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Journal articles on the topic 'Numerical representations'

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1

Siegler, Robert S., and John E. Opfer. "The Development of Numerical Estimation." Psychological Science 14, no. 3 (2003): 237–50. http://dx.doi.org/10.1111/1467-9280.02438.

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We examined children's and adults' numerical estimation and the representations that gave rise to their estimates. The results were inconsistent with two prominent models of numerical representation: the logarithmic-ruler model, which proposes that people of all ages possess a single, logarithmically spaced representation of numbers, and the accumulator model, which proposes that people of all ages represent numbers as linearly increasing magnitudes with scalar variability. Instead, the data indicated that individual children possess multiple numerical representations; that with increasing age
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2

Zhang, Jiajie, and Hongbin Wang. "The Effect of External Representations on Numeric Tasks." Quarterly Journal of Experimental Psychology Section A 58, no. 5 (2005): 817–38. http://dx.doi.org/10.1080/02724980443000340.

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This article explores the effect of external representations on numeric tasks. Through several minor modifications on the previously reported two-digit number comparison task, we obtained different results. Rather than holistic comparison, we found parallel comparison. We argue that this difference was a reflection of different representational forms: The comparison was based on internal representations in previous studies but on external representations in our present study. This representational effect is discussed under a framework of distributed number representations. We propose that in n
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3

Hauser, M. D., P. MacNeilage, and M. Ware. "Numerical representations in primates." Proceedings of the National Academy of Sciences 93, no. 4 (1996): 1514–17. http://dx.doi.org/10.1073/pnas.93.4.1514.

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4

Cohen, Dale J. "Numerical representations are neither abstract nor automatic." Behavioral and Brain Sciences 32, no. 3-4 (2009): 332–33. http://dx.doi.org/10.1017/s0140525x09990549.

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AbstractIn this commentary, I support and augment Cohen Kadosh & Walsh's (CK&W's) argument that numerical representations are not abstract. I briefly review data that support the non-abstract nature of the representation of numbers between zero and one, and I discuss how a failure to test alternative hypotheses has led researchers to erroneously conclude that numerals automatically activate their semantic meaning.
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5

Buijsman, Stefan, and Carlos Tirado. "Spatial–numerical associations: Shared symbolic and non-symbolic numerical representations." Quarterly Journal of Experimental Psychology 72, no. 10 (2019): 2423–36. http://dx.doi.org/10.1177/1747021819844503.

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During the last decades, there have been a large number of studies into the number-related abilities of humans. As a result, we know that humans and non-human animals have a system known as the approximate number system that allows them to distinguish between collections based on their number of items, separately from any counting procedures. Dehaene and others have argued for a model on which this system uses representations for numbers that are spatial in nature and are shared by our symbolic and non-symbolic processing of numbers. However, there is a conflicting theoretical perspective in w
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6

Falter, Christine M., Valdas Noreika, Julian Kiverstein, and Bruno Mölder. "Concrete magnitudes: From numbers to time." Behavioral and Brain Sciences 32, no. 3-4 (2009): 335–36. http://dx.doi.org/10.1017/s0140525x09990045.

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AbstractCohen Kadosh & Walsh (CK&W) present convincing evidence indicating the existence of notation-specific numerical representations in parietal cortex. We suggest that the same conclusions can be drawn for a particular type of numerical representation: the representation of time. Notation-dependent representations need not be limited to number but may also be extended to other magnitude-related contents processed in parietal cortex (Walsh 2003).
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7

Prather, Richard. "Individual differences in numerical comparison is independent of numerical precision." Journal of Numerical Cognition 5, no. 2 (2019): 220–40. http://dx.doi.org/10.5964/jnc.v5i2.164.

