Готові списки джерел за темами / Extrapolation

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Добірка наукової літератури з теми "Extrapolation"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 11 березня 2023

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Статті в журналах з теми "Extrapolation"

1

Zhu, X., and T. Wiegelmann. "Toward a fast and consistent approach to modeling solar magnetic fields in multiple layers." Astronomy & Astrophysics 658 (January 28, 2022): A37. http://dx.doi.org/10.1051/0004-6361/202141505.

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Aims. We aim to develop a fast and consistent extrapolation method for modeling multiple layers of the solar atmosphere. Methods. The new approach combines the magnetohydrostatic (MHS) extrapolation, which models the solar low atmosphere in a flat box, together with the nonlinear force-free field (NLFFF) extrapolation, which models the solar corona with a chromospheric vector magnetogram deduced from the MHS extrapolation. We tested our code with a snapshot of a radiative magnetohydrodynamic simulation of a solar flare and we conducted quantitative comparisons based on several metrics. Results. Following a number of test runs, we found an optimized configuration for the combination of two extrapolations with a 5.8-Mm-high box for the MHS extrapolation and a magnetogram at a height of 1 Mm for the NLFFF extrapolation. The new approach under this configuration has the capability to reconstruct the magnetic fields in multi-layers accurately and efficiently. Based on figures of merit that are used to assess the performance of different extrapolations (NLFFF extrapolation, MHS extrapolation, and the combined one), we find the combined extrapolation reaches the same level of accuracy as the MHS extrapolation and they are both better than the NLFFF extrapolation. The combined extrapolation is moderately efficient for application to magnetograms with high resolution.
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2

Norozi, Muhammad Ali. "Faster Ranking Using Extrapolation Techniques." International Journal of Computer Vision and Image Processing 1, no. 3 (July 2011): 35–52. http://dx.doi.org/10.4018/ijcvip.2011070103.

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Extrapolations are techniques in linear algebra that require little additional infrastructure that must be incorporated in the existing query-dependent Link Analysis Ranking (LAR) algorithms. Extrapolations in LAR settings relies on the prior knowledge of the (iterative) process that created the existing data points (iterates) to compute the new (improved) data point, which periodically leads to the desired solution faster than the original method. In this study, the author presents novel approaches using extrapolation techniques to speed-up the convergence of query-dependent iterative methods, link analysis based ranking methods, where hyperlink structures are used to determine relative importance of a document in the network of inter-connections. The author uses the framework defined in HITS and SALSA and proposes the use of different Extrapolation techniques for faster ranking. The paper improves algorithms like HITS and SALSA using Extrapolation techniques. With the proposed approaches it is possible to accelerate the iterative ranking algorithms in terms of reducing the number of iterations and increasing the rate of convergence.
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3

Cho, Doah, Saskia Cheyne, Sarah J. Lord, John Simes, and Chee Khoon Lee. "Extrapolating evidence for molecularly targeted therapies from common to rare cancers: a scoping review of methodological guidance." BMJ Open 12, no. 7 (July 2022): e058350. http://dx.doi.org/10.1136/bmjopen-2021-058350.

