Jane You, Hau-San Wong, and Guoqiang Han. "Robust Epileptic Seizure Classification." In Intelligent Computing Theories and Application. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42294-7_32.

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Higger, Matt, Murat Akcakaya, Umut Orhan, and Deniz Erdogmus. "Robust Classification in RSVP Keyboard." In Foundations of Augmented Cognition. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39454-6_47.

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Liu, Fanghui, Xiaolin Huang, Cheng Peng, Jie Yang, and Nikola Kasabov. "Robust Kernel Approximation for Classification." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_31.

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Kim, Hyun-Chul, and Zoubin Ghahramani. "Outlier Robust Gaussian Process Classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89689-0_93.

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Conference papers on the topic "Robust Classification"

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Aoley, Rushikesh. "Robust Color Classification for Autonomous Robotic Boats." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724966.

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Sourati, Zhivar, Darshan Girish Deshpande, Filip Ilievski, Kiril Gashteovski, and Sascha Saralajew. "Robust Text Classification: Analyzing Prototype-Based Networks." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.745.

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Ge, Wenshu, Huan Ma, Peizhuo Sheng, and Changqing Zhang. "Uncertainty-aware Dynamic Re-weighting Robust Classification." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651411.

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Fu, Haoqi, Keqin Shi, and Weiqiang Sun. "Robust Classification of Step Data of Exercise." In 2019 IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM). IEEE, 2019. http://dx.doi.org/10.1109/datacom.2019.00022.

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Bhattarai, Sumit, Pramil Paudel, Zhu Li, Bo Luo, and Fengjun Li. "Robust Privacy-Preserving Classification for Lensless Images." In 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS). IEEE, 2024. http://dx.doi.org/10.1109/mass62177.2024.00056.

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Tucker, Andrew, and Steven Kay. "Robust spectral classification." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, edited by Ivan Kadar. SPIE, 2018. http://dx.doi.org/10.1117/12.2304616.

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PARSONS, NH. "A ROBUST ALGORITHM FOR REVERBERATION SUPPRESSION IN DOPPLER SENSITIVE TRANSMISSIONS." In DETECTION & CLASSIFICATION OF UNDERWATER TARGETS 2007. Institute of Acoustics, 2023. http://dx.doi.org/10.25144/17802.

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Fidler, Sanja, and Ales Leonardis. "Robust LDA Classification by Subsampling." In 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE, 2003. http://dx.doi.org/10.1109/cvprw.2003.10089.

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Rattani, Ajita, D. R. Kisku, Manuele Bicego, and Massimo Tistarelli. "Robust Feature-Level Multibiometric Classification." In 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference. IEEE, 2006. http://dx.doi.org/10.1109/bcc.2006.4341631.

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Shi, Shengli, Zhong Qin, and Jianmin Xu. "Robust Algorithm of Vehicle Classification." In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007). IEEE, 2007. http://dx.doi.org/10.1109/snpd.2007.79.

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Reports on the topic "Robust Classification"

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Chowdhury, Shwetadwip. Automated extraction of cellular features for a potentially robust classification scheme. National Institute of Standards and Technology, 2010. http://dx.doi.org/10.6028/nist.ir.7738.

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Becker, Sarah, Craig Daughtry, and Andrew Russ. Robust forest cover indices for multispectral images. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42760.

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Trees occur in many land cover classes and provide significant ecosystem services. Remotely sensed multispectral images are often used to create thematic maps of land cover, but accurately identifying trees in mixed land-use scenes is challenging. We developed two forest cover indices and protocols that reliably identified trees in WorldView-2 multispectral images. The study site in Maryland included coniferous and deciduous trees associated with agricultural fields and pastures, residential and commercial buildings, roads, parking lots, wetlands, and forests. The forest cover indices exploite
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Schnitta-Israel, B. Robust Detection and Classification of Regional Seismic Signals Using a Two Mode/Two Stage Cascaded Adaptive Arma (CAARMA) Model. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada154710.