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Numeracy, as measured by performance on the non-symbolic numerical comparison task, is a key construct in numerical and mathematical cognition. The current study examines individual variation in performance on the numerical comparison task. We contrast the hypothesis that performance on the numerical comparison task is primarily due to more accurate representations of numbers with the hypothesis that performance dependent on decision-making factors. We present data from two behavioral experiments and a mathematical model. In both behavioral experiments we measure the precision of participant’s
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8

Smilde, Age K., and Thomas Hankemeier. "Numerical Representations of Metabolic Systems." Analytical Chemistry 92, no. 20 (2020): 13614–21. http://dx.doi.org/10.1021/acs.analchem.9b05613.

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9

Zhao, Jiaying, and Ru Yu. "Statistical regularities compress numerical representations." Journal of Vision 15, no. 12 (2015): 390. http://dx.doi.org/10.1167/15.12.390.

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10

Pesenti, Mauro, and Michael Andres. "Common mistakes about numerical representations." Behavioral and Brain Sciences 32, no. 3-4 (2009): 346–47. http://dx.doi.org/10.1017/s0140525x09990835.

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AbstractCohen Kadosh & Walsh (CK&W) argue that recent findings challenge the hypothesis of abstract numerical representations. Here we show that because, like many other authors in the field, they rely on inaccurate definitions of abstract and non-abstract representations, CK&W fail to provide compelling evidence against the abstract view.
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11

Gensemer, Susan H. "On numerical representations of semiorders." Mathematical Social Sciences 15, no. 3 (1988): 277–86. http://dx.doi.org/10.1016/0165-4896(88)90012-1.

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12

Grabner, Roland H. "Expertise in symbol-referent mapping." Behavioral and Brain Sciences 32, no. 3-4 (2009): 338–39. http://dx.doi.org/10.1017/s0140525x09990793.

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AbstractMuch evidence cited by Cohen Kadosh & Walsh (CK&W) in support of their notation-specific representation hypothesis is based on tasks requiring automatic number processing. Several of these findings can be alternatively explained by differential expertise in mapping numerical symbols onto semantic magnitude representations. The importance of considering symbol-referent mapping expertise in theories on numerical representations is highlighted.
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13

Cantlon, Jessica F., Sara Cordes, Melissa E. Libertus, and Elizabeth M. Brannon. "Numerical abstraction: It ain't broke." Behavioral and Brain Sciences 32, no. 3-4 (2009): 331–32. http://dx.doi.org/10.1017/s0140525x09990513.

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AbstractThe dual-code proposal of number representation put forward by Cohen Kadosh & Walsh (CK&W) accounts for only a fraction of the many modes of numerical abstraction. Contrary to their proposal, robust data from human infants and nonhuman animals indicate that abstract numerical representations are psychologically primitive. Additionally, much of the behavioral and neural data cited to support CK&W's proposal is, in fact, neutral on the issue of numerical abstraction.
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14

Matsuura, Tsutomu, and Saburou Saitoh. "Integral and Direct Representations of Nonlinear Inverse Mapping." Applied Mechanics and Materials 36 (October 2010): 476–84. http://dx.doi.org/10.4028/www.scientific.net/amm.36.476.

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In this paper we shall give practical, numerical and explicit representations of inverse mappings of n-dimensional mappings (of the solutions of n-nonlinear simultaneous equations) and show their numerical experiments by using computers. We derive those concrete formulas from very general ideas for the representation of the inverse functions.
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15

Fishburn, Peter. "Preference structures and their numerical representations." Theoretical Computer Science 217, no. 2 (1999): 359–83. http://dx.doi.org/10.1016/s0304-3975(98)00277-1.

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16

Booth, Julie L., and Robert S. Siegler. "Numerical Magnitude Representations Influence Arithmetic Learning." Child Development 79, no. 4 (2008): 1016–31. http://dx.doi.org/10.1111/j.1467-8624.2008.01173.x.

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17

Olsen, M. K., and A. S. Bradley. "Numerical representation of quantum states in the positive-P and Wigner representations." Optics Communications 282, no. 19 (2009): 3924–29. http://dx.doi.org/10.1016/j.optcom.2009.06.033.

