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Books on the topic 'Ensembles'

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Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Aggarwal, Charu C., and Saket Sathe. Outlier Ensembles. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7.

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2

Dearling, Robert. Keyboard instruments & ensembles. Philadelphia, PA: Chelsea House Publishers, 2000.

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3

Dearling, Robert. Keyboard instruments & ensembles. Philadelphia: Chelsea House Publishers, 2001.

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4

Ma, Yo-Yo. A playlist without borders. New York, NY: Masterworks, 2013.

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5

McDonald, Donna. The odyssey of the Philip Jones Brass Ensemble. Bulle, Switzerland: Editions Bim, 1986.

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6

Krivine, Jean-Louis. The orie des ensembles. Paris: Cassini, 1998.

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7

Hall, Michael J. W., and Marcel Reginatto. Ensembles on Configuration Space. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34166-8.

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8

Glass, Philip. Songs from liquid days. New York, N.Y: CBS, 1986.

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9

Berge, Claude. Hypergraphes: Combinatoire des ensembles finis. [Paris]: Gauthier-Villars, 1987.

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10

Sew it yourself nursery ensembles. Montrose, Pa: Chitra Publications, 1996.

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11

Music Educators National Conference (U.S.), ed. Strategies for teaching specialized ensembles. Reston, VA: Music Educators National Conference, 1999.

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12

Kahane, Jean-Pierre. Ensembles parfaits et séries trigonométriques. Paris: Hermann, 1994.

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13

Michelle, Guéritey, Lemoine Jean-Bernard, Paccard Pierre, and Neyrolles Yves, eds. Ensembles campanaires en Rhône-Alpes. Seyssel: Editions Comp'act, 1994.

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14

Darzacq, Denis. Denis Darzacq: Ensembles 1997-2000. Arles: Actes Sud, 2001.

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15

Okun, Oleg, Giorgio Valentini, and Matteo Re, eds. Ensembles in Machine Learning Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22910-7.

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16

Associated Board of the Royal Schools of Music. Jazz piano & jazz ensembles syllabus. London: Associated Board of the Royal Schools of Music, 1998.

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17

Cutietta, Robert A. Strategies for teaching specialized ensembles. Reston, VA: MENC, 1999.

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18

Okun, Oleg. Ensembles in Machine Learning Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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19

Brezin, Edouard, and Sinobu Hikami. Beta ensembles. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.20.

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This article deals with beta ensembles. Classical random matrix ensembles contain a parameter β, taking on the values 1, 2, and 4. This parameter, which relates to the underlying symmetry, appears as a repulsion sβ between neighbouring eigenvalues for small s. β may be regarded as a continuous positive parameter on the basis of different viewpoints of the eigenvalue probability density function for the classical random matrix ensembles - as the Boltzmann factor for a log-gas or the squared ground state wave function of a quantum many-body system. The article first considers log-gas systems before discussing the Fokker-Planck equation and the Calogero-Sutherland system. It then describes the random matrix realization of the β-generalization of the circular ensemble and concludes with an analysis of stochastic differential equations resulting from the case of the bulk scaling limit of the β-generalization of the Gaussian ensemble.

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20

Morozov, Alexei. Non-Hermitian ensembles. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.18.

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This article discusses the three-fold family of Ginibre random matrix ensembles (complex, real, and quaternion real) and their elliptic deformations. It also considers eigenvalue correlations that are exactly reduced to two-point kernels in the strongly and weakly non-Hermitian limits of large matrix size. Ginibre introduced the complex, real, and quaternion real random matrix ensembles as a mathematical extension of Hermitian random matrix theory. Statistics of complex eigenvalues are now used in modelling a wide range of physical phenomena. After providing an overview of the complex Ginibre ensemble, the article describes random contractions and the complex elliptic ensemble. It then examines real and quaternion-real Ginibre ensembles, along with real and quaternion-real elliptic ensembles. In particular, it analyses the kernel in the elliptic case as well as the limits of strong and weak non-Hermiticity.

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21

Spires, Ashley. Edie's Ensembles. Tundra Books, 2015.

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22

Jswd - Ensembles. Jovis Verlag GmbH, 2020.

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23

Party Ensembles. Leisure Arts, 2012.

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24

Socialist Ensembles. Univ of Minnesota Pr (Txt), 1994.

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25

Jazz Works for Ensembles (Jazz Ensembles S.). Associated Board of the Royal School of Music, 1999.

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26

Jazz Works for Ensembles (Jazz Ensembles S.). Associated Board of the Royal School of Music, 1999.

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27

Jazz Works for Ensembles (Jazz Ensembles S.). Associated Board of the Royal School of Music, 1999.

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28

Jazz Works for Ensembles (Jazz Ensembles S.). Associated Board of the Royal School of Music, 1999.

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29

Jazz Works for Ensembles (Jazz Ensembles S.). Associated Board of the Royal School of Music, 1999.

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30

Jazz Works for Ensembles (Jazz Ensembles S.). Associated Board of the Royal School of Music, 1999.