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Boopalan, Santhana. Aerial Wildlife Image Repository. Mississippi State University, 2023. http://dx.doi.org/10.54718/wvgf3020.

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The availability of an ever-improving repository of datasets allows machine learning algorithms to have a robust training set of images, which in turn allows for accurate detection and classification of wildlife. This repository (AWIR---Aerial Wildlife Image Repository) would be a step in creating a collaborative rich dataset both in terms of taxa of animals and in terms of the sensors used to observe (visible, infrared, Lidar etc.). Initially, priority would be given to wildlife species hazardous to aircrafts, and common wildlife damage-associated species. AWIR dataset is accompanied by a cla
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Maldonado, Leonardo, and Víctor Olivo. Is Venezuela Still an Upper-Middle-Income Country? Estimating the GNI per Capita for 2015–2021. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004612.

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In the 2022 World Bank (WB) country classifications by income level, Venezuela is classified as an upper-middle-income country. Due to the lack of reliable official economic information from the Venezuelan regime, the WB ranked the country using its gross national income (GNI) of 2013. However, after 2013 Venezuela started to experience one of the largest economic collapses observed in Latin American history. We use three different approaches (the Atlas method, extrapolation, and an adjusted deflator) to obtain consistent and robust estimates of the GNI per capita for Venezuela up to 2021. Our
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Fee, Kyle D. Income Inequality and Economic Growth in United States Counties: 1990s, 2000s and 2010s. Federal Reserve Bank of Cleveland, 2025. https://doi.org/10.26509/frbc-wp-202505.

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Using a common reduced-form regional growth model framework, an expanded geographic classification of counties, additional years of data, a trio of income inequality metrics, and multiple empirical specifications, this analysis confirms and builds upon the notion that the nature of the relationship between income inequality and economic growth varies across geography (Fallah and Partridge, 2007). A positive relationship between an income Gini coefficient and per capita income growth is observed only in central metro counties with population densities greater than 915 people per square mile or
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l
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Wilson, D., Steven Peckham, Max Krackow, Sora Haley, Sophia Bragdon, and Jay Clausen. Discriminating buried munitions based on physical models for their thermal response. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49749.

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Munitions and other objects buried near the Earth’s surface can often be recognized in infrared imagery because their thermal and radiative properties differ from the surrounding undisturbed soil. However, the evolution of the thermal signature over time is subject to many complex interacting processes, including incident solar radiation, heat conduction in the ground, longwave radiation from the surface, and sensible and latent heat exchanges with the atmosphere. This complexity makes development of robust classification algorithms particularly challenging. Machine-learning algorithms, althou
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Бережна, Маргарита Василівна. Maleficent: from the Matriarch to the Scorned Woman (Psycholinguistic Image). Baltija Publishing, 2021. http://dx.doi.org/10.31812/123456789/5766.

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The aim of the research is to identify the elements of the psycholinguistic image of the leading character in the dark fantasy adventure film Maleficent directed by Robert Stromberg (2014). The task consists of two stages, at the first of which I identify the psychological characteristics of the character to determine to which of the archetypes Maleficent belongs. As the basis, I take the classification of film archetypes by V. Schmidt. At the second stage, I distinguish the speech peculiarities of the character that reflex her psychological image. This paper explores 98 Maleficent’s turns of
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Arnold, Joshua. DTPH56-16-T-00004 EMAT Guided Wave Technology for Inline Inspections of Unpiggable Natural Gas Pipelines. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0012048.

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This project developed compact, lightweight, prototype Electro-Magnetic Acoustic Transducers (EMATs) and studied guided waves for defect detection, classification, and characterization in cast iron and steel pipes. Through lab testing, design, and Finite Element Analysis (FEA), guided wave propagation and defect interactions were evaluated, and the results were employed to optimize the prototype EMATs through successive design and testing iterations. The goal of developing EMATs for robotic inspection of unpiggable pipe was successfully achieved and demonstrated not only through prototype fabr
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