Understanding Information: From the Big Bang to Big Data 1st ed. 2017 [Kõva köide]

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  • Formaat: Hardback, 237 pages, kõrgus x laius: 235x155 mm, kaal: 5089 g, 27 Illustrations, black and white; XVIII, 237 p. 27 illus., 1 Hardback
  • Sari: Advanced Information and Knowledge Processing
  • Ilmumisaeg: 04-Aug-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319590898
  • ISBN-13: 9783319590899
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Understanding Information: From the Big Bang to Big Data 1st ed. 2017Suurem pilt
  • Formaat: Hardback, 237 pages, kõrgus x laius: 235x155 mm, kaal: 5089 g, 27 Illustrations, black and white; XVIII, 237 p. 27 illus., 1 Hardback
  • Sari: Advanced Information and Knowledge Processing
  • Ilmumisaeg: 04-Aug-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319590898
  • ISBN-13: 9783319590899
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Püsilink: https://www.kriso.ee/db/9783319590899.html
Märksõnad:
The motivation of this edited book is to generate an understanding about information, related concepts and the roles they play in the modern, technology permeated world. In order to achieve our goal, we observe how information is understood in domains, such as cosmology, physics, biology, neuroscience, computer science, artificial intelligence, the Internet, big data, information society, or philosophy. Together, these observations form an integrated view so that readers can better understand this exciting building-block of modern-day society.





On the surface, information is a relatively straightforward and intuitive concept. Underneath, however, information is a relatively versatile and mysterious entity. For instance, the way a physicist looks at information is not necessarily the same way as that of a biologist, a neuroscientist, a computer scientist, or a philosopher. Actually, when it comes to information, it is common that each field has its domain specific views, motivations, interpretations, definitions, methods, technologies, and challenges.





With contributions by authors from a wide range of backgrounds, Understanding Information: From the Big Bang to Big Data will appeal to readers interested in the impact of information on modern-day life from a variety of perspectives.

Arvustused

In this text, Schuster (Waseda Univ., Japan) aims to examine the broad topic of information from a wide variety of angles which could provide graduate students with indications of suitable fields for research. This text deserves a place as a reference in all university collections. Summing Up: Recommended. Upper-division undergraduates through faculty and professionals. (R. Bharath, Choice, Vol. 55 (10), June, 2018) This is a very good book! Its a thought piece about a range of ideas that couples information across several disciplines and across large timeframes. Each chapter has an extensive and good-quality bibliography. A quick citation search of the references suggests that the resources are well cited. This is a well-written book. (Robert M. Lynch, Computing Reviews, April, 2018)

