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Books on the topic 'Structure representations'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Plate, Tony A. Distributed representations and nested compositional structure. Toronto: University of Toronto, Dept. of Computer Science, 1994.

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2

Huckleberry, Alan, Ivan Penkov, and Gregg Zuckerman, eds. Lie Groups: Structure, Actions, and Representations. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7193-6.

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3

Jacobson, Nathan. Structure and representations of Jordan algebras. Providence: American Mathematical Society, 2008.

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4

Dinsmore, John. Partitioned Representations: A Study in Mental Representation, Language Understanding and Linguistic Structure. Dordrecht: Springer Netherlands, 1991.

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5

Partitioned representations: A study in mental representation, language understanding, and linguistic structure. Dordrecht: Kluwer Academic Publishers, 1991.

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6

Karpilovsky, Gregory. Structure of blocks of group algebras. Harlow, Essex, England: Longman Scientific & Technical, 1987.

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7

The analytic theory of multiplicative Galois structure. Providence, R.I., USA: American Mathematical Society, 1989.

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8

Puig, Lluis. On the local structure of Morita and Rickard equivalences between Brauer blocks. Basel: Springer, 1999.

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9

On the local structure of Morita and Rickard equivalences between Brauer blocks. Basel: Birkhäuser, 1999.

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10

Cornwall County Council. Planning Directorate. Cornwall structure plan: Statement of decisions in respect of objections and representations to the proposed modifications : March 1997. Truro: Cornwall County Council, 1996.

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11

Lambrecht, Knud. Information structure and sentence form: Topic, focus, and the mental representations of discourse referents. Cambridge: Cambridge University Press, 1994.

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12

Pascal, Moliner. La représentation sociale comme grille de lecture: Étude expérimentale de sa structure et aperçu sur ses processus de transformation. Aix-en-Provence: Publications Université de Provence, 1992.

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13

1943-, Wiegand Roger, ed. Cohen-Macaulay representations. Providence, R.I: American Mathematical Society, 2012.

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14

The Sanskrit language: An overview : history and structure, linguistic and philosophical representations, uses and users. Varanasi: Indica Books, 2000.

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15

Principal structures and methods of representation theory. Providence, R.I: American Mathematical Society, 2005.

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16

The structure of perceptual experience. Chichester, West Sussex: John Wiley & Sons Inc., 2015.

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17

Harper, Robert. Structure and representation in LF. Edinburgh: University of Edinburgh, Laboratory for Foundations in Computer Science, 1989.

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18

Argument structure: Representation and theory. Dordrecht: Springer, 2011.

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19

de la Peña, José A., Ernesto Vallejo, and Natig Atakishiyev, eds. Algebraic Structures and Their Representations. Providence, Rhode Island: American Mathematical Society, 2005. http://dx.doi.org/10.1090/conm/376.

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20

Morecroft, John D. W. Strategy and the representation of structure. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1986.

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21

Plate, Tony A. Holographic reduced representation: Distributed representation for cognitive structures. Stanford, CA: CSLI Publications, 2003.

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22

René, Schott, ed. Algebraic structures and operator calculus. Dordrecht: Kluwer Academic, 1993.

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23

Evolving hierarchical systems: Their structure and representation. New York: Columbia University Press, 1985.

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24

Seeing objects: The structure of visual representation. Paderborn: mentis, 2010.

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25

Goodstein, L. P. Representation of Process State, Structure and Control. Roskilde: Riso National Laboratory, 1987.

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26

Beyond Kuhn: Scientific explanation, theory structure, incommensurability, and physical necessity. Aldershot, Hants, England: Ashgate Pub., 2006.

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27

Representation and invariance of scientific structures. Stanford, Calif: CSLI Publications, 2002.

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28

Classificatory structures: Concepts, relations and representation. Frankfurt/Main: Indeks Verlag, 1995.

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29

Eklund, Peter W., Gerard Ellis, and Graham Mann, eds. Conceptual Structures: Knowledge Representation as Interlingua. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61534-2.

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30

Cabanes, Marc, ed. Finite Reductive Groups: Related Structures and Representations. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-4124-9.

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31

Persson, Torsten. How do electoral rules shape party structures, government coalitions, and economic policies? Cambridge, Mass: National Bureau of Economic Research, 2003.

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32

Igusa, Kiyoshi, 1949- editor of compilation, Martsinkovsky, A. (Alex), editor of compilation, and Todorov, G. (Gordana), editor of compilation, eds. Expository lectures on representation theory: Maurice Auslander Distinguished Lectures and International Conference, April 25-30, 2012, Woods Hole Oceanographic Institute, Quissett Campus, Falmouth, MA. Providence, Rhode Island: American Mathematical Society, 2014.

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33

Hajnicz, Elżbieta. Time structures: Formal description and algorithmic representation. Berlin: Springer, 1996.

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34

Plate, Tony A. Distributed representations and nested compositional structure. 1994.

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35

Leshinskaya, Anna, and Alfonso Caramazza. Organization and Structure of Conceptual Representations. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199735471.013.025.

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36

Educational Resources Information Center (U.S.), ed. Quantifying the characteristics of knowledge structure representations: A lattice-theoretic framework. Los Angeles, CA: Center for the Study of Evaluation, National Center for Research on Evaluation, Standards, and Student Testing, Graduate School of Education & Information Studies, University of California, Los Angeles, 1998.

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37

Hewitt, Edwin, and Kenneth A. Ross. Abstract Harmonic Analysis: Structure of Topological Groups Integration Theory Group Representations. Springer-Verlag, 1987.

