Created to teach students many of the most important techniques used for constructing combinatorial designs, this is an ideal textbook for advanced undergraduate and graduate courses in combinatorial design theory.
Thedevelopmentoftechnologiesforhigh-throughputmeasurementofgene expression in biological system is providing powerful new tools for inv- tigating the transcriptome on a genomic scale, and across diverse biol- ical systems and experimental designs.
An essential contribution to the study of the history of computers, this work identifies the computer's impact on the physical, biological, cognitive, and medical sciences.
The Science of Secrecy from Ancient Egypt to Quantum CryptographyFrom the best-selling author of Fermat's Last Theorem, The Code Book is a history of man's urge to uncover the secrets of codes, from Egyptian puzzles to modern day computer encryptions.
The aim of the book is to cover the three fundamental aspects of research in equilibrium problems: the statement problem and its formulation using mainly variational methods, its theoretical solution by means of classical and new variational tools, the calculus of solutions and applications in concrete cases.
This book contains 28 research articles from among the 49 papers and abstracts presented at the Tenth International Conference on Fibonacci Numbers and Their Applications.
Logic Synthesis and Verification Algorithms is a textbook designed for courses on VLSI Logic Synthesis and Verification, Design Automation, CAD and advanced level discrete mathematics.
A cooperative system is defined to be multiple dynamic entities that share information or tasks to accomplish a common, though perhaps not singular, objective.
Although rigidity has been studied since the time of Lagrange (1788) and Maxwell (1864), it is only in the last twenty-five years that it has begun to find applications in the basic sciences.
Coding and Modulation for Digital Television presents a comprehensive description of all error control coding and digital modulation techniques used in Digital Television (DTV).
Graph Separators with Applications is devoted to techniques for obtaining upper and lower bounds on the sizes of graph separators - upper bounds being obtained via decomposition algorithms.
Basic Category Theory for Computer Scientists provides a straightforward presentation of the basic constructions and terminology of category theory, including limits, functors, natural transformations, adjoints, and cartesian closed categories.
An understanding of the theory and application of logic is fundamental both to successful software and hardware development, and to gain a thorough grasp of modern computing.
Stochastic Subsurface Hydrogeology is the study of subsurface, geological heterogeneity, and its effects on flow and transport process, using probabilistic and geostatistical concepts.
Mathematical modelling and simulation is an increasingly powerful area of mathematics and computer science, which in recent years has been fuelled by the unprecedented access to larger than ever stores of data.
Mathematical modelling and simulation is an increasingly powerful area of mathematics and computer science, which in recent years has been fuelled by the unprecedented access to larger than ever stores of data.
An Invitation to Computational Homotopy is an introduction to elementary algebraic topology for those with an interest in computers and computer programming.
The subject of sparse matrices has its root in such diverse fields as management science, power systems analysis, surveying, circuit theory, and structural analysis.
The interplay between computability and randomness has been an active area of research in recent years, reflected by ample funding in the USA, numerous workshops, and publications on the subject.
Great interest is now being shown in computational and mathematical neuroscience, fuelled in part by the rise in computing power, the ability to record large amounts of neurophysiological data, and advances in stochastic analysis.
The fields of computational fluid dynamics (CFD) and optimal shape design (OSD) have received considerable attention in the recent past, and are of practical importance for many engineering applications.
Great interest is now being shown in computational and mathematical neuroscience, fuelled in part by the rise in computing power, the ability to record large amounts of neurophysiological data, and advances in stochastic analysis.
The interplay between computability and randomness has been an active area of research in recent years, reflected by ample funding in the USA, numerous workshops, and publications on the subject.
The five-volume set LNCS 11536, 11537, 11538, 11539 and 11540 constitutes the proceedings of the 19th International Conference on Computational Science, ICCS 2019, held in Faro, Portugal, in June 2019.
This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020.
This book constitutes the proceedings of the 14th International Conference on Verification and Evaluation of Computer and Communication Systems, VECoS 2020, which was supposed to be held in Xi'an, China, in October 2020, but was held virtually instead.
Generating random networks efficiently and accurately is an important challenge for practical applications, and an interesting question for theoretical study.
The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications.
This monograph represents a historic breakthrough in the field of linear programming (LP)since George Dantzig first discovered the simplex method in 1947.
This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs).
Numerical Algorithmic Science and Engineering (NAS&E), or more compactly, Numerical Algorithmics, is the theoretical and empirical study and the practical implementation and application of algorithms for solving finite-dimensional problems of a numeric nature.
This book explores minimum divergence methods of statistical machine learning for estimation, regression, prediction, and so forth, in which we engage in information geometry to elucidate their intrinsic properties of the corresponding loss functions, learning algorithms, and statistical models.
Employ essential tools and functions of the MATLAB and Simulink packages, which are explained and demonstrated via interactive examples and case studies.
This book provides an overview of network science from the perspective of diverse academic fields, offering insights into the various research areas within network science.