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ObjectivesCancer is increasingly classified according to biomarkers that drive tumour growth and therapies developed to target them. In rare biomarker-defined cancers, randomised controlled trials to adequately assess targeted therapies may be infeasible. Extrapolating existing evidence of targeted therapy from common cancers to rare cancers sharing the same biomarker may reduce evidence requirements for regulatory approval in rare cancers. It is unclear whether guidelines exist for extrapolation. We sought to identify methodological guidance for extrapolating evidence from targeted therapies used for common cancers to rare biomarker-defined cancers.DesignScoping review.Data sourcesWebsites of health technology assessment agencies, regulatory bodies, research groups, scientific societies and industry. EBM Reviews—Cochrane Methodology Register and Health Technology Assessment, Embase and MEDLINE databases (1946 to 11 May 2022).Eligibility criteriaPapers proposing a framework or recommendations for extrapolating evidence for rare cancers, small populations and biomarker-defined cancers.Data extraction and synthesisWe extracted framework details where available and guidance for components of extrapolation. We used these components to structure and summarise recommendations.ResultsWe identified 23 papers. One paper provided an extrapolation framework but was not cancer specific. Extrapolation recommendations addressed six distinct components: strategies for grouping cancers as the same biomarker-defined disease; analytical validation requirements of a biomarker test to use across cancer types; strategies to generate control data when a randomised concurrent control arm is infeasible; sources to inform biomarker clinical utility assessment in the absence of prospective clinical evidence; requirements for surrogate endpoints chosen for the rare cancer; and assessing and augmenting safety data in the rare cancer.ConclusionsIn the absence of an established framework, our recommendations for components of extrapolation can be used to guide discussions about interpreting evidence to support extrapolation. The review can inform the development of an extrapolation framework for biomarker-targeted therapies in rare cancers.
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4

Zhang, Jie, and George A. McMechan. "Turning wave migration by horizontal extrapolation." GEOPHYSICS 62, no. 1 (January 1997): 291–97. http://dx.doi.org/10.1190/1.1444130.

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Conventional migration based on depth stepping extrapolation fails to migrate turning wave energy because of its inability to propagate energy with dips beyond 90°. A viable strategy for imaging turning waves is to use horizontal, rather than depth, extrapolation. This can be implemented by a 90° rotation of the extrapolator so that the data are extrapolated horizontally rather than vertically. In this geometry, the energy associated with turned rays consistently moves in the same direction as the extrapolation, and so only one pass is necessary to image turned reflections. The viability of this strategy is demonstrated with both synthetic and field poststack data that include turned reflections from salt flanks. Depth extrapolation images the near‐horizontal structure and horizontal extrapolation images the near‐vertical structure, and combining them gives a full image containing all dips.
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5

Clewell, Harvey J., and Melvin E. Andersen. "Risk Assessment Extrapolations and Physiological Modeling." Toxicology and Industrial Health 1, no. 4 (October 1985): 111–34. http://dx.doi.org/10.1177/074823378500100408.

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The process of assessing the risk associated with human exposure to environmental chemicals inevitably relies on a number of assumptions, estimates and rationalizations. One of the more challenging aspects of risk assessment involves the need to extrapolate beyond the range of conditions used in experimental animal studies to predict anticipated human risks. The most obvious extrapolation required is that from the tested animal species to humans; but others are also generally required, including extrapolating from high dose to low dose, from one route of exposure to another and from one exposure timeframe to another. Several avenues are available for attempting these extrapolations, ranging from the assumption of strict correspondence of dose to the use of statistical correlations. One promising alternative for conducting more scientifically sound extrapolations is that of using physiologically based pharmacokinetic models that contain sufficient biological detail to allow pharmacokinetic behavior to be predicted for widely different exposure scenarios. In recent years, successful physiological models have been developed for a variety of volatile and nonvolatile chemicals, and their ability to perform the extrapolations needed in risk assessment has been demonstrated. Techniques for determining the necessary biochemical parameters are readily available, and the computational requirements are now within the scope of even a personal computer. In addition to providing a sound framework for extrapolation, the predictive power of a physiologically based pharmacokinetic model makes it a useful tool for more reliable dose selection before beginning large-scale studies, as well as for the retrospective analysis of experimental results.
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6

Zhou, Hongbo, and George A. McMechan. "Rigorous absorbing boundary conditions for 3-D one‐way wave extrapolation." GEOPHYSICS 65, no. 2 (March 2000): 638–45. http://dx.doi.org/10.1190/1.1444760.