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18

Qin, Dan. "A Note on Numerical Representations of Nested System of Strict Partial Orders." Games 12, no. 3 (2021): 57. http://dx.doi.org/10.3390/g12030057.

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This note provides two numerical representations of a nested system of strict partial orders. The first representation is based on utility and threshold functions. We generalize the threshold representation of menu-dependent preferences by allowing the threshold to depend not only on the menu but also on the pair of alternatives under comparison. The threshold function can be interpreted as the distance between alternatives. The second representation is based on the aggregation of multi-dimensional preference. This representation describes a decision-making procedure where multiple criteria ar
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19

Day, Steven M., and Keith L. McLaughlin. "Seismic source representations for spall." Bulletin of the Seismological Society of America 81, no. 1 (1991): 191–201. http://dx.doi.org/10.1785/bssa0810010191.

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Abstract Spall may be a significant secondary source of seismic waves from underground explosions. The proper representation of spall as a seismic source is important for forward and inverse modeling of explosions for yield estimation and discrimination studies. We present a new derivation of a widely used point force representation for spall, which is based on a horizontal tension crack model. The derivation clarifies the relationship between point force and moment tensor representations of the tension crack. For wavelengths long compared with spall depth, the two representations are equivale
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20

Abdüsselam, Mustafa Serkan, and Ebru Turan-Güntepe. "Examination of the Transitions between Modal Representations in Coding Training." International Journal of Computer Science Education in Schools 5, no. 1 (2021): 3–15. http://dx.doi.org/10.21585/ijcses.v5i1.125.

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This study aims to determine the perceptions of undergraduates, who are receiving coding training in a faculty of education, on modal representations employed in the training process and identify their transition skills between representations. The research used the quantity search method, non-experimental design, and descriptive search models, calculating the obtained data frequencies by numerical analysis. The study was carried out with the participation of 58 undergraduates in the Computer and Instructional Technology Department of an education faculty in the 2018-2019 academic year. The re
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21

Haghir Chehreghani, Morteza, and Mostafa Haghir Chehreghani. "Learning representations from dendrograms." Machine Learning 109, no. 9-10 (2020): 1779–802. http://dx.doi.org/10.1007/s10994-020-05895-3.

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Abstract We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different distance measures and representations can be inferred from different types of dendrograms, level functions and distance functions. Via an appropriate embedding, we compute a vector-based represen
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22

Uller, Claudia, Susan Carey, Gavin Huntley-Fenner, and Laura Klatt. "What representations might underlie infant numerical knowledge?" Cognitive Development 14, no. 1 (1999): 1–36. http://dx.doi.org/10.1016/s0885-2014(99)80016-1.

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23

Soares Neto, J. J., and L. S. Costa. "Numerical Generation of Optimized Discrete Variable Representations." Brazilian Journal of Physics 28, no. 1 (1998): 1–11. http://dx.doi.org/10.1590/s0103-97331998000100001.

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24

Wang, Yu, Yu Luo, Alejandra Echeverri, and Jiaying Zhao. "Visual and numerical representations of dynamic systems." Journal of Vision 16, no. 12 (2016): 1106. http://dx.doi.org/10.1167/16.12.1106.

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25

Wang, He Yu, Feng Cui, and Xing Hua Wang. "Explicit Representations for Local Lagrangian Numerical Differentiation." Acta Mathematica Sinica, English Series 23, no. 2 (2006): 365–72. http://dx.doi.org/10.1007/s10114-005-0902-0.

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26

Drapeau, Samuel, and Asgar Jamneshan. "Conditional preference orders and their numerical representations." Journal of Mathematical Economics 63 (March 2016): 106–18. http://dx.doi.org/10.1016/j.jmateco.2015.12.004.

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27

Bikchentaev, Airat M., and Rinat S. Yakushev. "Representation of tripotents and representations via tripotents." Linear Algebra and its Applications 435, no. 9 (2011): 2156–65. http://dx.doi.org/10.1016/j.laa.2011.04.003.