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38

Mattock, Peter. Visible Maths: Using Representations and Structure to Enhance Mathematics Teaching in Schools. Crown House Publishing, 2019.

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39

Pilz, Brian S. On the Loewy structure of projective modules for some semilinear groups. 1993.

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40

Bodle, James H. The effects of task structure and level of interest on infants' event representations. 1993.

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41

Bodle, James H. The effects of task structure and level of interest on infants' event representations. 1993.

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42

Ross, Kenneth A., and Edwin Hewitt. Abstract Harmonic Analysis: Volume I Structure of Topological Groups Integration Theory Group Representations. Springer, 2013.

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43

Abstract Harmonic Analysis: Volume 1: Structure of Topological Groups. Integration Theory. Group Representations (Grundlehren der mathematischen Wissenschaften). Springer, 1994.

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44

Cattani, Eduardo. Introduction to Variations of Hodge Structure. Edited by Eduardo Cattani, Fouad El Zein, Phillip A. Griffiths, and Lê Dũng Tráng. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691161341.003.0007.

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This chapter emphasizes the theory of abstract variations of Hodge structure (VHS) and, in particular, their asymptotic behavior. It first studies the basic correspondence between local systems, representations of the fundamental group, and bundles with a flat connection. The chapter then turns to analytic families of smooth projective varieties, the Kodaira–Spencer map, Griffiths' period map, and a discussion of its main properties: holomorphicity and horizontality. These properties motivate the notion of an abstract VHS. Next, the chapter defines the classifying spaces for polarized Hodge structures and studies some of their basic properties. Finally, the chapter deals with the asymptotics of a period mapping with particular attention to Schmid's orbit theorems.

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45

Austerweil, Joseph L., Samuel J. Gershman, and Thomas L. Griffiths. Structure and Flexibility in Bayesian Models of Cognition. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.9.

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Probability theory forms a natural framework for explaining the impressive success of people at solving many difficult inductive problems, such as learning words and categories, inferring the relevant features of objects, and identifying functional relationships. Probabilistic models of cognition use Bayes’s rule to identify probable structures or representations that could have generated a set of observations, whether the observations are sensory input or the output of other psychological processes. In this chapter we address an important question that arises within this framework: How do people infer representations that are complex enough to faithfully encode the world but not so complex that they “overfit” noise in the data? We discuss nonparametric Bayesian models as a potential answer to this question. To do so, first we present the mathematical background necessary to understand nonparametric Bayesian models. We then delve into nonparametric Bayesian models for three types of hidden structure: clusters, features, and functions. Finally, we conclude with a summary and discussion of open questions for future research.

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46

Hewitt, E., and K. a. Ross. Abstract Harmonic Analysis: Structure of Topological Groups-Integration Theory-Group Representations (Grundlehren Der Mathematischen Wissenschaften). 2nd ed. Springer-Verlag, 1995.

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47

Hewitt, Edwin, and Kenneth A. Ross. Abstract Harmonic Analysis: Volume 1: Structure of Topological Groups, Integration Theory, Group Representations (Grundlehren Der Mathematischen Wissenschaften). 2nd ed. Springer, 1987.

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48

Tzelgov, Joseph, Dana Ganor-Stern, Arava Kallai, and Michal Pinhas. Primitives and Non-primitives of Numerical Representations. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.019.

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Primitives of numerical representation are numbers holistically represented on the mental number line (MNL). Non-primitives are numbers generated from primitives in order to perform specific tasks. Primitives can be automatically retrieved from long-term memory (LTM). Using the size congruency effect in physical comparisons as a marker of automatic retrieval, and its modulation by intrapair numerical distance as an indication of alignment along the MNL, we identify single-digits, but not two-digit numbers, as primitives. By the same criteria, zero is a primitive, but negative numbers are not primitives, which makes zero the smallest numerical primitive. Due to their unique notational structure, fractions are automatically perceived as smaller than 1. While some specific, familiar unit fractions may be primitives, this can be shown only when component bias is eliminated by training participants to denote fractions by unfamiliar figures.

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49

Thevenot, Catherine, and Pierre Barrouillet. Arithmetic Word Problem Solving and Mental Representations. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.043.

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Arithmetic word problem solving is considered as a testing ground of mathematical achievement, but remains the area of mathematics in which students experience the greatest difficulties. In this chapter, we review recent theoretical and empirical work that could shed light on these difficulties. We first describe the most frequently used classifications of word problems and assess their psychological relevance. Then, we present the main hypotheses concerning the nature of the representations involved in word problems. Some theories assume that problem solving relies on the instantiation of schemas abstracted from recurrently encountered problems of the same relational structure, whereas other theories propose that ad hoc transient mental representations are constructed for each problem encountered. A third part is devoted to the impact of individual differences in calculation, reading comprehension, and more general factors, such as working memory capacity. Finally, we address the issue of enhancing performance in word problem solving.

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50

Camp, Elisabeth. Why Maps are Not Propositional. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198732570.003.0002.

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Many philosophers and logicians assume an exhaustive and exclusive dichotomy between “imagistic”, iconic, or pictorial representations and “discursive”, logical, or propositional ones. Maps seem to fall somewhere in between, with different theorists assimilating them to one or the other side of the divide. Given this assumption, philosophers and logicians interested in defending the logical tractability of maps have typically analyzed them as being predicative, where this is understood as a species of logical, propositional representation. This chapter argues that the best way to interpret the debate about propositionality is as concerning a representational system’s operative functional structure. Propositional structure is claimed to exhibit several distinctive properties: it is digital, asymmetrical, general, recursive, and hierarchical. However, there is little positive evidence that cartographic structure exhibits these features in the relevant sense.

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