Relevant bibliographies by topics / Differential learning

Contents

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

Academic literature on the topic 'Differential learning'

Author: Grafiati
Published: 9 September 2021
Last updated: 18 February 2022

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Journal articles on the topic "Differential learning"

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Hopp, Holger. "Differential learning of prediction." Epistemological issue with keynote article “The role of language processing in language acquisition” by Colin Phillips and Lara Ehrenhofer 5, no. 4 (2015): 476–81. http://dx.doi.org/10.1075/lab.5.4.06hop.

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Cai, Yiqiao, Jingliang Liao, Tian Wang, Yonghong Chen, and Hui Tian. "Social learning differential evolution." Information Sciences 433-434 (April 2018): 464–509. http://dx.doi.org/10.1016/j.ins.2016.10.003.

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Gierut, Judith A. "Differential Learning of Phonological Oppositions." Journal of Speech, Language, and Hearing Research 33, no. 3 (1990): 540–49. http://dx.doi.org/10.1044/jshr.3303.540.

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This study evaluated whether variations in the structure of minimal versus maximal opposition treatments would result in empirical differences in phonological learning. Subjects were 3 children who excluded at least six sounds from their pretreatment phonetic and phonemic inventories. An alternating treatments design in combination with a staggered multiple baseline across subjects was used to evaluate differences in learning the two types of oppositions. Results indicated that treatment of maximal oppositions led to greater improvement in the children’s production of treated sounds, more addi
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Hammer, R., A. Brechmann, F. Ohl, G. Diesendruck, D. Weinshall, and S. Hochstein. "Differential learning processes for categorization." Journal of Vision 8, no. 6 (2010): 839. http://dx.doi.org/10.1167/8.6.839.

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Wang, Jun, and Zhi-Hua Zhou. "Differentially Private Learning with Small Public Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6219–26. http://dx.doi.org/10.1609/aaai.v34i04.6088.

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Differentially private learning tackles tasks where the data are private and the learning process is subject to differential privacy requirements. In real applications, however, some public data are generally available in addition to private data, and it is interesting to consider how to exploit them. In this paper, we study a common situation where a small amount of public data can be used when solving the Empirical Risk Minimization problem over a private database. Specifically, we propose Private-Public Stochastic Gradient Descent, which utilizes such public information to adjust parameters
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Afsari, Bahman, Donald German, and Elana J. Fertig. "Learning Dysregulated Pathways in Cancers from Differential Variability Analysis." Cancer Informatics 13s5 (January 2014): CIN.S14066. http://dx.doi.org/10.4137/cin.s14066.

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Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with
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I. Schollhorn, W. "The Nonlinear Nature of Learning - A Differential Learning Approach." Open Sports Sciences Journal 5, no. 1 (2012): 100–112. http://dx.doi.org/10.2174/1875399x01205010100.

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Huai, Mengdi, Di Wang, Chenglin Miao, Jinhui Xu, and Aidong Zhang. "Pairwise Learning with Differential Privacy Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 694–701. http://dx.doi.org/10.1609/aaai.v34i01.5411.

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Pairwise learning has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. Many machine learning tasks can be categorized as pairwise learning, such as AUC maximization and metric learning. Existing techniques for pairwise learning all fail to take into consideration a critical issue in their design, i.e., the protection of sensitive information in the training set. Models learned by such algorithms can implicitly memorize the details of sensitive information, which offers opportunity for malicious parties to infer it from the l
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Malueg, David A. "Repeated Insurance Contracts with Differential Learning." Review of Economic Studies 55, no. 1 (1988): 177. http://dx.doi.org/10.2307/2297537.

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Xiang, Tao, Yang Li, Xiaoguo Li, Shigang Zhong, and Shui Yu. "Collaborative ensemble learning under differential privacy." Web Intelligence 16, no. 1 (2018): 73–87. http://dx.doi.org/10.3233/web-180374.

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Dissertations / Theses on the topic "Differential learning"

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Lundmark, Magnus, and Carl-Johan Dahlman. "Differential privacy and machine learning: Calculating sensitivity with generated data sets." Thesis, KTH, Data- och elektroteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209481.