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28

Olsen, M. K., R. J. Lewis-Swan, and A. S. Bradley. "Errata: Numerical representation of quantum states in the positive-P and Wigner representations." Optics Communications 370 (July 2016): 327–28. http://dx.doi.org/10.1016/j.optcom.2016.02.068.

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29

Krause, Florian, Oliver Lindemann, Ivan Toni, and Harold Bekkering. "Different Brains Process Numbers Differently: Structural Bases of Individual Differences in Spatial and Nonspatial Number Representations." Journal of Cognitive Neuroscience 26, no. 4 (2014): 768–76. http://dx.doi.org/10.1162/jocn_a_00518.

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A dominant hypothesis on how the brain processes numerical size proposes a spatial representation of numbers as positions on a “mental number line.” An alternative hypothesis considers numbers as elements of a generalized representation of sensorimotor-related magnitude, which is not obligatorily spatial. Here we show that individuals' relative use of spatial and nonspatial representations has a cerebral counterpart in the structural organization of the posterior parietal cortex. Interindividual variability in the linkage between numbers and spatial responses (faster left responses to small nu
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30

Peters, Ellen, and Alan Castel. "Numerical representation, math skills, memory, and decision-making." Behavioral and Brain Sciences 32, no. 3-4 (2009): 347–48. http://dx.doi.org/10.1017/s0140525x09990847.

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AbstractThe consideration of deliberate versus automatic processing of numeric representations is important to math education, memory for numbers, and decision-making. In this commentary, we address the possible roles for numeric representations in such higher-level cognitive processes. Current evidence is consistent with important roles for both automatic and deliberative processing of the representations.
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31

Khutoryansky, Naum, and Horacio Sosa. "Construction of Dynamic Fundamental Solutions for Piezoelectric Solids." Applied Mechanics Reviews 48, no. 11S (1995): S222—S229. http://dx.doi.org/10.1115/1.3005076.

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Fundamental solutions are derived within the framework of transient dynamic, three-dimensional piezoelectricity. The purpose of the article is to show alternate integral representations for such solutions. Thus, a representation over the unit sphere in accordance to a methodology based on the plane wave decomposition is provided. It is shown, however, that more efficient representations from a computational point of view can be achieved through appropriate coordinate transformations. Hence, representations of the fundamental solutions over surfaces of slowness are provided as novel alternative
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32

Munari, Cosimo. "Multi-utility representations of incomplete preferences induced by set-valued risk measures." Finance and Stochastics 25, no. 1 (2020): 77–99. http://dx.doi.org/10.1007/s00780-020-00440-5.

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AbstractWe establish a variety of numerical representations of preference relations induced by set-valued risk measures. Because of the general incompleteness of such preferences, we have to deal with multi-utility representations. We look for representations that are both parsimonious (the family of representing functionals is indexed by a tractable set of parameters) and well behaved (the representing functionals satisfy nice regularity properties with respect to the structure of the underlying space of alternatives). The key to our results is a general dual representation of set-valued risk
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33

Demidovskij, Alexander, and Eduard Babkin. "Adapting Neural Turing Machines for linguistic assessments aggregation in neural-symbolic decision support systems." Information and Control Systems, no. 5 (October 26, 2021): 40–50. http://dx.doi.org/10.31799/1684-8853-2021-5-40-50.

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Introduction: The construction of integrated neurosymbolic systems is an urgent and challenging task. Building neurosymbolic decision support systems requires new approaches to represent knowledge about a problem situation and to express symbolic reasoning at the subsymbolic level. Purpose: Development of neural network architectures and methods for effective distributed knowledge representation and subsymbolic reasoning in decision support systems in terms of algorithms for aggregation of fuzzy expert evaluations to select alternative solutions. Methods: Representation of fuzzy and uncertain
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34

Butterfill, Stephen A. "Infants' representations of causation." Behavioral and Brain Sciences 34, no. 3 (2011): 126–27. http://dx.doi.org/10.1017/s0140525x10002426.

