Relevant bibliographies by topics / Distance function

Contents

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

Academic literature on the topic 'Distance function'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Distance function"

1

Färe, Rolf, Dimitris Margaritis, Paul Rouse, and Israfil Roshdi. "Estimating the hyperbolic distance function: A directional distance function approach." European Journal of Operational Research 254, no. 1 (October 2016): 312–19. http://dx.doi.org/10.1016/j.ejor.2016.03.045.

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Lee, Won-Ju, Min-Kyu Cheon, Chang-Ho Hyun, and Mi-Gnon Park. "Distance Sensitive AdaBoost using Distance Weight Function." International Journal of Fuzzy Logic and Intelligent Systems 12, no. 2 (June 30, 2012): 143–48. http://dx.doi.org/10.5391/ijfis.2012.12.2.143.

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Tjani, Maria. "Distance of a Bloch Function to the Little Bloch Space." Bulletin of the Australian Mathematical Society 74, no. 1 (January 2006): 101–19. http://dx.doi.org/10.1017/s0004972700047493.

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Motivated by a formula of P. Jones that gives the distance of a Bloch function to BMOA, the space of bounded mean oscillations, we obtain several formulas for the distance of a Bloch function to the little Bloch space, β0. Immediate consequences are equivalent expressions for functions in β0. We also give several examples of distances of specific functions to β0. We comment on connections between distance to β0 and the essential norm of some composition operators on the Bloch space, β. Finally we show that the distance formulas in β have Bloch type spaces analogues.
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Vijender, Dr C. "An Altering Distance Function in Fuzzy Metric Fixed Point Theorems." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 350–58. http://dx.doi.org/10.31142/ijtsrd2293.

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Jung, Jaewoon, Takaharu Mori, and Yuji Sugita. "2P072 Efficient Lookup Table using a Linear Function of Inverse Distance Squared(01D. Protein: Function,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S170. http://dx.doi.org/10.2142/biophys.53.s170_6.

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Vicini, Delio, Sébastien Speierer, and Wenzel Jakob. "Differentiable signed distance function rendering." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–18. http://dx.doi.org/10.1145/3528223.3530139.

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Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular interest but also poses severe challenges: shapes are intertwined with visibility, whose discontinuous nature introduces severe bias in computed derivatives unless costly precautions are taken. Shape representations like triangle meshes suffer from additional difficulties, since the continuous optimization of mesh parameters cannot introduce topological changes. One common solution to these difficulties entails representing shapes using signed distance functions (SDFs) and gradually adapting their zero level set during optimization. Previous differentiable rendering of SDFs did not fully account for visibility gradients and required the use of mask or silhouette supervision, or discretization into a triangle mesh. In this article, we show how to extend the commonly used sphere tracing algorithm so that it additionally outputs a reparameterization that provides the means to compute accurate shape parameter derivatives. At a high level, this resembles techniques for differentiable mesh rendering, though we show that the SDF representation admits a particularly efficient reparameterization that outperforms prior work. Our experiments demonstrate the reconstruction of (synthetic) objects without complex regularization or priors, using only a per-pixel RGB loss.
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Prangle, Dennis. "Adapting the ABC Distance Function." Bayesian Analysis 12, no. 1 (March 2017): 289–309. http://dx.doi.org/10.1214/16-ba1002.

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Gulisashvili, Archil, and Peter Laurence. "The Heston Riemannian distance function." Journal de Mathématiques Pures et Appliquées 101, no. 3 (March 2014): 303–29. http://dx.doi.org/10.1016/j.matpur.2013.06.004.

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Aparicio, Juan, Jesus T. Pastor, and Fernando Vidal. "The weighted additive distance function." European Journal of Operational Research 254, no. 1 (October 2016): 338–46. http://dx.doi.org/10.1016/j.ejor.2016.04.006.

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Pastor, Jesus T., Juan Aparicio, Javier Alcaraz, Fernando Vidal, and Diego Pastor. "Bounded directional distance function models." Central European Journal of Operations Research 26, no. 4 (July 12, 2018): 985–1004. http://dx.doi.org/10.1007/s10100-018-0562-7.

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Dissertations / Theses on the topic "Distance function"

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Regula, Meyer Lisa. "Genetic distance as a function of geographic distance in Ohio Dusky salamanders." Connect to resource, 2005. http://hdl.handle.net/1811/470.

