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Mathematics for Future Computing and Communications [Kõva köide]

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  • Formaat: Hardback, 396 pages, kõrgus x laius x paksus: 257x176x21 mm, kaal: 900 g, Worked examples or Exercises
  • Ilmumisaeg: 16-Dec-2021
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1316513580
  • ISBN-13: 9781316513583
Teised raamatud teemal:
Mathematics for Future Computing and CommunicationsSuurem pilt
  • Formaat: Hardback, 396 pages, kõrgus x laius x paksus: 257x176x21 mm, kaal: 900 g, Worked examples or Exercises
  • Ilmumisaeg: 16-Dec-2021
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1316513580
  • ISBN-13: 9781316513583
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781316513583.html
Märksõnad:
For 80 years, mathematics has driven fundamental innovation in computing and communications. This timely book provides a panorama of some recent ideas in mathematics and how they will drive continued innovation in computing, communications and AI in the coming years. It provides a unique insight into how the new techniques that are being developed can be used to provide theoretical foundations for technological progress, just as mathematics was used in earlier times by Turing, von Neumann, Shannon and others. Edited by leading researchers in the field, chapters cover the application of new mathematics in computer architecture, software verification, quantum computing, compressed sensing, networking, Bayesian inference, machine learning, reinforcement learning and many other areas.

New mathematical ideas go hand-in-hand with innovation in computing, communications and AI. Such innovations are described here in a book that provides a panorama of ideas and applications in computer architecture, software verification, quantum computing, compressed sensing, Bayesian inference, machine learning, reinforcement learning and more.

Arvustused

' In a field in which depth is often privileged, the broad spectrum of topics offered in this book makes it a welcome addition for nonspecialist readers with some background who are eager to approach the use of mathematics in computing and communications Recommended.' L. Benedicenti, Choice