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AbstractIt is consistent with the evidence in The Origin of Concepts to conjecture that infants' causal representations, like their numerical representations, are not continuous with adults', so that bootstrapping is needed in both cases.
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35

Piantadosi, Steven T., and Jessica F. Cantlon. "True Numerical Cognition in the Wild." Psychological Science 28, no. 4 (2017): 462–69. http://dx.doi.org/10.1177/0956797616686862.

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Cognitive and neural research over the past few decades has produced sophisticated models of the representations and algorithms underlying numerical reasoning in humans and other animals. These models make precise predictions for how humans and other animals should behave when faced with quantitative decisions, yet primarily have been tested only in laboratory tasks. We used data from wild baboons’ troop movements recently reported by Strandburg-Peshkin, Farine, Couzin, and Crofoot (2015) to compare a variety of models of quantitative decision making. We found that the decisions made by these
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36

De Hevia, Maria-Dolores, Luisa Girelli, Emanuela Bricolo, and Giuseppe Vallar. "The representational space of numerical magnitude: Illusions of length." Quarterly Journal of Experimental Psychology 61, no. 10 (2008): 1496–514. http://dx.doi.org/10.1080/17470210701560674.

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In recent years, a growing amount of evidence concerning the relationships between numerical and spatial representations has been interpreted, by and large, in favour of the mental number line hypothesis—namely, the analogue continuum where numbers are spatially represented (Dehaene, 1992; Dehaene, Piazza, Pinel, & Cohen, 2003). This numerical representation is considered the core of number meaning and, accordingly, needs to be accessed whenever numbers are semantically processed. The present study explored, by means of a length reproduction task, whether besides the activation of laterali
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37

Ashkenazi, Sarit, and Yulia Tsyganov. "The Cognitive Estimation Task is nonunitary: Evidence for multiple magnitude representation mechanisms among normative and ADHD college students." Journal of Numerical Cognition 2, no. 3 (2017): 220–46. http://dx.doi.org/10.5964/jnc.v2i3.3.

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There is a current debate on whether the cognitive system has a shared representation for all magnitudes or whether there are unique representations. To investigate this question, we used the Biber cognitive estimation task. In this task, participants were asked to provide estimates for questions such as, “How many sticks of spaghetti are in a package?” The task uses different estimation categories (e.g., time, numerical quantity, distance, and weight) to look at real-life magnitude representations. Experiment 1 assessed (N = 95) a Hebrew version of the Biber Cognitive Estimation Task and foun
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38

Mehta, Michael D., and Paul Simpson-Housley. "Effect of Numerical Representations of Risk on Perception." Perceptual and Motor Skills 84, no. 3 (1997): 714. http://dx.doi.org/10.2466/pms.1997.84.3.714.

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39

Huber, Stefan, Korbinian Moeller, and Hans-Christoph Nuerk. "Dissociating Number Line Estimations from Underlying Numerical Representations." Quarterly Journal of Experimental Psychology 67, no. 5 (2014): 991–1003. http://dx.doi.org/10.1080/17470218.2013.838974.

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40

Predescu, Cristian, and J. D. Doll. "Optimal series representations for numerical path integral simulations." Journal of Chemical Physics 117, no. 16 (2002): 7448–63. http://dx.doi.org/10.1063/1.1509058.

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41

Constantinides, George, Adam Kinsman, and Nicola Nicolici. "Numerical Data Representations for FPGA-Based Scientific Computing." IEEE Design & Test of Computers 28, no. 4 (2011): 8–17. http://dx.doi.org/10.1109/mdt.2011.48.

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42

Mendizabal-Ruiz, Gerardo, Israel Román-Godínez, Sulema Torres-Ramos, Ricardo A. Salido-Ruiz, and J. Alejandro Morales. "On DNA numerical representations for genomic similarity computation." PLOS ONE 12, no. 3 (2017): e0173288. http://dx.doi.org/10.1371/journal.pone.0173288.