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Thesis (Honors)--Ohio State University, 2005.
Title from first page of PDF file. Document formattted into pages: contains 20 p. Includes bibliographical references (p. 12-14). Available online via Ohio State University's Knowledge Bank.
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Mody, Ravi. "Optimizing the distance function for nearest neighbors classification." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p1470299.

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Thesis (M.S.)--University of California, San Diego, 2009.
Title from first page of PDF file (viewed December 2, 2009). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 48-49).
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Parker, M. J. "Mean values and distance functions in potential theory." Thesis, University of Liverpool, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382068.

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Rasouli, Alireza. "Calibrating the Distance-Deterrence Function for the Perth Metropolitan Area." Thesis, Curtin University, 2018. http://hdl.handle.net/20.500.11937/59663.

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The Perth metropolitan area and its surrounding regions have been expanding rapidly. With this rapid growth, consideration should be given to strategic modelling. Development of reliable model depends significantly on the calibrated parameters to reflect the existing situation. Deterrence functions play an important role for distribution of the trips and would simulate the trip distances. Therefore they should be calibrated for any particular models. This study aims to review the most common deterrence functions and calibrate them for the work trips.
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Singh, Rupesh Kumar. "Distance Learning and Attribute Importance Analysis by Linear Regression on Idealized Distance Functions." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495909607902884.

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Ragipi, Rushid Ajsuna. "Technical efficiency of Swedish district courts : - a stochastic distance function analysis." Thesis, Linnéuniversitetet, Institutionen för nationalekonomi och statistik (NS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78120.

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The aim of this study is to measure Swedish district courts’ technical efficiency for the period between 2000 and 2016 by applying the stochastic distance function approach. Although a very important issue from a policy perspective, a few studies have measured the efficiency of the courts. The narrow literature is also limited to using nonparametric methods, such as the DEA. The stochastic distance function has not been used for this purpose before and hence this is the first study to do so. The estimated mean score of technical efficiency is 93%. However, this study observes that efficiency levels increase throughout the studied period. Large variations between efficiency levels of different courts are also observed. Policy recommendations are to learn from courts with higher efficiency levels.
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Östman, Martin. "Video Coding Based on the Kantorovich Distance." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2330.

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In this Master Thesis, a model of a video coding system that uses the transportation plan taken from the calculation of the Kantorovich distance is developed. The coder uses the transportation plan instead of the differential image and sends it through blocks of transformation, quantization and coding.

The Kantorovich distance is a rather unknown distance metric that is used in optimization theory but is also applicable on images. It can be defined as the cheapest way to transport the mass of one image into another and the cost is determined by the distance function chosen to measure distance between pixels. The transportation plan is a set of finitely many five-dimensional vectors that show exactly how the mass should be moved from the transmitting pixel to the receiving pixel in order to achieve the Kantorovich distance between the images. A vector in the transportation plan is called an arc.

The original transportation plan was transformed into a new set of four-dimensional vectors called the modified difference plan. This set replaces the transmitting pixel and the receiving pixel with the distance from the transmitting pixel of the last arc and the relative distance between the receiving pixel and the transmitting pixel. The arcs where the receiving pixels are the same as the transmitting pixels are redundant and were removed. The coder completed an eleven frame sequence of size 128x128 pixels in eight to ten hours.

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Corbin, Sierra Fontaine. "Keeping Your Friends Close: Perceived Distance as a Function of Psychological Closeness." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1497502217239512.

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Tang, Siu-shing. "Integrating distance function learning and support vector machine for content-based image retrieval /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?CSED%202006%20TANG.

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Furumatsu, Noriko, and Jiro Nemoto. "Scale and scope economies of Japanese private universities revisited with an input distance function approach." Springer, 2014. http://hdl.handle.net/2237/20574.

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Books on the topic "Distance function"

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J, Berry Kenneth, ed. Permutation methods: A distance function approach. 2nd ed. New York: Springer, 2007.

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Hyperbolic complex spaces. Berlin: Springer, 1998.

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Kumar, Surender. Analysing industrial water demand in India: An input distance function approach. New Delhi: Publications Unit, National Institute of Public Finance and Policy, 2004.

