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1

Aisenbrey, Silke. Optimal Matching Analyse. Wiesbaden: VS Verlag für Sozialwissenschaften, 2000. http://dx.doi.org/10.1007/978-3-663-10955-6.

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2

Thomas, Carlos. Search and matching functions and optimal monetary policy. London: Centre for Economic Performance, London School of Economics and Political Science, 2006.

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3

Katzman, Brett. Optimal monetary impulse-response functions in a matching model. Cambridge, MA: National Bureau of Economic Research, 1999.

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4

Katzman, Brett. Optimal monetary impulse-response functions in a matching model. [Minneapolis, Minn.]: Federal Reserve Bank of Minneapolis, 1999.

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5

Halpin, Brendan. Class careers as sequences: An optimal matching analysis of work-life histories. Colchester: ESRC Research Centre on Micro-social Change, 1996.

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6

Essays on Discrete Optimization: Optimal Stopping and Popular Matchings. [New York, N.Y.?]: [publisher not identified], 2022.

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7

Dawson, K. M. Implicit model matching as an approach to three-dimensional object recognition. Dublin: Trinity College, Department of Computer Science, 1991.

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8

Gloster, Lawrence Anthony William. Optical parametric oscillators: Noncollinear phase matching and linewidth studies in beta barium borate. Manchester: University of Manchester, 1995.

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9

Li, Yanjun. Jing xiang pi pei yu mu biao shi bie ji shu. Xi an: Xi bei gong ye ta xue chu ban she, 2009.

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10

I, Watson C., Paek Eung Gi, and National Institute of Standards and Technology (U.S.), eds. Effect of resolution and image quality on combined optical and neural network fingerprint matching. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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11

Template matching techniques in computer vision: Theory and practice. Chichester, West Sussex, U.K: Wiley, 2009.

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12

Wiskott, Laurenz. Labeled graphs and dynamic link matching for face recognition and scene analysis. Thun: Deutsch, 1995.

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13

Lee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. Amsterdam: IOS Press, 2003.

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14

Giancarlo, Raffaele. Combinatorial Pattern Matching: 22nd Annual Symposium, CPM 2011, Palermo, Italy, June 27-29, 2011. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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15

service), SpringerLink (Online, ed. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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16

Symposium on Combinatorial Pattern Matching (21st 2010 New York, N.Y.). Combinatorial pattern matching: 21st annual symposium, CPM 2010, New York, NY, USA, June 21-23, 2010 ; proceedings. Berlin: Springer, 2010.

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17

David, Hutchison. Combinatorial Pattern Matching: 20th Annual Symposium, CPM 2009 Lille, France, June 22-24, 2009 Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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18

Jens, Stoye, and SpringerLink (Online service), eds. Combinatorial Pattern Matching: 23rd Annual Symposium, CPM 2012, Helsinki, Finland, July 3-5, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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19

Fischer, Johannes. Combinatorial Pattern Matching: 24th Annual Symposium, CPM 2013, Bad Herrenalb, Germany, June 17-19, 2013. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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20

Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.

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21

Ron, Yachnin, ed. The optimal policy mix: Matching ends and means in environmental policy making. Ottawa: Conference Board of Canada, 2000.

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22

Ruitenbeek, Arnold John Van. A framework for matching user needs to an optimal level of office automation. 1988.

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23

Aisenbrey, Silke. Optimal Matching Analyse: Anwendungen in den Sozialwissenschaften (Studien zur Wissenschafts-- und Organisationssoziologie) (Volume 2) (German Edition). VS Verlag für Sozialwissenschaften, 2013.

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24

Fisher, David. A Compendium of Deformation-Mechanism Maps for Metals. Materials Research Forum LLC, 2022. http://dx.doi.org/10.21741/9781644901694.

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Deformation-mechanism maps represent an invaluable guide to predicting the optimum processing conditions for a material. They are also useful in matching a material to a given engineering application. The present book summarizes recent research results in the field. The book references 106 original resources and includes their direct web link for in-depth reading.
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25

Muthukrishnan, S., Suleyman C. Sahinalp, and Ugur Dogrusoz. Combinatorial Pattern Matching: 15th Annual Symposium, CPM 2004, Istanbul, Turkey, July 5-7, 2004, Proceedings. Springer London, Limited, 2004.