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Absorbing boundary conditions play an important role in one‐way wave extrapolations by reducing reflections at grid edges. Clayton and Engquist’s 2-D absorbing boundary conditions for one‐way wave extrapolation by depth stepping in the frequency domain are extended to three dimensions using paraxial approximations of the scalar wave equation. Internal consistency is retained by incorporating the interior extrapolation equation with the absorbing boundary conditions. Numerical schemes are designed to make the proposed absorbing boundary conditions both mathematically correct and efficient with negligible extra cost. Synthetic examples illustrate the effectiveness of the algorithm for extrapolation with the 3-D 45° one‐way wave equation.
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7

Kodell, Ralph L., and David W. Gaylor. "Uncertainty of Estimates of Cancer Risks Derived by Extrapolation from High to Low Doses and from Animals to Humans." International Journal of Toxicology 16, no. 4-5 (July 1997): 449–60. http://dx.doi.org/10.1080/109158197227062.

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The uncertainties associated with extrapolating model-based cancer risks from high to low doses and animal-based cancer risks to humans are examined. It is argued that low-dose linear extrapolation based on statistical confidence limits calculated from animal data is designed to account for data uncertainty, model-selection uncertainty, and model-fitting instability. The intent is to err on the side of safety, that is, overstating rather than understating the true risk. The tendency toward conservatism in predicting human cancer risks from animal data based on linear extrapolation is confirmed by a real-data analysis of the various sources of uncertainty involved in extrapolating from animals to humans. Along with the tendency toward conservatism, a high degree of overall uncertainty in the interspecies extrapolation process is demonstrated. It is concluded that human cancer risk estimates based on animal data may underestimate the true risk by a factor of 10 or may overestimate that risk by a factor of 1,000.
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8

Jing, J. R., Q. Li, X. Y. Ding, N. L. Sun, R. Tang, and Y. L. Cai. "AENN: A GENERATIVE ADVERSARIAL NEURAL NETWORK FOR WEATHER RADAR ECHO EXTRAPOLATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W9 (October 25, 2019): 89–94. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w9-89-2019.

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Abstract. Weather radar echo is one of the fundamental data for meteorological workers to weather systems identification and classification. Through the technique of weather radar echo extrapolation, the future short-term weather conditions can be predicted and severe convection storms can be warned. However, traditional extrapolation methods cannot offer accurate enough extrapolation results since their modeling capacity is limited, the recent deep learning based methods make some progress but still remains a problem of blurry prediction when making deeper extrapolation, which may due to they choose the mean square error as their loss function and that will lead to losing echo details. To address this problem and make a more realistic and accurate extrapolation, we propose a deep learning model called Adversarial Extrapolation Neural Network (AENN), which is a Generative Adversarial Network (GAN) structure and consist of a conditional generator and two discriminators, echo-frame discriminator and echo-sequence discriminator. The generator and discriminators are trained alternately in an adversarial way to make the final extrapolation results be realistic and accurate. To evaluate the model, we conduct experiments on extrapolating 0.5h, 1h, and 1.5h imminent future echoes, the results show that our proposed AENN can achieve the expected effect and outperforms other models significantly, which has a powerful potential application value for short-term weather forecasting.
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9

Mousa, Wail A., Mirko van der Baan, Said Boussakta, and Desmond C. McLernon. "Designing stable extrapolators for explicit depth extrapolation of 2D and 3D wavefields using projections onto convex sets." GEOPHYSICS 74, no. 2 (March 2009): S33—S45. http://dx.doi.org/10.1190/1.3077621.