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Privacy has never been more important to maintain in today’s information society. Companies and organizations collect large amounts of data about their users. This information is considered to be valuable due to its statistical usage that provide insight into certain areas such as medicine, economics, or behavioural patterns among individuals. A technique called differential privacy has been developed to ensure that the privacy of individuals are maintained. This enables the ability to create useful statistics while the privacy of the individual is maintained. However the disadvantage of diffe
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Aslaksen, Jonasson Erik Alexander. "Differential Deep Learning for Pricing Exotic Financial Derivatives." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301311.

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Calculating the value of a financial derivative is a central problem in quantitative finance. For many exotic derivatives there are no closed-form solutions for present values, instead, computationally expensive Monte Carlo methods are used for valuation. In addition to the present value, the sensitivities (derivatives with respect to the input parameters) are of interest. This study trains deep neural networks to approximate the present value of an autocallable, a highly path-dependent and customizable financial derivative in the form of a contract between two parties. This study pretrains de
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Jones, Nicholas. "Neuronal correlates of non-differential appetitive conditioning." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367782.

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Mack, Jonas. "Physics Informed Machine Learning of Nonlinear Partial Differential Equations." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441275.

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Andblom, Mikael. "Improved Differential Diagnostics Using Methods in Machine Learning and Regression." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240083.

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There is a desire both from the patient and the society to have efficient tools for differential diagnostics. Mathematical relationships between diseases and observable consequences are defined in the thesis. Specifically artificial neural networks are considered in the modeling of the doctor’s methodology. To suggest further lab tests or symptoms to look for the network is inverted by looking at a minimization problem where the objective function gradient can be analytically calculated. Due to difficulties in obtaining real life medical data a program was constructed to generate artificial patient dat
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Kendal, Margaret. "Teaching and learning introductory differential calculus with a computer algebra system /." Connect to thesis, 2001. http://eprints.unimelb.edu.au/archive/00000299.

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Jaeger, Michael J. "The Effects Of Differential Reinforcement Structures On Computer-Assisted Instructional Learning." Scholarly Commons, 1985. https://scholarlycommons.pacific.edu/uop_etds/3035.

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The use of computer assisted instruction as a viable educational technology in our nation's schools has presented educators with new dilemmas and decisions. To operate these devices in the manner in which they were intended, computer software or courseware, whose cost often rivals the original cost of the computers themselves, must be purchased before their potential benefit can be realized. The most widely used type of instructional design that is inherent in these programs can be categorized under the operant theory of learning comprising the styles such as tutorial, skills building, and dri
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McIntosh, David Eugene. "Cluster analysis of the differential ability scales : profiles for learning disabled subtypes." Virtual Press, 1990. http://liblink.bsu.edu/uhtbin/catkey/720161.

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The purpose of this study was to determine if distinct subgroups of learning disabled children could be identified using a single, recently developed, instrument--the Differential Ability Scales (DAS). Ward's method of cluster analysis was used to group 83 school-verified learning disabled children from the standardization sample.These children were classified using the core, diagnostic, and achievement subtests of the DAS. Achievement subtest scores were transformed into T-scores with a mean of 50 and a standard deviation of 10 to provide scores on the same scale as the core and diagnostic su
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Weeks, Clinton. "Investigation of the differential forgetting rates of item and associative information /." [St. Lucia, Qld.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16837.pdf.

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Shi, Xun Xun. "Intelligent Differential Ion Mobility Spectrometry (iDMS): A Machine Learning Algorithm that Simplifies Optimization of Lipidomic Differential Ion Mobility Spectrometry Parameters." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42794.

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Glycosphingolipids such as α- and β-glucosylceramides (GlcCers) and α- and β- galactosylceramides (GalCers) are stereoisomers differentially synthesized by gut bacteria and their mammalian hosts in response to environmental insult. Thus, lipidomic assessment of α- and β-GlcCers and α- and β-GalCers is crucial for inferring biological functions and biomarker discovery. However, simultaneous quantification of these stereoisomeric lipids is difficult due to their virtually identical structures. Differential mobility mass spectrometry (DMS), as an orthogonal separation to high performance liquid c
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Books on the topic "Differential learning"

1

Brian, Lowe, ed. Learning differential equations through DERIVE. Studentlitteratur, 1998.