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Mandal, Sabuj Kumar. Energy use efficiency in Indian cement industry: Application of data envelopment analysis and directional distance function. Bangalore: Institute for Social and Economic Change, 2009.

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S, Madheswaran, and Institute for Social and Economic Change, eds. Energy use efficiency in Indian cement industry: Application of data envelopment analysis and directional distance function. Bangalore: Institute for Social and Economic Change, 2009.

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Kumar, Surender. Resource use efficiency of US electricity generating plants during the SO₂ trading regime: A distance function approach. New Delhi: National Institute of Public Finance and Policy, 2004.

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Kumar, Surender. Resource use efficiency of US electricity generating plants during the SO₂ trading regime: A distance function approach. New Delhi: National Institute of Public Finance and Policy, 2004.

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Hailu, Atakelty. Environmentally sensitive productivity analysis of the Canadian pulp and paper industry, 1959-1994: An input distance function approach. Edmonton, Alta: Sustainable Forest Management Network, 1998.

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Gupta, Manish. Estimation of marginal abatement costs for undesirable outputs in India's power generation sector: An output distance function approach. New Delhi: Publications Unit, National Institute of Public Finance and Policy, 2005.

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1941-, Hag Kari, and Broch Ole Jacob, eds. The ubiquitous quasidisk. Providence, Rhode Island: American Mathematical Society, 2012.

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Book chapters on the topic "Distance function"

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Kao, Chiang. "Distance Function Efficiency Measures." In International Series in Operations Research & Management Science, 43–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31718-2_3.

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Dendek, Cezary, and Jacek Mańdziuk. "Probability-Based Distance Function for Distance-Based Classifiers." In Artificial Neural Networks – ICANN 2009, 141–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04274-4_15.

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Pastor, Jesus T., Juan Aparicio, Javier Alcaraz, Fernando Vidal, and Diego Pastor. "The Reverse Directional Distance Function." In International Series in Operations Research & Management Science, 15–57. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48461-7_2.

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Nagy, Benedek, Robin Strand, and Nicolas Normand. "A Weight Sequence Distance Function." In Lecture Notes in Computer Science, 292–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38294-9_25.

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Vanschoenwinkel, Bram, Feng Liu, and Bernard Manderick. "Context-Sensitive Kernel Functions: A Distance Function Viewpoint." In Advances in Machine Learning and Cybernetics, 861–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_90.

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Penner, Alvin. "Least Squares Orthogonal Distance Fitting." In Fitting Splines to a Parametric Function, 3–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12551-6_2.

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Iida, Kazuhiro. "Distance Perception and HRTF." In Head-Related Transfer Function and Acoustic Virtual Reality, 129–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9745-5_7.

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Lee, Ji-Young, and Jing Zhou. "Function and Identification of Mobile Transcription Factors." In Short and Long Distance Signaling, 61–86. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1532-0_3.

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Balinsky, Alexander A., W. Desmond Evans, and Roger T. Lewis. "Boundary Curvatures and the Distance Function." In The Analysis and Geometry of Hardy's Inequality, 49–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22870-9_2.

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Aparicio, Juan, Fernando Borras, Jesus T. Pastor, and Jose L. Zofio. "Loss Distance Functions and Profit Function: General Duality Results." In International Series in Operations Research & Management Science, 71–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48461-7_4.

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Conference papers on the topic "Distance function"

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Brown, Eli T., Jingjing Liu, Carla E. Brodley, and Remco Chang. "Dis-function: Learning distance functions interactively." In 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2012. http://dx.doi.org/10.1109/vast.2012.6400486.

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Burkardt, Matthias, Jian-Ping Chen, Karl Slifer, and Wally Melnitchouk. "The g[sub 2] Structure Function." In SPIN STRUCTURE AT LONG DISTANCE: Workshop Proceedings. AIP, 2009. http://dx.doi.org/10.1063/1.3203298.

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Sen, Yang, Liang Min, Guo Jiankui, Ruan Beijun, and Zhu Yangyong. "An Efficient Flexible Semantic Distance Function." In 2007 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.4347221.

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Liu, Jingjing, Eli T. Brown, and Remco Chang. "Find distance function, hide model inference." In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2011. http://dx.doi.org/10.1109/vast.2011.6102478.

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Barequet, Gill, Amy Briggs, Matthew Dickerson, Cristian Dima, and Michael T. Goodrich. "Animating the polygon-offset distance function." In the thirteenth annual symposium. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/262839.263097.