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We have developed a robust algorithm for designing explicit depth extrapolation operators using the projections-onto-convex-sets (POCS) method. The operators are optimal in the sense that they satisfy all required extrapolation design characteristics. In addition, we propose a simple modification of the POCS algorithm (modified POCS, or MPOCS) that further enhances the stability of extrapolated wavefields and reduces the number of iterations required to design such operators to approximately 2% of that required for the basic POCS design algorithm. Various synthetic tests show that 25-coefficient 1D extrapolation operators, which have 13 unique coefficients, can accommodate dip angles up to 70°. We migrated the SEG/EAGE salt model data with the operators and compare our results with images obtained via extrapolators based on modified Taylor series and with wavefield extrapolation techniques such as phase shift plus interpolation (PSPI) and split-step Fourier. The MPOCS algorithm provides practically stable depth extrapolators. The resulting migrated section is comparable in quality to an expensive PSPI result and visibly outperforms the other two techniques. Strong dips and subsalt structures are imaged clearly. Finally, we extended the 1D extrapolator design algorithm, using MPOCS for 2D extrapolation, to the 2D case to perform 3D extrapolation; the result is a perfect circularly symmetric migration impulse response.
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Suominen, Arho, and Marko Seppänen. "Bibliometric data and actual development in technology life cycles: flaws in assumptions." Foresight 16, no. 1 (March 4, 2014): 37–53. http://dx.doi.org/10.1108/fs-03-2013-0007.

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Purpose – Motivated with the ever growing number of bibliometric trend extrapolation studies, the purpose of this paper is to demonstrate through two technologies how the selection of an upper limit of growth affects the correlation and causality of technology development measured with bibliometric data. Design/methodology/approach – The paper uses Gompertz and Fisher-Pry curves to model the technological development of white light emitting diodes and flash memory, and show with extrapolation results from several bibliometric sources how a typical bias is caused in trend extrapolations. Findings – The paper shows how drastic an effect the decision to set an upper bound has on trend extrapolations, to be used as a reference for applications. The paper recommends carefully examining the interconnection of actual development and bibliometric activity. Originality/value – Despite increasing interest in modelling technological data using this method, reports rarely discuss basic assumptions and their effects on outcomes. Since trend extrapolations are applied more widely in different disciplines, the basic limitations of methods should be explicitly expressed.
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Дисертації з теми "Extrapolation"

1

Johnson, Walter William. "Studies in motion extrapolation /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487265143146004.

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Domingo, Salazar Carlos. "Endpoint estimates via extrapolation theory." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/396143.

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In this thesis, we study different variants of Rubio de Francia’s extrapolation that allow us to obtain estimates near L1. This theory is subsequently applied to deduce enpoint boundedness for the Bochner-Riesz operator and other classes of multipliers. We also present results related to Yano’s extrapolation on Lorentz spaces and how it can be related to the theory of weights.
En aquesta tesi, estudiem variants de l’extrapolació de Rubio de Francia que permetin obtenir estimacions a prop de l’espai L1. Aquesta teoria l’apliquem després per deduïr acotacions a l’extrem per l’operador de Bochner-Riesz i altres classes de multiplicadors. També presentem altres resultats sobre teoria d’extrapolació de tipus Yano en espais de Lorentz i sobre com es pot relacionar amb la teoria de pesos.
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3

Salam, Ahmed. "Extrapolation : extension et nouveaux résultats." Lille 1, 1993. http://www.theses.fr/1993LIL10191.

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4

Lin, Tim T. Y., and Felix J. Herrmann. "Compressed wavefield extrapolation with curvelets." Society of Exploration Geophysicists, 2007. http://hdl.handle.net/2429/560.

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An explicit algorithm for the extrapolation of one-way wavefields is proposed which combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in {3-D}. By using ideas from ``compressed sensing'', we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. According {to} compressed sensing theory, signals can successfully be recovered from an imcomplete set of measurements when the measurement basis is incoherent} with the representation in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can successfully be extrapolated in the modal domain via a computationally cheaper operatoion. A proof of principle for the ``compressed sensing'' method is given for wavefield extrapolation in 2-D. The results show that our method is stable and produces identical results compared to the direct application of the full extrapolation operator.
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5

Lim, Hee Jin. "Facilitatory neural dynamics for predictive extrapolation." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1759.

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6

Fang, Zhide. "Robust extrapolation designs for linear models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0035/NQ46835.pdf.

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7

Segovia, Carlos. "Extrapolation and commutators of singular integrals." Pontificia Universidad Católica del Perú, 2014. http://repositorio.pucp.edu.pe/index/handle/123456789/97397.