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Learning radiology: Recognizing the basics. 2nd ed. Elsevier Mosby, 2012.

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Robertson, Jean. Specific learning difficulties (for example dyslexia): Differential diagnosis and intervention. University of Manchester, 1996.

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A, Nguyen Dung, ed. Learning from medical errors: Clinical problems. Radcliffe Pub., 2005.

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A, Nguyen Dung, ed. Learning from medical errors: Legal issues. Radcliffe, 2005.

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Breaux, Elizabeth. How the best teachers differentiate instruction. Eye on Education, 2010.

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Breaux, Elizabeth. How the best teachers differentiate instruction. Eye on Education, 2010.

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Boutte, Magee, ed. How the best teachers differentiate instruction. Eye on Education, 2010.

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Spencer, Sally (Sally A.), 1955-, ed. Collaborate, communicate, & differentiate!: How to increase student learning in today's diverse schools. Corwin Press, 2011.

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1954-, Kaufeldt Martha, ed. Think Big, Start Small: How to Differentiate Instruction in a Brain-Friendly Classroom. Solution Tree Press, 2012.

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Book chapters on the topic "Differential learning"

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Zhang, Jingqiao, and Arthur C. Sanderson. "Parameter Adaptive Differential Evolution." In Evolutionary Learning and Optimization. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01527-4_4.

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Antwi Bosiakoh, Thomas. "Differential Association Theory." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1869.

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Nassif, Houssam, Vítor Santos Costa, Elizabeth S. Burnside, and David Page. "Relational Differential Prediction." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33460-3_45.

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Zhang, Jingqiao, and Arthur C. Sanderson. "Theoretical Analysis of Differential Evolution." In Evolutionary Learning and Optimization. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01527-4_3.

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Zhang, Jingqiao, and Arthur C. Sanderson. "Surrogate Model-Based Differential Evolution." In Evolutionary Learning and Optimization. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01527-4_5.

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Zhang, Jingqiao, and Arthur C. Sanderson. "Adaptive Multi-objective Differential Evolution." In Evolutionary Learning and Optimization. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01527-4_6.

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Brest, Janez, Aleš Zamuda, and Borko Bošković. "Adaptation in the Differential Evolution." In Adaptation, Learning, and Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14400-9_2.

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Kwon, Oh Nam. "Differential Equations Teaching and Learning." In Encyclopedia of Mathematics Education. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77487-9_100023-1.

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Kwon, Oh Nam. "Differential Equations Teaching and Learning." In Encyclopedia of Mathematics Education. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-15789-0_100023.

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Gong, Yue-Jiao, Qi Zhou, Ying Lin, and Jun Zhang. "Orthogonal Predictive Differential Evolution." In Proceedings in Adaptation, Learning and Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13359-1_12.

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Conference papers on the topic "Differential learning"

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Weaverdyck, Noah, Dhayaa Anbajagane, and August E. Evrard. "Differential Assessment, Differential Benefit." In L@S '20: Seventh (2020) ACM Conference on Learning @ Scale. ACM, 2020. http://dx.doi.org/10.1145/3386527.3406731.

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Kosko, Bart. "Differential Hebbian learning." In AIP Conference Proceedings Volume 151. AIP, 1986. http://dx.doi.org/10.1063/1.36225.

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Cheu, Eng Yeow. "Sequential learning of differential trend." In 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2012. http://dx.doi.org/10.1109/eais.2012.6232830.

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Abadi, Martin, Andy Chu, Ian Goodfellow, et al. "Deep Learning with Differential Privacy." In CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016. http://dx.doi.org/10.1145/2976749.2978318.

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Sun, Lichao, Jianwei Qian, and Xun Chen. "LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/217.

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Training deep learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of raw data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous works do not give a practical solution due to two issues. First, the range difference of weights in different deep learning model layers has not been explicitly considered when applying local differential privacy mechanism. Second, the
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Anumasa, Srinivas, and Srijith P.K. "Delay Differential Neural Networks." In ICMLT 2021: 2021 6th International Conference on Machine Learning Technologies. ACM, 2021. http://dx.doi.org/10.1145/3468891.3468908.