Part I Introduction
1 From the Tannhauser Gate to z8_GND_5296: A Day Trip on the Life-Cycle of Information
3(26)
Alfons Josef Schuster
1.1 Introduction
3(2)
1.2 From Caveman to Spaceman
5(2)
1.3 Digits, Revolutions, and the Information Life-Cycle
7(4)
1.3.1 The Information Life-Cycle
10(1)
1.4 Data, Information, and Knowledge
11(3)
1.4.1 Data
12(1)
1.4.2 Information
13(1)
1.4.3 Knowledge
13(1)
1.5 Fundamental Information Life-Cycle Processes
14(6)
1.5.1 Acquisition and Collection
15(1)
1.5.2 Storage and Classification
16(1)
1.5.3 Analysis and Manipulation
17(2)
1.5.4 Retrieval, Dissemination, Usage, and Maintenance
19(1)
1.6 Information Society
20(3)
1.6.1 Decentralized Information Society
20(2)
1.6.2 A Voice for Information Society
22(1)
1.7 Summary
23(6)
References
23(6)
Part II The World of Large and Small Systems
2 Expanding Beyond the Solar System: Current Observation and Theory
29(22)
Ko Yamada
Satoshi Inaba
2.1 Introduction
29(1)
2.2 Observation of Extrasolar Planets
30(5)
2.2.1 Radial Velocity Survey Detection
32(1)
2.2.2 Transit Search Detection
33(1)
2.2.3 Gravitational Microlensing Detection
34(1)
2.2.4 Direct Detection
35(1)
2.3 Characteristic of Extrasolar Planets
35(4)
2.4 Planet Formation
39(5)
2.4.1 Formation of a Protoplanetary Disk
39(1)
2.4.2 Formation of Protoplanet
40(1)
2.4.3 Formation of Gas Giant Planets
41(3)
2.5 Data Processing for Extrasolar Planet Research
44(3)
2.5.1 Data Acquisition
44(1)
2.5.2 Data Management
45(1)
2.5.3 Data Analysis
46(1)
2.6 Summary
47(4)
References
48(3)
3 Information in Quantum Theory
51(18)
Andrew Whitaker
3.1 Introduction
51(1)
3.2 Quantum Information Theory
52(1)
3.3 Quantum Computation
53(3)
3.4 Quantum Cryptography
56(2)
3.5 Quantum Teleportation
58(1)
3.6 Quantum Information
59(3)
3.7 The Universe as a Quantum Computer
62(1)
3.8 Summary
63(6)
References
64(5)
Part III The World of Living Things
4 The Potential of Plants and Seeds in DNA-Based Information Storage
69(14)
Karin Fister
Iztok Fister Jr.
Jana Murovec
4.1 Introduction
69(2)
4.2 Materials and Methods
71(6)
4.2.1 DNA Basics
72(1)
4.2.2 Coding Program
73(1)
4.2.3 Code DNA Synthesis and Cloning
74(1)
4.2.4 Plant Material
74(1)
4.2.5 Plant Transformation
74(1)
4.2.6 DNA Isolation and PCR Analysis
75(1)
4.2.7 Sanger Sequencing
76(1)
4.3 Results
77(1)
4.3.1 Coding Program
77(1)
4.3.2 Storing Data in N. Benthamiana and Reading Data from the Plant
77(1)
4.4 Discussion
78(1)
4.5 Summary
79(4)
References
80(3)
5 Memory Processing in the Nervous System
83(18)
Naoyuki Sato
5.1 Introduction
83(2)
5.2 Physiological Basis of Memory
85(5)
5.2.1 Neuron: The Unit of Information Coding and Processing
85(1)
5.2.2 Synapse: The Principal Component of Memory
86(2)
5.2.3 Neural Oscillations: Dynamics for Cooperation Among Neural Populations
88(2)
5.3 Memory Classification
90(3)
5.3.1 Development: Shaping the Basic Structure of the Nervous System
91(1)
5.3.2 Short-Term and Working Memory
91(1)
5.3.3 Long-Term Memory
92(1)
5.4 The Arrival of Big Data to Neuroscience
93(3)
5.4.1 Brain Structure Data
94(1)
5.4.2 Database for Task-Related Brain Activation
95(1)
5.4.3 Database of Computational Models
95(1)
5.5 Conclusion
96(5)
References
96(5)
Part IV The World of Intelligent Machines and Finiteness
6 From Computing Machines to Learning Intelligent Machines: Chronological Development of Alan Turing's Thought on Machines
101(30)
Katsuhiko Sano
Mai Sugimoto
6.1 Introduction
101(2)
6.1.1 Related Work
102(1)
6.2 How Can We Model Effective Computation by Human Caclulator?
103(9)
6.2.1 The Entscheidungsproblem and Effective or Mechanical Procedure
103(2)
6.2.2 Turing Machine in 1936: Computing Machine
105(2)
6.2.3 Universal Computing Machine
107(1)
6.2.4 Unsolvable Problems in Turing's 1936 Paper
108(2)