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l. Introduction In these notes we shall present results concerning LP inequalities with different but related weights for commutators of singular and strongly singular integrals. These commutators turn out to be controlled by commutator of fractional order of the Hardy-Littlewood maximal operator. The boundedness properties are obtained by extrapolation from infinity. These notes are based mainly on [G-H-S-T].
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8

Musielak-Mersak, Céline. "Vieillissement cognitif, apprentissage fonctionnel et extrapolation." Reims, 2005. http://theses.univ-reims.fr/exl-doc/GED00000224.pdf.

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L'objectif de ce travail est d'étudier l'effet du vieillissement sur les processus cognitifs impliqués dans l'abstraction et l'adaptation aux relations complexes de l'environnement. Un total de 208 personnes (âgées de 18-25, 40-50, 65-75 et 76-90 ans) ont participé à cette étude. Dans l'expérience 1, l'apprentissage de fonctions curvilinéaires (en forme de U et de U-inversé) est comparé à celui de fonctions linéaires (directe et inverse). Un test d'extrapolation permet d'évaluer la qualité de l'abstraction selon la fonction apprise. Les résultats montrent que les personnes âgées conservent leurs capacités d'extrapolation, tout particulièrement lorsque la fonction est directe. Les différences liées à l'âge ne sont que quantitatives. Ces performances peuvent s'interpréter dans le cadre théorique des fonctions exécutives. L'expérience 2 permet d'observer l'impact de l'âge sur le passage d'une stratégie d'apprentissage fonctionnelle à une stratégie associative. Les résultats témoignent des difficultés éprouvées par les personnes âgées lorsque aucune fonction ne peut être exploitée pour relier les variables. La flexibilité et la capacité de mémoire de travail des personnes âgées seraient insuffisantes pour leur permettre un passage réussi entre les deux stratégies. Un projet est présenté. Il permettrait d'étudier l'effet de l'âge sur l'apprentissage de relations probabilistes en présence d'indices non pertinents
The aim of the present study is to examine the effect of aging on abstraction and adjustment to the complex relationships of the environement. A total of 208 individuals (aged 18-25, 40-50, 65-75, 76-90 years old) participated in this study. In experiment 1, the leaming of curvilinear functions (U-shaped and Inverse U-shaped functions) is compared with the learning of linear functions (direct and inverse functions). An extrapolation test is conducted to examine abstraction. Results show that extrapolation capacities are preserved in the elderly, especially when the relation between cue and criterion is a direct one. Age related differences are only quantitative. The results can be interpreted within the theoretical framework of executive functions. Experiment 2 is aimed at examining the effects of aging on shirting from a functional strategy of learning to an associative strategy of learning. Resuls show difficulties in older people when no function can be used to associate variables. It seems that, in the elderly, the lack of flexibility and the reduction of working memory capacity prevent the shirting between the two strategies. A project is presented. It is aimed at examining the effect of aging on multiple-cue probability learning tasks with non pertinent eues
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Beals, Mark J. "Radar target imaging using data extrapolation." Connect to resource, 1993. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1200677949.

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Musielak-Mersak, Céline Chasseigne Gérard. "Vieillissement cognitif, apprentissage fonctionnel et extrapolation." Reims : Éditeur, 2005. http://scdurca.univ-reims.fr/exl-doc/GED00000224.pdf.

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Книги з теми "Extrapolation"

1

Extrapolation blues! Alexandria, VA: Alexander Street Press, 2003.

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2

Milman, Mario. Extrapolation and Optimal Decompositions. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/bfb0073498.

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Brezinski, Claude, and Michela Redivo-Zaglia. Extrapolation and Rational Approximation. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58418-4.

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4

Calabrese, Edward J. Principles of animal extrapolation. Chelsea, Mich: Lewis Publishers, 1991.

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5

Jawerth, Björn. Extrapolation theory with applications. Providence, R.I., USA: American Mathematical Society, 1991.