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Debie, Essam, Kamran Shafi, Chris Lokan, and Kathryn Merrick. "Investigating Differential Evolution based rule discovery in learning classifier systems." In 2013 IEEE Symposium on Differential Evolution (SDE). IEEE, 2013. http://dx.doi.org/10.1109/sde.2013.6601445.

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Ren, Xuan, Zhi-zhao Chen, and Zhen Ma. "Differential Evolution Using Smaller Population." In 2010 Second International Conference on Machine Learning and Computing. IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.9.

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Triastcyn, Aleksei, and Boi Faltings. "Federated Learning with Bayesian Differential Privacy." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005465.

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Wang, Xin, Hideaki Ishii, Linkang Du, Peng Cheng, and Jiming Chen. "Differential Privacy-preserving Distributed Machine Learning." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029938.

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Reports on the topic "Differential learning"

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Gress, Gabriel. Understanding machine learning approaches for partial differential equations. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1669073.

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McGee, Steven, Amanda Durik, and Jess Zimmerman. The Impact of Text Genre on Science Learning in an Authentic Science Learning Environment. The Learning Partnership, 2015. http://dx.doi.org/10.51420/conf.2015.2.

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A gap exists between research on learning and research on interest. Cognitive researchers rarely consider motivational processes, and interest researchers rarely consider cognitive process. However, it is essential to consider both since achievement and interest are in fact intertwined. In this paper we (1) discuss a theoretical model that intertwines cognitive and interest development, (2) describe how that model informed the development of educational materials, and (3) report on the results of the cognitive components of a randomized research study examining the impact of text genre on lear
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Sowa, Patience, Rachel Jordan, Wendi Ralaingita, and Benjamin Piper. Higher Grounds: Practical Guidelines for Forging Learning Pathways in Upper Primary Education. RTI Press, 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0069.2105.

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To address chronically low primary school completion rates and the disconnect between learners’ skills at the end of primary school and the skills learners need to thrive in secondary school identified in many low- and middle-income countries, more investment is needed to improve the quality of teaching and learning in upper primary grades. Accordingly, we provide guidelines for improving five components of upper primary education: (1) In-service teacher professional development and pre-service preparation to improve and enhance teacher quality; (2) a focus on mathematics, literacy, and core c
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Pritchett, Lant, and Martina Viarengo. Learning Outcomes in Developing Countries: Four Hard Lessons from PISA-D. Research on Improving Systems of Education (RISE), 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/069.

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The learning crisis in developing countries is increasingly acknowledged (World Bank, 2018). The UN’s Sustainable Development Goals (SDG) include goals and targets for universal learning and the World Bank has adopted a goal of eliminating learning poverty. We use student level PISA-D results for seven countries (Cambodia, Ecuador, Guatemala, Honduras, Paraguay, Senegal, and Zambia) to examine inequality in learning outcomes at the global, country, and student level for public school students. We examine learning inequality using five dimensions of potential social disadvantage measured in PIS
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Lavadenz, Magaly, and Gisela O’Brien. District Administrators' Perspectives on the Impact of The Local Control Funding Formula on English Learners. Loyola Marymount University, 2017. http://dx.doi.org/10.15365/ceel.policy.6.

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Two years into implementation, this policy brief examines how California’s Local Control Funding Formula (LCFF) and its accompanying Local Control Accountability Plan (LCAP) meet the needs of English Learners (ELs). Researchers seek to understand district administrator perspectives on the impact of LCFF for ELs through interviews and focus groups with administrators that represent districts from Northern, Central, and Southern California. Findings reveal that although the LCAP serves as a mechanism to increase personnel and PD efforts to address EL needs, it is still largely viewed as a compli
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Downard, Alicia, Stephen Semmens, and Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40439.

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The orientation of constructed levee embankments relative to alluvial swales is a useful measure for identifying regions susceptible to backward erosion piping (BEP). This research was conducted to create an automated, efficient process to classify patterns and orientations of swales within the Lower Mississippi Valley (LMV) to support levee risk assessments. Two machine learning algorithms are used to train the classification models: a convolutional neural network and a U-net. The resulting workflow can identify linear topographic features but is unable to reliably differentiate swales from o
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