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31

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Ensembles with hard constraints. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0005.

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This chapter introduces random graph ensembles involving hard constraints such as setting a fixed total number of links or fixed degree sequence, including properties of the partition function. It continues on from the previous chapter’s investigation of ensembles with soft-constrained numbers of two-stars (two-step paths) and soft-constrained total number of triangles, but now combined with a hard constraint on the total number of links. This illustrates phase transitions in a mixed-constrained ensemble – which in this case is shown to be a condensation transition, where the network becomes clumped. This is investigated in detail using techniques from statistical mechanics and also looking at the averaged eigenvalue spectrum of the ensemble. These phase transition phenomena have important implications for the design of graph generation algorithms. Although hard constraints can (by force) impose required values of observables, difficult-to-reconcile constraints can lead to graphs being generated with unexpected and unphysical overall topologies.

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32

Cottrell, Stephen. The creative work of large ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199346677.003.0013.

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Preparing large ensembles for performance involves musical, social, logistical and financial challenges of a kind seldom encountered in other forms of collective music-making. The conventional approach to meeting the challenges that arise during rehearsal is to appoint a single musical overseer, usually a conductor, whose ostensible role in musical preparation is to directly influence the musicians while working towards the creation of a musical product to be delivered in later performances. Rehearsal leadership, viewed from this perspective, moves predominantly in one direction, from conductor to ensemble. But such a perspective oversimplifies the conductor’s relationship with the ensemble, the relationships between the musicians, and the strategies that the latter must employ when working in large ensembles. Conceptualizing the ensemble as a complex system of interrelated components, where leadership and creative agency are distributed and developed through rehearsal to achieve what audiences assume to be a unified whole, yields new understanding of the work of large ensembles. This chapter examines these components of the creative process in orchestral and choral rehearsal and performance, the internal and external forces shaping and constraining that process, and the approaches that individual musicians and conductors could adopt in response to the changing contexts in which such creativity might be manifested.

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33

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.

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This chapter presents some theoretical tools for defining random graph ensembles systematically via soft or hard topological constraints including working through some properties of the Erdös-Rényi random graph ensemble, which is the simplest non-trivial random graph ensemble where links appear between two nodes with a fixed probability p. The chapter sets out the central representation of graph generation as the result of a discrete-time Markovian stochastic process. This unites the two flavours of graph generation approaches – because they can be viewed as simply moving forwards or backwards through this representation. It is possible to define a random graph by an algorithm, and then calculate the associated stationary probability. The alternative approach is to specify sampling weights and then to construct an algorithm that will have these weights as the stationary probabilities upon convergence.

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34

Stuen-Walker, Elizabeth. Ensembles for Viola. Alfred Publishing Company, 1999.

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35

Ensembles for Viola. Alfred Publishing Company, 2000.

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36

Ensembles for Viola. Birch Tree Group Ltd, 1999.

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37

Mooney, Rick. Ensembles for Cello. Alfred Publishing Company, 1999.

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38

Ensembles for Flute. Birch Tree Group Ltd, 1999.

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39

American Theatre Ensembles. Bloomsbury Publishing Plc, 2021. http://dx.doi.org/10.5040/9781350051577.

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40

(EDT), Alfred Publishing. Ensembles for Flute. Alfred Publishing Company, 2002.

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41

Ensembles for Guitar. Summy-Birchard, 1999.

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42

Stuen-Walker, Elizabeth. Ensembles for Viola. Birch Tree Group Ltd, 1999.

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43

Celebrated Keyboard Ensembles. Alfred Publishing Co., Inc., 2010.

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44

Théorie des ensembles. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34035-5.

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45

Mooney, Rick. Ensembles for Cello. Alfred Publishing Company, 1999.

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46

Théorie des Ensembles. Springer London, Limited, 2007.

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47

Nicolas Bourbaki. Théorie des ensembles. Springer, 2006.

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48

National Aeronautics and Space Administration (NASA) Staff. Input Decimated Ensembles. Independently Published, 2018.

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49

Anderson, Greg W. Spectral statistics of orthogonal and symplectic ensembles. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.5.

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This article describes a direct approach for computing scalar and matrix kernels, respectively for the unitary ensembles on the one hand and the orthogonal and symplectic ensembles on the other hand, leading to correlation functions and gap probabilities. In the classical orthogonal polynomials (Hermite, Laguerre, and Jacobi), the matrix kernels for the orthogonal and symplectic ensemble are expressed in terms of the scalar kernel for the unitary case, using the relation between the classical orthogonal polynomials going with the unitary ensembles and the skew-orthogonal polynomials going with the orthogonal and symplectic ensembles. The article states the fundamental theorem relating the orthonormal and skew-orthonormal polynomials that enter into the Christoffel-Darboux kernels

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50

Farberman, Harold. Percussion Ensemble Collection: 4 Ensembles for 6 Players (Percussion Performance). Alfred Publishing Company, 1993.

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