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43

Cellier, F. E., and SungDo Chi. "Numerical Properties of Trajectory Representations of Polynomial Matrices." IFAC Proceedings Volumes 24, no. 4 (1991): 177–81. http://dx.doi.org/10.1016/s1474-6670(17)54267-6.

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44

Odic, D., and J. Halberda. "Representations of Difficulty and Confidence in Numerical Discrimination." Journal of Vision 12, no. 9 (2012): 805. http://dx.doi.org/10.1167/12.9.805.

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45

Vu, Xuan-Ha, Djamila Sam-Haroud, and Boi Faltings. "Enhancing numerical constraint propagation using multiple inclusion representations." Annals of Mathematics and Artificial Intelligence 55, no. 3-4 (2009): 295–354. http://dx.doi.org/10.1007/s10472-009-9129-6.

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46

De Cruz, Helen. "How do spatial representations enhance cognitive numerical processing?" Cognitive Processing 13, S1 (2012): 137–40. http://dx.doi.org/10.1007/s10339-012-0445-0.

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47

Medvyatskaya, Alisa M., and Vasily A. Ogorodnikov. "Approximate spectral models of random processes with periodic properties." Russian Journal of Numerical Analysis and Mathematical Modelling 34, no. 6 (2019): 353–60. http://dx.doi.org/10.1515/rnam-2019-0030.

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Abstract We consider approaches to simulation of periodically correlated random processes based on the nonstandard spectral representation of the process with parameters periodically varying in time and also on spectral representations using the vector stationary Gaussian processes.
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48

PAI, MADHUSUDAN G., and SHANKAR SUBRAMANIAM. "A comprehensive probability density function formalism for multiphase flows." Journal of Fluid Mechanics 628 (June 1, 2009): 181–228. http://dx.doi.org/10.1017/s002211200900617x.

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A theoretical foundation for two widely used statistical representations of multiphase flows, namely the Eulerian–Eulerian (EE) and Lagrangian–Eulerian (LE) representations, is established in the framework of the probability density function (p.d.f.) formalism. Consistency relationships between fundamental statistical quantities in the EE and LE representations are rigorously established. It is shown that fundamental quantities in the two statistical representations bear an exact relationship to each other only under conditions of spatial homogeneity. Transport equations for the probability de
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49

Kyrchei, Ivan I. "Determinantal Representations of the Core Inverse and Its Generalizations with Applications." Journal of Mathematics 2019 (October 1, 2019): 1–13. http://dx.doi.org/10.1155/2019/1631979.

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In this paper, we give the direct method to find of the core inverse and its generalizations that is based on their determinantal representations. New determinantal representations of the right and left core inverses, the right and left core-EP inverses, and the DMP, MPD, and CMP inverses are derived by using determinantal representations of the Moore-Penrose and Drazin inverses previously obtained by the author. Since the Bott-Duffin inverse has close relation with the core inverse, we give its determinantal representation and its application in finding solutions of the constrained linear equ
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50

Faulkenberry, Thomas J., Alexander Cruise, and Samuel Shaki. "Reversing the Manual Digit Bias in Two-Digit Number Comparison." Experimental Psychology 64, no. 3 (2017): 191–204. http://dx.doi.org/10.1027/1618-3169/a000365.

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Abstract. Though recent work in numerical cognition has supported a strong tie between numerical and spatial representations (e.g., a mental number line), less is known about such ties in multi-digit number representations. Along this line, Bloechle, Huber, and Moeller (2015) found that pointing positions in two-digit number comparison were biased leftward toward the decade digit. Moreover, this bias was reduced in unit-decade incompatible pairs. In the present study, we tracked computer mouse movements as participants compared two-digit numbers to a fixed standard (55). Similar to Bloechle et
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