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Hillel, Aharon Bar, and Daphna Weinshall. "Learning distance function by coding similarity." In the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273505.

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Lata, A. S., Dinesh Rao, and M. G. M. Khan. "Calibration Estimation Using Proposed Distance Function." In 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2017. http://dx.doi.org/10.1109/apwconcse.2017.00037.

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Balint, Csaba, Gabor Valasek, and Lajos Gergo. "Operations on Signed Distance Function Estimates." In CAD'22. CAD Solutions LLC, 2022. http://dx.doi.org/10.14733/cadconfp.2022.329-333.

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Zhang, Zheng, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen. "Distance-weighted modulation transfer function measurement method." In Fifth Conference on Frontiers in Optical Imaging Technology and Applications, edited by Wenqing Liu, Huilin Jiang, and Junhao Chu. SPIE, 2018. http://dx.doi.org/10.1117/12.2506947.

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Morgachev, Gleb, Alexey Goncharov, and Vadim Strijov. "Distance Function Selection for Multivariate Time-Series." In 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI). IEEE, 2019. http://dx.doi.org/10.1109/ic-aiai48757.2019.00021.

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Reports on the topic "Distance function"

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Estellers, Virginia, Dominique Zosso, Rongjie Lai, Jean-Philippe Thiran, Stanley Osher, and Xavier Bresson. An Efficient Algorithm for Level Set Method Preserving Distance Function. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557314.

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Stenner, R., J. Droppo, R. Peloquin, R. Bienert, and N. VanHouten. Guidance manual on the estimation of airborne asbestos concentrations as a function of distance from a contaminated roadway for roadway screening. Office of Scientific and Technical Information (OSTI), April 1990. http://dx.doi.org/10.2172/7197832.

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Stenner, R., J. Droppo, R. Peloquin, R. Bienert, and N. VanHouten. Guidance manual on the estimation of airborne asbestos concentrations as a function of distance from a contaminated surface area for area suspension evaluations. Office of Scientific and Technical Information (OSTI), April 1990. http://dx.doi.org/10.2172/7028519.

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Zilberman, Mark. Shouldn’t Doppler 'De-boosting' be accounted for in calculations of intrinsic luminosity of Standard Candles? Intellectual Archive, September 2021. http://dx.doi.org/10.32370/iaj.2569.

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"Doppler boosting / de-boosting" is a well-known relativistic effect that alters the apparent luminosity of approaching/receding radiation sources. "Doppler boosting" alters the apparent luminosity of approaching light sources to appear brighter, while "Doppler de-boosting" alters the apparent luminosity of receding light sources to appear fainter. While "Doppler boosting / de-boosting" has been successfully accounted for and observed in relativistic jets of AGN, double white dwarfs, in search of exoplanets and stars in binary systems it was ignored in the establishment of Standard Candles for cosmological distances. A Standard Candle adjustment appears necessary for "Doppler de-boosting" for high Z, otherwise we would incorrectly assume that Standard Candles appear dimmer, not because of "Doppler de-boosting" but because of the excessive distance, which would affect the entire Standard Candles ladder at cosmological distances. The ratio between apparent (L) and intrinsic (Lo) luminosities as a function of redshift Z and spectral index α is given by the formula ℳ(Z) = L/Lo=(Z+1)^(α-3) and for Type Ia supernova as ℳ(Z) = L/Lo=(Z+1)^(-2). These formulas are obtained within the framework of Special Relativity and may require adjustments within the General Relativity framework.
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Zilberman, Mark. “Doppler de-boosting” and the observation of “Standard candles” in cosmology. Intellectual Archive, July 2021. http://dx.doi.org/10.32370/iaj.2549.