6.2.5 How Did Turing Solve the Entscheidungsproblem Negatively?
110(2)
6.3 From the Universal Computing Machine to Practical Computing Machines
112(2)
6.3.1 Turing's Dissertation at Princeton: Oracle Machine
112(1)
6.3.2 Turing and Practical Computing Machines
112(2)
6.4 Three Requirements for Intelligent Behavior of Machines at Lecture to the London Mathematical Society
114(2)
6.5 Learning Process to Organize Intelligent Machinery
116(7)
6.5.1 How to Obtain Machine with Discipline and Initiative ...
117(1)
6.5.2 P-Type Machines
118(3)
6.5.3 The Scope of P-Type Machines and Beyond
121(2)
6.6 How Can We Construct an Intelligent Machine to Pass the Imitation Game?
123(5)
6.6.1 The Imitation Game
123(2)
6.6.2 Learning Process for Child Program
125(3)
6.7 Conclusion
128(3)
References
129(2)
7 Finite Information Agency
131(22)
Alfons Josef Schuster
7.1 Introduction
131(2)
7.2 Information Space Scenarios
133(7)
7.2.1 Scenario 1
133(4)
7.2.2 Scenario 2
137(1)
7.2.3 Scenario 3
138(2)
7.3 Mathematical Modeling
140(1)
7.4 Interpretations of Finite Information Spaces
141(8)
7.4.1 The General Value of the Model
141(2)
7.4.2 The Model from an Information Society and Evolutionary Point of View
143(2)
7.4.3 The Model from a Computational Point of View
145(4)
7.5 Summary
149(4)
References
149(4)
Part V The World of Networks, Clouds, and Big Data Processing
8 Distributed and Connected Information in the Internet
153(22)
Jurgen Vogel
8.1 Introduction
153(2)
8.2 Web Data
155(5)
8.2.1 Web Data Format and Web Applications
156(1)
8.2.2 Searching and Finding Data
157(2)
8.2.3 Evaluating Information Retrieval Algorithms
159(1)
8.3 Social Data
160(1)
8.4 User Data
161(2)
8.5 Text Data
163(2)
8.6 Machine Data
165(2)
8.7 Big Data
167(1)
8.8 Linked Data
168(2)
8.9 Summary
170(5)
References
170(5)
9 Custom Hardware Versus Cloud Computing in Big Data
175(22)
Gaye Lightbody
Fiona Browne
Valeriia Haberland
9.1 Introduction
175(2)
9.2 Applications
177(4)
9.2.1 Genomics and Proteomics
177(1)
9.2.2 Digital Pathology
178(1)
9.2.3 Self-Quantification
178(1)
9.2.4 Surveillance
179(1)
9.2.5 Internet-of-Things
180(1)
9.2.6 Finance
181(1)
9.3 Computational Challenges
181(1)
9.4 High-Performance Computing Solutions
182(4)
9.4.1 Graphics Processing Units (GPU) Computing
182(1)
9.4.2 Field Programmable Gate Arrays
183(1)
9.4.3 Cloud Computing Platforms
184(1)
9.4.4 Deep Learning Libraries
185(1)
9.5 The Role for Custom Hardware
186(2)
9.5.1 Deep Learning
187(1)
9.5.2 ASIC Enhanced Cloud Platforms
187(1)
9.5.3 ASIC Deep Learning Processors
188(1)
9.6 Discussion
188(9)
References
190(7)
Part VI The World of Society and Philosophy
10 Information Overload in a Data-Intensive World
197(22)
Tibor Koltay
10.1 Introduction
197(1)
10.2 Information Overload
198(5)
10.2.1 General Characteristics of Information Overload
199(1)
10.2.2 Information Overload in Business Environments
200(1)
10.2.3 Information Overload in Everyday Life Information Seeking
201(1)
10.2.4 The Role of Information Technology
202(1)
10.2.5 Information Overload in the Data-Intensive World
202(1)
10.3 Alleviating the Symptoms of Information Overload
203(9)
10.3.1 Design and Information Architecture
204(1)
10.3.2 Interacting with Information
205(7)
10.4 Discussion
212(1)
10.5 Summary
213(6)
References
213(6)
11 Causal/Informational Theories of Mental Content
219(16)
Fred Adams
11.1 Introduction
219(2)
11.2 Natural vs. Non-natural Meaning
221(1)
11.3 Isomorphism Plus Causation and Conditions of Fidelity
221(2)
11.4 Information-Based Theories
223(1)
11.5 Attack on Wisconsin Semantics
224(4)
11.5.1 Contra Stampe
225(1)
11.5.2 Contra Dretske
226(2)
11.6 Dretske's Response: Indicator Function Account
228(2)
11.7 Fodor's Asymmetrical Causal Dependency Theory of Meaning
230(1)
11.8 Conclusion
231(4)
References
232(3)
Index 235