Muu info

A panorama of new ideas in mathematics that are driving innovation in computing and communications.
Preface ix
Part I Computing
1(132)
Introduction to Part I
3(3)
1 Mathematics, Models and Architectures
6(48)
Bill McColl
1.1 Introduction
6(2)
1.2 Moving Beyond von Neumann
8(2)
1.3 Programming-Oriented Models
10(5)
1.4 An Algorithm-Oriented Model
15(2)
1.5 A Bridging Model
17(8)
1.6 Parallel Algorithms and Complexity
25(7)
1.7 Networks and Communications
32(3)
1.8 Resilience-Oriented Models
35(13)
1.9 New Research Directions
48(6)
2 Mathematics and Software Verification
54(20)
Chen Haibo
Gao Xin
2.1 Introduction
54(1)
2.2 Basic Theories of Formal Methods
55(5)
2.3 Spectrum of Formal Methods
60(1)
2.4 Applications of Formal Methods
61(5)
2.5 Challenges of Formal Verification in Software Systems
66(1)
2.6 Towards Well-Engineered Formal Verification
67(3)
2.7 Conclusion
70(4)
3 Mathematics for Quantum Computing
74(24)
Kong Yunchuan
3.1 Introduction
75(3)
3.2 Quantum Algorithms
78(5)
3.3 Quantum Error Correction
83(4)
3.4 Quantum Control
87(11)
4 Mathematics for Al: Categories, Toposes, Types
98(35)
Daniel Bennequin
Jean-Claude Belfiore
4.1 Introduction
99(1)
4.2 History
100(3)
4.3 Categories, Topos, Types and Stacks
103(12)
4.4 Topos of Deep Neural Networks
115(7)
4.5 Information Theories
122(2)
4.6 Higher Categories and Homotopy Types
124(2)
4.7 Categories and Toposes in Computer Science
126(7)
Part II Communications
133(92)
Introduction to Part II
135(3)
5 Mathematics and Compressed Sensing
138(15)
Zhang Ftui
Long Zichao
5.1 Introduction
138(1)
5.2 Sampling Theory and Data Recovery
139(1)
5.3 Main Theory and Breakthroughs
140(5)
5.4 Algorithms
145(2)
5.5 General Compressed Sensing
147(2)
5.6 Applications in Industry
149(1)
5.7 Open Questions
150(3)
6 Mathematics, Information Theory, and Statistical Physics
153(34)
Merouane Debbah
6.1 Mathematics of Propagation: Maximum Entropy Principle
153(12)
6.2 Mathematics of Matrices: Statistical Physics
165(10)
6.3 Mathematics of Communications: Information Theory
175(8)
6.4 Conclusion
183(4)
7 Mathematics of Data Networking
187(24)
Li Zongpeng
Miao Lihua
Tang Siyu
7.1 Introduction
187(1)
7.2 System Capacity Region
188(1)
7.3 Theory and Algorithms of Network Optimization
188(6)
7.4 The Theory of Network Coding
194(6)
7.5 Mathematics for Internet Quality of Service (QoS)
200(6)
7.6 Conclusion
206(5)
8 Mathematics and Network Science
211(14)
Sun Jie
8.1 Introduction
212(1)
8.2 Characterizations of Real Networks
213(2)
8.3 Structural Models of Complex Networks
215(3)
8.4 Community Detection and Network Partition
218(2)
8.5 Network Dynamics: Synchronization, Control, and Optimization
220(2)
8.6 Data-Driven Analysis: Causal Inference, Automated Modeling
222(1)
8.7 Conclusion
223(2)
Part III Artificial Intelligence
225(150)
Introduction to Part III
227(3)
9 Mathematics, Information and Learning
230(55)
Jong Wen
Ge Yiqun
9.1 Introduction
230(1)
9.2 Definition of Information
231(20)
9.3 Neural Network Information
251(25)
9.4 Learnability
276(7)
9.5 Conclusion
283(2)
10 Mathematics and Bayesian Inference
285(24)
Guo Kaiyang
Lv Wenlong
Zhang Jianfeng
10.1 Introduction
285(2)
10.2 Bayesian Inference
287(3)
10.3 Exact Inference in Bayesian Linear Regression
290(3)
10.4 Approximate Inference
293(7)
10.5 Distributed Inference
300(1)
10.6 Bayesian Optimization
301(3)
10.7 Bayesian Transfer Learning
304(1)
10.8 Designing a Prior
305(1)
10.9 Duality between Control and Inference
306(3)
11 Mathematics, Optimization and Machine Learning
309(20)
Jui Shang-Ling
11.1 Introduction
309(2)
11.2 Stochastic Convex Optimization
311(4)
11.3 Direct Methods for Non-Convex Optimization
315(4)
11.4 Optimization for Deep Learning
319(5)
11.5 Open Problems
324(5)
12 Mathematics of Reinforcement Learning
329(46)
Wu Shuang
Wang Jun
12.1 Introduction
329(1)
12.2 Bayesian Decision Principle
330(1)
12.3 Markov Decision Process
330(10)
12.4 Algorithmic Development
340(15)
12.5 Theoretical Foundations
355(11)
12.6 Challenges
366(9)
Part IV Future
375(9)
13 Mathematics and Prospects for Future Breakthroughs
377(7)
Dang Wenshuan
13.1 Future AI: From Perception to Cognition
377(1)
13.2 Future Discovery: From Digital Twin to Quantum Twin
378(1)
13.3 Future Unified Computing Architectures
379(1)
13.4 Future Wireless Systems
380(1)
13.5 Future IP Networks
381(1)
13.6 Future Optical Technologies
381(1)
13.7 Future Autonomous Driving Networks
382(1)
13.8 Future Mathematics: The Analytical Approach
383(1)
Editors and Contributing Authors 384
Liao Heng is Fellow of Huawei and Chief Scientist of Huawei's 2012 Laboratories and HiSilicon. He leads research and development teams working on computer architecture, processors and software platforms and he also leads the Huawei CSTT committee which is focused on fundamental theory exploration. He has more than 20 years of experience in semiconductor industry. His recent work includes storage controller architectures, SSD controllers, high performance server class CPUs, AI processors and computing theory research. Prior to joining Huawei, he was a Fellow at PMC-Sierra, Inc. He has authored over 30 patents. Bill McColl is the Director of the Future Computing Lab at Huawei's Zurich Research Center, where he leads research on architecture, software and algorithms. He is also a Fellow of Wadham College, Oxford University. Previously he was Professor of Computer Science, Head of Research in Parallel Computing and Chairman of the Faculty of Computer Science at Oxford. He established and led Oxford Parallel, a major center for research on industrial and business applications of HPC at the university. Much of his research has focused on the Bulk Synchronous Parallel (BSP) approach to parallel architecture, software and algorithms. BSP is now used throughout industry for massively parallel graph databases, graph analytics, machine learning and other areas of AI.