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Books on the topic 'Network sequences'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Leader, Jeffery J. Neural network identification of keystream generators. Naval Postgraduate School, 1993.

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2

1951-, Sun Hsiao, ed. Protocol conformance testing using unique input/output sequences. World Scientific, 1997.

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3

Hegde, Malati. An experience in using UIO sequences and rural Chinese postman tours for conformance testing of a Q.931 implementation. Dept. of Electrical Communication Engineering, Indian Institute of Science, 1992.

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4

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24797-2.

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5

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012.

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6

Laan, Dinard van der. The structure and performance of optimal routing sequences. Universiteit Leiden, 2003.

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7

U, Bastolla, ed. Structural approaches to sequence evolution: Molecules, networks, populations. Springer, 2007.

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8

Lipo, Wang, and Alkon Daniel L, eds. Artificial neural networks: Oscillations, chaos, and sequence processing. IEEE Computer Society Press, 1993.

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9

Bozward, David Victor. Capacity improvements for a direct-sequence code division multiple access network. Aston University. Department of Electrical Engineering and Applied Physics, 1995.

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10

Frishman, Dmitrij. Modern genome annotation: The BioSapiens Network. Springer, 2009.

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11

Fazlollahi, Mina. Inferring Transcriptional and Post-Transcriptional Network Structure by Exploiting Natural Sequence Variation. [publisher not identified], 2013.

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12

Peruski, Leonard F. The Internet and the new biology: Tools for genomic and molecular research. ASM Press, 1997.

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13

Keeler, James David. Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memeory models. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1987.

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14

Golomb, Solomon W. Sequences, Subsequences, and Consequences: International Workshop, SSC 2007, Los Angeles, CA, USA, May 31 - June 2, 2007, Revised Invited Papers. Springer-Verlag Berlin Heidelberg, 2007.

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15

Jonathan, Jedwab, and SpringerLink (Online service), eds. Sequences and Their Applications – SETA 2012: 7th International Conference, Waterloo, ON, Canada, June 4-8, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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16

Lee, Giles C., Gori Marco, and International Summer School on Neural Networks (1997 : Salerno, Italy), eds. Adaptive processing of sequences and data structures: International Summer School on Neural Networks "E.R. Caianiello" Vietri sul Mare, Salerno, Italy, September 6-13 1997, tutorial lectures. Springer, 1998.

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17

J, Bishop M., ed. Guide to human genome computing. 2nd ed. Academic Press, 1998.

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18

Flapan, Erica. Knots, molecules, and the universe: An introduction to topology. American Mathematical Society, 2015.

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19

Holmes, Donald P. F. Development of control software for 2-port component calibration sequences using a vector network analyser with S-parameter unit. 1988.

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20

Holland, John H. 4. Agents, networks, degree, and recirculation. Oxford University Press, 2014. http://dx.doi.org/10.1093/actrade/9780199662548.003.0004.

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‘Agents, networks, degree, and recirculation’ explains that when studying complex adaptive systems (CAS) in a grammar-like way, agents serve as the ‘alphabet’. The hierarchical organization of CAS implies different kinds of agents at different levels, with correspondingly different grammars. The interactions of signal-processing agents at a point in time can be specified by a network—a snapshot of the agents’ performance capability. The combination of high fanout (the richness of an agent’s interactions) and hierarchical organization results in complex networks that include large numbers of se

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21

Newman, Mark. The configuration model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0012.

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A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, an

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22

Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin / Heidelberg, 2014.

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23

Supervised Sequence Labelling With Recurrent Neural Networks. Springer, 2012.

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24

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer, 2012.

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25

Bengio, Yoshua. Neural Networks for Speech and Sequence Recognition. Itp New Media, 1996.

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26

Netwalker Uprising: A Netwalk Sequence Novel. Reynolds-Ward, Joyce, 2013.

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27

Reynolds-Ward, Joyce. Netwalk's Children: A Netwalk Sequence Novel. CreateSpace Independent Publishing Platform, 2015.

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28

Johnson, Veretta. Phase sequence optimization in a network of multiphase signals. 1992.

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29

Bastolla, Ugo, Markus Porto, Eduardo Roman, and Michele Vendruscolo. Structural Approaches to Sequence Evolution: Molecules, Networks, Populations. Springer London, Limited, 2007.

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30

Bastolla, Ugo, Markus Porto, Eduardo Roman, and Michele Vendruscolo. Structural Approaches to Sequence Evolution: Molecules, Networks, Populations. Springer Berlin / Heidelberg, 2010.

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31

Wang, Lipo. Artificial Neural Networks: Oscillations, Chaos, and Sequence Processing. Ieee Computer Society, 1993.

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32

Wang, Lipo. Artificial Neural Networks: Oscillations, Chaos, and Sequence Processing. Ieee Computer Society, 1993.

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33

Foley, John Miles. Linkmaps. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037184.003.0007.

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This chapter introduces linkmaps, which are suggested routes through the wiki network, particular sequences of ePathways that have been found to be illuminating in one way or another through the Pathways Project website. The freedom to explore and to construe is nearly absolute, as a variety of patterns can be created and shared, and surfers have the freedom to engage suggested itineraries or diverge from established ePathways and form their own, fashioning their experiences at every juncture and thus creating linkmaps unique to themselves. Alongside instructions on how readers can share their

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34

Dong, Guozhu, and Jian Pei. Sequence Data Mining. Springer, 2010.

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35

Dong, Guozhu, and Jian Pei. Sequence Data Mining. Springer London, Limited, 2007.

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36

Using Direct-Sequenced Spread Spectrum in a Wired Local Area Network. Storming Media, 2001.

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37

A Direct Sequence Code-Division Multiple-Access Local Area Network Model. Storming Media, 2002.

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38

Fischer, Joerg. Optimal Sequence-Based Control of Networked Linear Systems. Karlsruhe Scientific Publishing, 2015.

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39

Fischer, Jörg. Optimal Sequence-Based Control of Networked Linear Systems. Saint Philip Street Press, 2020.

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40

Cleeremans, Axel. Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing. MIT Press, 2019.

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41

Murphy, Kai. Inferring Mechanism-Based Gene Regulatory Network Models from Expression and Sequence Data. Independently Published, 2019.

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42

Microarrays And Transcription Networks. Landes Bioscience, 2005.

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43

Frishman, Dmitrij, and Alfonso Valencia. Modern Genome Annotation: The Biosapiens Network. Springer, 2011.

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44

Davidson, Eric H. The Regulatory Genome: Gene Regulatory Networks In Development And Evolution. Academic Press, 2006.

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45

Davidson, Eric H. The Regulatory Genome: Gene Regulatory Networks In Development And Evolution. Academic Press, 2006.

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46

Bishop, Martin J. Guide to Human Genome Computing, Second Edition. 2nd ed. Academic Press, 1998.

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47

Bishop, Martin J. Guide to Human Genome Computing, Second Edition. Academic Press, 1998.

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48

Bishop, Martin J. Guide to Human Genome Computing. Elsevier Science & Technology Books, 1998.

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49

Direct-Sequence Spread-Spectrum Modulation for Utility Packet Transmission in Underwater Acoustic Communication Networks. Storming Media, 2002.

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50

Direct-Sequence Spread-Spectrum Modulation for Utility Packet Transmission in Underwater Acoustic Communication Networks. Storming Media, 2002.

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