![]() Moving the model through its various configurations is a great “stress reliever,” and I am so thankful I was able to fold this interesting design. I was ecstatic when I succeeded! I found it interesting how the group of folds that you repeat many times across each row reminded me of knitting. After all of my years of folding I felt hopeful that my experience and my skill would allow me to fold the model. Recently I saw this origami model in my “recommended” videos in Youtube and wanted to try it again. I tried folding the ball from the tutorial, but I could not figure out how to collapse the model after doing all of the pre-creasing. I was amazed how the paper could compress into a stick, how you could “squish” it into a ball, and how you could even turn it inside out! It was hard to imagine how a stiff piece of paper could become so flexible and fluid. I remember when I was very young, probably late in elementary school, I saw a Youtube video of this model. ![]() Johnstone I (2001) On the distribution of the largest eigenvalue in principal components analysis.The way this origami design moves is entrancing and unique to this model. McKay, (2012) Guide for Using buckygen (version 1.0),, last accessed on 22 February 2017Īnderson T (1963) Asymptotic theory for principal components analysis. McKay, (2011) Guide for Using plantri (version 4.5),, last accessed on 22 February 2017 Hoffman A (1970) “On eigenvalues and colorings of graphs,” Graph Theory and Its Applications H. Wilf H (1967) The eigenvalues of a graph and its chromatic number. Griffith D (2000) Eigenfunction properties and approximations of selected incidence matrices employed in spatial analyses. Griffith D, Sone A (1995) Trade-offs associated with normalizing constant computational simplifications for estimating spatial statistical models. Mahajan, The complexity of planarity testing. Brinkmann, (n.d.) fullgen Manual,, last accessed on 22 February 2017Į. (2002) Graph Theory and Geography: An Interactive View, Wiley, New York Ecology, available at (last accessed on 18 August 2018) Dray, (2018) Optimizing the choice of a spatial weighting matrix in eigenvector-based methods. The European Physical Journal B 71:259–271Įrmagun A, Levinson D (2018) An introduction to the network weight matrix. Masucci A, Smith D, Crooks A, Batty M (2009) Random planar graphs and the London street network. Griffith D (2017) Some robust assessments of Moran eigenvector spatial filtering. Páez A, Scott D, Volz E (2008) Weight matrices for social influence analysis: an investigation of measurement errors and their effect on model identification and estimation quality. Tobin, (2016) Three conjectures in extremal spectral graph theory, arXiv:1606.01916v1, last accessed on 15 December 2016 Spat Stat 1:100–109īoots B, Royal G (1991) A conjecture on the maximum value of the principal eigenvalue of a planar graph. Geographical Analysis, (2011) Issue 4, 43, 345–435įortin M-J, James P, MacKenzie A, Melles S, Rayfield B (2012) Spatial statistics, spatial regression, and graph theory in economy. Wilson (2005) An Atlas of Graphs, Clarendon press, Gloucestershire, England Shawe-Taylor (editors), Springer-Verlag, Berlin (2005) pp. “The number of planar graphs and properties of random planar graphs,” Proceedings of the 2005 International Conference on Analysis of Algorithms C. Journal of Combinatorial Theory, Series B 88:119–134īonichon N, Gavoille C, Hanusse N, Poulalhon D, Schaeffer G (2006) Planar graphs, via well-orderly maps and trees. Osthus D, Prömel H, Taraz A (2003) On random planar graphs, the number of planar graphs and their triangulations. Wagner, (2011) An experimental study on generating planar graphs, Karlsruhe Reports in Informatics,13, Karlsruhe Institute of Technology, Faculty of Informatics Karlsruhe, Germany, 2011 ![]() ![]() Bodirsky M, Gröpl C, Kang M (2007) Generating labeled planar graphs uniformly at random. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |