General discrepancy estimates III: The Erd�s-Tur�n-Koksma inequality for the Haar function system

1995 ◽  
Vol 120 (1) ◽  
pp. 25-45 ◽  
Author(s):  
Peter Hellekalek
Keyword(s):  
Function System ◽  
Haar Function

scholarly journals General discrepancy estimates II: the Haar function system

1994 ◽  
Vol 67 (4) ◽  
pp. 313-322 ◽  
Author(s):  
Peter Hellekalek
Keyword(s):  
Function System ◽  
Haar Function

Delis-Kaplan Executive Function System for Adults With Learning Disabilities

2006 ◽  
Author(s):  
Noam E. Wittlin ◽  
Linda A. Reddy ◽  
Lana A. Tiersky
Keyword(s):  
Learning Disabilities ◽  
Executive Function ◽  
Function System ◽  
Adults With Learning Disabilities

ARCLENGTH AS THE INVARIANT MEASURE FOR AN IFS WITH PROBABILITIES

Fractals ◽  
2015 ◽  
Vol 23 (04) ◽  
pp. 1550046
Author(s):  
D. LA TORRE ◽  
F. MENDIVIL
Keyword(s):  
Invariant Measure ◽  
Iterated Function System ◽  
Function System ◽  
Iterated Function

Given a continuous rectifiable function [Formula: see text], we present a simple Iterated Function System (IFS) with probabilities whose invariant measure is the normalized arclength measure on the graph of [Formula: see text].

Stochastic Fractal Modeling and Process Planning for Machining Using Two-Dimensional Iterated Function System

2008 ◽  
Vol 392-394 ◽  
pp. 575-579
Author(s):  
Yu Hao Li ◽  
Jing Chun Feng ◽  
Y. Li ◽  
Yu Han Wang
Keyword(s):  
Path Planning ◽  
Tool Path ◽  
Iterated Function System ◽  
Numerical Control ◽  
Function System ◽  
Tool Path Planning ◽  
Ongoing Research ◽  
Iterated Function ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Self-affine and stochastic affine transforms of R2 Iterated Function System (IFS) are investigated in this paper for manufacturing non-continuous objects in nature that exhibit fractal nature. A method for modeling and fabricating fractal bio-shapes using machining is presented. Tool path planning algorithm for numerical control machining is presented for the geometries generated by our fractal generation function. The tool path planning algorithm is implemented on a CNC machine, through executing limited number of iteration. This paper describes part of our ongoing research that attempts to break through the limitation of current CAD/CAM and CNC systems that are oriented to Euclidean geometry objects.

scholarly journals A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. A. Camilleri ◽  
S. B. Eickhoff ◽  
S. Weis ◽  
J. Chen ◽  
J. Amunts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Executive Function ◽  
Executive Functioning ◽  
Factor Model ◽  
Five Factor Model ◽  
Principal Component ◽  
Function System ◽  
Learning Approach ◽  
Psychometric Data ◽  
Machine Learning Approach

AbstractWhile a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis–Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.

Modelling of tree crowns with realistic morphological features: New reconstruction methodology based on Iterated Function System tool

2011 ◽  
Vol 222 (3) ◽  
pp. 503-513 ◽  
Author(s):  
A. Collin ◽  
A. Lamorlette ◽  
D. Bernardin ◽  
O. Séro-Guillaume
Keyword(s):  
Iterated Function System ◽  
Morphological Features ◽  
Function System ◽  
Tree Crowns ◽  
Iterated Function
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