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Abstracts of Working Papers in Economics

2000 Abstracts of Working Papers in Economics  
AB This paper presents an analysis of the sustainability of current account deficits in transition economies in Central and Eastern Europe.  ...  AB This paper proposes that analysis of purchasing power parity and the law of one price should explicitly account for the possibility of "commodity points" ~ thresholds delineating a region of no central  ...  Forecasting. AB This paper illustrates the use of a real-time data set for forecasting.  ... 
doi:10.1017/s0951007900004976 fatcat:fpe5wjr64fbyvotznpmyayg64a

Abstracts of Working Papers in Economics

1999 Abstracts of Working Papers in Economics  
AB This paper introduces an instrumental variables estimator for the clTcct of a binary treatment on the quanlilcs of potential outcomes.  ...  However, a simple test reveals that few of these firms behaved in a fashion consistent with binding cash flow constraints. In addition, most were cash rich.  ...  In this paper, I use a variety of estimation strategies and samples to estimate the effect of the program on math and reading scores.  ... 
doi:10.1017/s0951007900004617 fatcat:cdm4vvqx6ne6vihbu3zikddb2m

Abstracts of Working Papers in Economics

1998 Abstracts of Working Papers in Economics  
AB This paper uses Bayesian stochastic frontier methods to measure the productivity gap between Poland and Western countries that existed before the beginning of the main Polish economic reform.  ...  AB This paper assumes that the underlying asset prices are lognormally distributed, and derives necessary and sufficient conditions for the valuation of options using a Black-Scholes type methodology.  ...  We also develop a large sample procedure to measure the forecast sensitivity when we are uncertain whether or not to include the linear trend. Barr, David G.  ... 
doi:10.1017/s0951007900003995 fatcat:siqha7fymfbdfnz2vr74tl4oai

Abstracts of Working Papers in Economics

1997 Abstracts of Working Papers in Economics  
Complexity.AB This paper is an introduction to using neural networks in economic dynamics.  ...  This paper analyzes the role of labor mobility in the transmission of existing productive knowledge and in the creation of new productive knowledge.  ...  The paper provides several lessons for the design of conversion legislation.  ... 
doi:10.1017/s0951007900003570 fatcat:zftl4zq3gzbw7n3ve65vrnzmyq

Fault Location Dependency of Short-Circuit Currents in MMC based Meshed HVDC Cable Systems

Anna Pfendler, Andreas Saciak, Jutta Hanson, Gerd Balzer
2019 2019 IEEE Milan PowerTech  
area.An original tour with art, design and fashion!  ...  While physical predition methods strongly rely on the accuracy of the weather forecast, Artificial Neural Networks are based on the learning process of the underlying models and are commonly referred to  ...  paper to be included in the conference proceedings and uploaded to IEEE Xplore.  ... 
doi:10.1109/ptc.2019.8810935 fatcat:nkvo2dg3fvejjm4srk3k23uwga

Conference Digest

2020 2020 IEEE Aerospace Conference  
Processing of the histogram of I/Q data via deep learning, enhances feature resolution for neural network fusion.  ...  A neural network is then trained to provide accurate SNIIRS scores from single images without being provided knowledge of the object model.  ...  A SOM is a two-layer artificial neural network (ANN) that produces a low-dimensional representation of the training samples.  ... 
doi:10.1109/aero47225.2020.9172613 fatcat:ioqf5ijrx5gvffu3ls34aa2nsq

Satellite Climate Data Records: Development, Applications, and Societal Benefits

Wenze Yang, Viju John, Xuepeng Zhao, Hui Lu, Kenneth Knapp
2016 Remote Sensing  
This review paper discusses how to develop, produce, sustain, and serve satellite climate data records (CDRs) in the context of transitioning research to operation (R2O).  ...  Figure 1 shows the production pathways schematically for various CDR products (ICDR, FCDR, TCDR, and CIR) and their relative dependence, which is produced in either shortly-delayed or delayed fashions  ...  Knapp improved and polished the whole paper.  ... 
doi:10.3390/rs8040331 fatcat:le3hbxaakbf5nhxokomrp223ba

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li (+2 others)
2018 Hydrology and Earth System Sciences  
This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences.  ...  Readers who are less familiar with machine learning or deep learning are referred to a companion review paper (Shen18), which provides a more comprehensive and technical background than this opinion paper  ...  This paper suggests that DLbased methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences.  ... 
doi:10.5194/hess-22-5639-2018 fatcat:hy2y6srekjc7bpeyczna2xlwzi

100 years of Progress in Applied Meteorology Part III: Additional Applications

Sue Ellen Haupt, Branko Kosović, Scott W. McIntosh, Fei Chen, Kathleen Miller, Marshall Shepherd, Marcus Williams, Sheldon Drobot
2018 Meteorological Monographs  
applications reviewed in this series of chapters are not comprehensive, but they will whet the reader's appetite for learning more about how meteorology can make a concrete impact on the world's population by enhancing  ...  Section 2 of this chapter is related to agriculture, food security, and how meteorological and hydrological knowledge is used to enhance production in an effort to help feed the world's population.  ...  Neural networks (NNs) became a popular approach. Krasnopolsky et al. (1995) used a neural network to retrieve wind speeds from a microwave imager.  ... 
doi:10.1175/amsmonographs-d-18-0012.1 fatcat:ra5nseow3zhbbbufgmupb44bua

International Research Conference on Smart Computing and Systems Engineering SCSE 2020 Proceedings [Full Conference Proceedings]

2020 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)  
In this paper, a method is proposed to evaluate and forecast the credibility level of a specific twitter profile that is currently active in the platform.  ...  The paper covers the gap of a lack of knowledge regarding Blockchainbased land authentication systems for the Sri Lankan context.  ...  Section II of this paper explains the Lavenberg-Marquardt algorithm used in the development of the training process of ANN.  ... 
doi:10.1109/scse49731.2020.9313027 fatcat:gjk5az2mprgvrpallwh6uhvlfi

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

Jean-Marc Fellous, Guillermo Sapiro, Andrew Rossi, Helen Mayberg, Michele Ferrante
2019 Frontiers in Neuroscience  
However, even with XAI approaches, one should not assume that understanding the statistical causality of neural interactions is equivalent to understanding behavior; a highly sophisticated knowledge of  ...  Similarly, see an up-to-date public repository of implemented XAI models ( and papers ( target group to which these explanations are  ... 
doi:10.3389/fnins.2019.01346 pmid:31920509 pmcid:PMC6923732 fatcat:ew7ukgezxjacln6cz4izlke5s4

Advancing Energy Testing of Mobile Applications

Reyhaneh Jabbarvand, Sam Malek
2017 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)  
forecasting.  ...  First, they are aimed at profiling an app's energy behavior, rather than the creation of reproducible and reusable tests, such that they can be used in a systematic fashion as part of a regression testing  ... 
doi:10.1109/icse-c.2017.45 dblp:conf/icse/Jabbarvand17 fatcat:pa2fnnbrqjfzjpqaarzcc3nqjy

29th International Conference on Data Engineering [book of abstracts]

2013 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW)  
To the best of our knowledge, this is the first paper that addresses join-based skyline queries over sliding windows.  ...  To the best of our knowledge, this is the first paper to explore the potential of using search engine data assets for e-tailers.  ... 
doi:10.1109/icdew.2013.6547409 fatcat:wadzpuh3b5htli4mgb4jreoika

Man, machine, scientific models and creation science

Steven Gollmer
2018 The Proceedings of the International Conference on Creationism  
In the intervening 80 years all sciences have exploded in the use of quantitative measures to find patterns and trends in data.  ...  As this trend continues to accelerate, two areas of caution need to be taken seriously: 1) the use of properly validated techniques and 2) evaluating the role of assumptions in the development of models  ...  Finally, I would like to thank my wife Evelyn, who is my love, my companion and my proof-reader.  ... 
doi:10.15385/jpicc.2018.8.1.13 fatcat:ow5dtw3kgfa4jdbatlem7abkza

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Marc-Andre Schulz, B. T. Thomas Yeo, Joshua T. Vogelstein, Janaina Mourao-Miranada, Jakob N. Kather, Konrad Kording, Blake Richards, Danilo Bzdok
2020 Nature Communications  
On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models.  ...  Three trends appear to stand out in the existing brain-imaging literature.  ...  More details of Peng's model can be found in their paper. Briefly, this model architecture was motivated by VGGNet, but reduced to~3 million model parameters.  ... 
doi:10.1038/s41467-020-18037-z pmid:32843633 fatcat:lsys7eym5ja6lg3crvm66xp3te
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