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6

Brezinski, Claude. Extrapolation methods: Theory and practice. Amsterdam: North-Holland, 1991.

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7

Derek, Littlewood, and Stockwell Peter, eds. Impossibility fiction: Alternativity, extrapolation, speculation. Amsterdam: Rodopi, 1996.

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8

Lakonishok, Josef. Contrarian investment, extrapolation, and risk. Cambridge, MA: National Bureau of Economic Research, 1993.

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Lakonishok, Josef. Contrarian investment, extrapolation, and risk. Champaign: University of Illinois at Urbana-Champaign, 1993.

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10

Celant, Giorgio, and Michel Broniatowski. Interpolation and Extrapolation Optimal Designs 1. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119292272.

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Частини книг з теми "Extrapolation"

1

Zhu, Peijuan, and Andrej Skerjanec. "Extrapolation." In Biosimilars, 571–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99680-6_21.

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Gooch, Jan W. "Extrapolation." In Encyclopedic Dictionary of Polymers, 982. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15242.

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Kenny, Peter. "Extrapolation." In Better Business Decisions from Data, 179–81. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0184-8_17.

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Metcalfe, Mike. "Extrapolation." In Forecasting Profit, 139–78. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2255-3_7.

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Plowright, Philip D. "Extrapolation." In Making Architecture Through Being Human, 196–98. Abingdon, Oxon ; New York, NY : Routledge, 2020.: Routledge, 2019. http://dx.doi.org/10.4324/9780429261718-56.

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Carroll, R. J., D. Ruppert, and L. A. Stefanski. "Simulation Extrapolation." In Measurement Error in Nonlinear Models, 79–106. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4477-1_4.

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Travis, Curtis C. "Interspecies Extrapolation." In Biologically Based Methods for Cancer Risk Assessment, 63–78. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4684-5625-7_7.

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Smith, Stanley K., Jeff Tayman, and David A. Swanson. "Extrapolation Methods." In A Practitioner's Guide to State and Local Population Projections, 185–213. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7551-0_8.

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Swanson, David A., and Jeff Tayman. "Extrapolation Methods." In The Springer Series on Demographic Methods and Population Analysis, 115–35. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-90-481-8954-0_6.

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Hairer, Ernst, and Gerhard Wanner. "Extrapolation Methods." In Springer Series in Computational Mathematics, 426–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-05221-7_29.

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Тези доповідей конференцій з теми "Extrapolation"

1

Zhang, Yiming, Nam Ho Kim, Chanyoung Park, and Raphael T. Haftka. "Function Extrapolation at One Inaccessible Point Using Converging Lines." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47689.

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Анотація:
Focus of this paper is on the prediction accuracy of multidimensional functions at an inaccessible point. The paper explores the possibility of extrapolating a high-dimensional function using multiple one-dimensional converging lines. The main idea is to select samples along lines towards the inaccessible point. Multi-dimensional extrapolation is thus transformed into a series of one-dimensional extrapolations that provide multiple estimates at the inaccessible point. We demonstrate the performance of converging lines using Kriging to extrapolate a two-dimensional drag coefficient function. Post-processing of extrapolation results from different lines based on Bayesian theory is proposed to combine the multiple predictions. Selection of lines is also discussed. The method of converging lines proves to be more robust and reliable than two-dimensional Kriging surrogate for the example.
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2

Lee, Sangjin, Hyeongmin Lee, Taeoh Kim, and Sangyoun Lee. "Extrapolative-Interpolative Cycle-Consistency Learning For Video Frame Extrapolation." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191286.

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3

Hooker, Giles. "Diagnosing extrapolation." In the 2004 ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1014052.1014121.

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4

Gaudet, Shaun R., and J. E. Donald Gauthier. "A Simple Sub-Idle Component Map Extrapolation Method." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27193.