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“Doppler boosting” is a well-known relativistic effect that alters the apparent luminosity of approaching radiation sources. “Doppler de-boosting” is the name of relativistic effect observed for receding light sources (e.g. relativistic jets of active galactic nuclei and gamma-ray bursts). “Doppler boosting” changes the apparent luminosity of approaching light sources to appear brighter, while “Doppler de-boosting” causes the apparent luminosity of receding light sources to appear fainter. While “Doppler de-boosting” has been successfully accounted for and observed in relativistic jets of AGN, it was ignored in the establishment of Standard candles for cosmological distances. A Standard candle adjustment of an Z>0.1 is necessary for “Doppler de-boosting”, otherwise we would incorrectly assume that Standard Candles appear dimmer not because of “Doppler de-boosting” but because of the excessive distance, which would affect the entire Standard Candles ladder at cosmological distances. The ratio between apparent (L) and intrinsic (Lo) luminosities as a function of the redshift Z and spectral index α is given by the formula ℳ(Z) = L/Lo=(Z+1)α -3 and for Type Ia supernova appears as ℳ(Z) = L/Lo=(Z+1)-2. “Doppler de-boosting” may also explain the anomalously low luminosity of objects with a high Z without the introduction of an accelerated expansion of the Universe and Dark Energy.
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Zilberman, Mark. PREPRINT. “Doppler de-boosting” and the observation of “Standard candles” in cosmology. Intellectual Archive, June 2021. http://dx.doi.org/10.32370/ia_2021_06_23.

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PREPRINT. “Doppler boosting” is a well-known relativistic effect that alters the apparent luminosity of approaching radiation sources. “Doppler de-boosting” is the term of the same relativistic effect observed for receding light sources (e.g.relativistic jets of active galactic nuclei and gamma-ray bursts). “Doppler boosting” alters the apparent luminosity of approaching light sources to appear brighter, while “Doppler de-boosting” alters the apparent luminosity of receding light sources to appear fainter. While “Doppler de-boosting” has been successfully accounted for and observed in relativistic jets of AGN, it was ignored in the establishment of Standard candles for cosmological distances. A Standard candle adjustment of Z>0.1 is necessary for “Doppler de-boosting”, otherwise we would incorrectly assume that Standard Candles appear dimmer, not because of “Doppler de-boosting” but because of the excessive distance, which would affect the entire Standard Candles ladder at cosmological distances. The ratio between apparent (L) and intrinsic (Lo) luminosities as a function of the redshift Z and spectral index α is given by the formula ℳ(Z) =L/Lo=(Z+1)^(α-3) and for Type Ia supernova appears as ℳ(Z)=L/Lo=(Z+1)^(-2). “Doppler de-boosting” may also explain the anomalously low luminosity of objects with a high Z without the introduction of an accelerated expansion of the Universe and Dark Energy.
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Zilberman, Mark. "Doppler De-boosting" and the Observation of "Standard Candles" in Cosmology. Intellectual Archive, July 2021. http://dx.doi.org/10.32370/iaj.2552.

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“Doppler boosting” is a well-known relativistic effect that alters the apparent luminosity of approaching radiation sources. “Doppler de-boosting” is the same relativistic effect observed but for receding light sources (e.g. relativistic jets of AGN and GRB). “Doppler boosting” alters the apparent luminosity of approaching light sources to appear brighter, while “Doppler de-boosting” alters the apparent luminosity of receding light sources to appear fainter. While “Doppler de-boosting” has been successfully accounted for and observed in relativistic jets of AGN, it was ignored in the establishment of Standard candles for cosmological distances. A Standard Candle adjustment of Z>0.1 is necessary for “Doppler de-boosting”, otherwise we would incorrectly assume that Standard Candles appear dimmer, not because of “Doppler de-boosting” but because of the excessive distance, which would affect the entire Standard Candles ladder at cosmological distances. The ratio between apparent (L) and intrinsic (Lo) luminosities as a function of the redshift Z and spectral index α is given by the formula ℳ(Z) = L/Lo=(Z+1)α -3 and for Type Ia supernova appears as ℳ(Z) = L/Lo=(Z+1)-2. “Doppler de-boosting” may also explain the anomalously low luminosity of objects with a high Z without the introduction of an accelerated expansion of the Universe and Dark Energy.
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Memoli, Facundo, and Guillermo Sapiro. Distance Functions and Geodesics on Points Clouds. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada437158.

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Ilyin, M. E. The distance learning course «Theory of probability, mathematical statistics and random functions». OFERNIO, December 2018. http://dx.doi.org/10.12731/ofernio.2018.23529.

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Chapman, Martin C., G. A. Bollinger, and Matthew S. Sibol. Modeling Delay-Fired Explosion Spectra and Source Function Deconvolution at Regional Distances. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada260232.

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