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26

Data Matching Concepts And Techniques For Record Linkage Entity Resolution And Duplicate Detection. Springer, 2012.

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27

Invariant Object Recognition Based on Elastic Graph Matching (Frontiers in Artificial Intelligence and Applications, 86). Ios Pr Inc, 2002.

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28

Christen, Peter. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, 2014.

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29

Christen, Peter. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, 2012.

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30

Combinatorial Pattern Matching 19th Annual Symposium Cpm 2008 Pisa Italy June 1820 2008 Proceedings. Springer, 2008.

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31

(Editor), Suleyman C. Sahinalp, S. Muthukrishnan (Editor), and Ugur Dogrusoz (Editor), eds. Combinatorial Pattern Matching: 15th Annual Symposium, CPM 2004, Istanbul, Turkey, July 5-7, 2004, Proceedings (Lecture Notes in Computer Science). Springer, 2004.

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32

Kuznetsov, Sergei O., Pavel Pevzner, and Alexander S. Kulikov. Combinatorial Pattern Matching: 25th Annual Symposium, CPM 2014, Moscow, Russia, June 16-18, 2014. Proceedings. Springer London, Limited, 2014.

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33

Combinatorial Pattern Matching: 25th Annual Symposium, CPM 2014, Moscow, Russia, June 16-18, 2014. Proceedings. Springer, 2014.

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34

Fischer, Johannes, and Peter Sanders. Combinatorial Pattern Matching: 24th Annual Symposium, CPM 2013, Bad Herrenalb, Germany, June 17-19, 2013, Proceedings. Springer Berlin / Heidelberg, 2013.

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35

Society of Photo-Optical Instrumentation Engineers (Corporate Author) and Thomas P. Ryan (Editor), eds. Matching the Energy Source to the Clinical Need: Proceedings of a Conference Held 23-24 January, 2000, San Jose, California (Critical Reviews of Optical Science and Technology). SPIE-International Society for Optical Engine, 2000.

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36

(Editor), Moshe Lewenstein, and Gabriel Valiente (Editor), eds. Combinatorial Pattern Matching: 17th Annual Symposium, CPM 2006, Barcelona, Spain, July 5-7, 2006, Proceedings (Lecture Notes in Computer Science). Springer, 2006.

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37

Wright, A. G. The Photomultiplier Handbook. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.001.0001.

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This handbook is aimed at helping users of PMTs who are faced with the challenge of designing sensitive light detectors for scientific and industrial purposes. The raison d’être for photomultipliers (PMTs) stems from four intrinsic attributes: large detection area, high, and noiseless gain, and wide bandwidth. Detection involves a conversion process from photons to photoelectrons at the photocathode. Photoelectrons are subsequently collected and increased in number by the action of an incorporated electron multiplier. Photon detection, charge multiplication, and many PMT applications are statistical in nature. For this reason appropriate statistical treatments are provided and derived from first principles. PMTs are characterized by a range of photocathodes offering detection over UV to infra-red wavelengths, the sensitivities of which can be calibrated by National Laboratories. The optical interface between light sources and PMTs, particularly for diffuse or uncollimated light, is sparsely covered in the scientific literature. The theory of light guides, Winston cones, and other light concentrators points to means for optimizing light collection subject to the constraints of Liouville’s theorem (étandue). Certain PMTs can detect single photons but are restricted by the limitations of unwanted background ranging in magnitude from a fraction of a photoelectron equivalent to hundreds of photoelectrons. These sources, together with their correlated nature, are examined in detail. Photomultiplier biasing requires a voltage divider comprising a series of resistors or active components, such as FETs. Correct biasing provides the key to linear operation and so considerable attention is given to the treatment of this topic. Electronic circuits and modules that perform the functions of charge to voltage conversion, pulse shaping, and impedance matching are analysed in detail.
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38

Wolfrum, Philipp. Information Routing, Correspondence Finding, and Object Recognition in the Brain. Springer, 2010.

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39

Wolfrum, Philipp. Information Routing, Correspondence Finding, and Object Recognition in the Brain. Springer, 2011.

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40

Wolfrum, Philipp. Information Routing, Correspondence Finding, and Object Recognition in the Brain. Springer, 2014.

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