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This paper describes a simple sub-idle component map extrapolation method. Used in conjunction with gas turbine performance models, it enables designers to estimate sub-idle gas turbine performance during engine start-up. The lack of information available regarding component maps in the sub-idle regime creates major challenges for starting system designers or control system designers as the numerical convergence of performance models decreases rapidly below idle speed. The proposed component map extrapolation method alleviates this problem by extrapolating given component map data well below idle speed. The underlying equations of the method are based on the principles of incompressible similarity laws. Also known as pump laws, these equations are modified to account for compressibility effects by varying the similarity law exponents. To estimate the integrity of the extrapolated component maps and to build confidence in the sub-idle extrapolation method, extrapolate speed lines were compared to speed lines found in the original component map. Even though the extrapolation method is yet to be experimentally validated, preliminary estimates showed that the extrapolation method did produce adequate component maps. To demonstrate the potential of the component extrapolation method when used in conjunction with gas turbine performance models, a virtual test case engine was modeled and used to produce start-up performance data.
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5

Lévy, Bruno. "Dual domain extrapolation." In ACM SIGGRAPH 2003 Papers. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/1201775.882277.

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6

Woerdeman, Hugo J., and Akram Aldroubi. "Extrapolation in multiresolutions." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Andrew F. Laine and Michael A. Unser. SPIE, 1994. http://dx.doi.org/10.1117/12.188764.

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7

Mitchell, Jeff, Pontus Stenetorp, Pasquale Minervini, and Sebastian Riedel. "Extrapolation in NLP." In Proceedings of the Workshop on Generalization in the Age of Deep Learning. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-1005.

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8

Eckert, Zulah, and Gary Nutt. "Parallel Program Trace Extrapolation." In 1994 International Conference on Parallel Processing (ICPP'94). IEEE, 1994. http://dx.doi.org/10.1109/icpp.1994.151.

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Duttagupta, Subhasri, and Rupinder Singh Virk. "PerfExt: Performance Extrapolation Tool." In 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2012. http://dx.doi.org/10.1109/cimsim.2012.71.

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Katz, Amnon, and Kenneth Graham. "Extrapolation of airplane states." In Flight Simualtion Technologies Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-3428.

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Звіти організацій з теми "Extrapolation"

1

Barberis, Nicholas, Robin Greenwood, Lawrence Jin, and Andrei Shleifer. Extrapolation and Bubbles. Cambridge, MA: National Bureau of Economic Research, January 2016. http://dx.doi.org/10.3386/w21944.

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2

Lakonishok, Josef, Robert Vishny, and Andrei Shleifer. Contrarian Investment, Extrapolation, and Risk. Cambridge, MA: National Bureau of Economic Research, May 1993. http://dx.doi.org/10.3386/w4360.

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3

Xing, Tao, and Fred Stern. Factors of Safety for Richardson Extrapolation. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada498086.

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4

Jenkins, Chris. Automation and Database Collection and Extrapolation. Office of Scientific and Technical Information (OSTI), February 2016. http://dx.doi.org/10.2172/1814437.

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5

Munson, Miles, and Kegelmeyer, W. Philip,. Builtin vs. auxiliary detection of extrapolation risk. Office of Scientific and Technical Information (OSTI), February 2013. http://dx.doi.org/10.2172/1095941.

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Cogan, James, and Brian Reen. A Method for Extrapolation of Atmospheric Soundings. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada604460.

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Parzen G. Prediction of Long Term Stability by Extrapolation. Office of Scientific and Technical Information (OSTI), June 2000. http://dx.doi.org/10.2172/1061614.

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Mullahy, John. Econometric Risk Adjustment, Endogeneity, and Extrapolation Bias. Cambridge, MA: National Bureau of Economic Research, May 2006. http://dx.doi.org/10.3386/w12236.

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Hale, D. Stable explicit depth extrapolation of seismic wavefields. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/6507600.

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Medina, Richard L., and Richard A. Albanese. Animal-to-Human Extrapolation Using Compartmental Models. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada234082.

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