The aim of this text is to introduce the beginner to the theory of Riordan arrays. Starting in a simple and constructive manner, the basic structure of these arrays is explained with clear examples, before a more theoretical grounding is provided. Ordinary Riordan arrays and exponential Riordan arrays are examined, with many explicit examples, and their applications to combinatorics and other areas are explored. The production matrix of a Riordan array is shown to play a key role, along with various sequence characterizations. Formal prerequisites are kept to a minimum, in order to provide a gentle introduction to this exciting area, that involves linear algebra, group theory and combinatorics. The reader will be well positioned to further explore Riordan arrays and their applications, and to undertake their own projects. They will join a community of interested mathematicians that now spans all continents, in a growing area of research and application.
Adapted from a series of lectures given by the authors, this monograph focuses on radial basis functions (RBFs), a powerful numerical methodology for solving PDEs to high accuracy in any number of dimensions. This method applies to problems across a wide range of PDEs arising in fluid mechanics, wave motions, astro- and geosciences, mathematical biology, and other areas and has lately been shown to compete successfully against the very best previous approaches on some large benchmark problems. Using examples and heuristic explanations to create a practical and intuitive perspective, the authors address how, when, and why RBF-based methods work. The authors trace the algorithmic evolution of RBFs, starting with brief introductions to finite difference (FD) and pseudospectral (PS) methods and following a logical progression to global RBFs and then to RBF-generated FD (RBF-FD) methods. The RBF-FD method, conceived in 2000, has proven to be a leading candidate for numerical simulations in an increasingly wide range of applications, including seismic exploration for oil and gas, weather and climate modeling, and electromagnetics, among others. This is the first survey in book format of the RBF-FD methodology and is suitable as the text for a one-semester first-year graduate class.
R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R.
In the past decade, molecular biology has been transformed from the art of cloning a single gene to a statistical science measuring and calculating properties of entire genomes. New high-throughput methods have been developed for genome sequencing and studying the cell at different systematic levels such as transcriptome, proteome, metabolome and other -omes. At the heart of most high-throughput methods is the technique of polymerase chain reaction (PCR). PCR Primer Design focuses on primer design, which is critical to both the efficiency and the accuracy of the PCR. With intricate descriptions of basic approaches as well as specialized methods, this volume is an exceptional reference for all those involved in studying the genome. In PCR Primer Design, authors describe basic approaches for PCR primer design in addition to specialized methods. These state-of-the-art methods can be used for both genome-scale experiments and for small-scale individual PCR amplifications. This volume will be useful for organizations performing whole genome studies, companies designing instruments that utilize PCR, and individual scientists ??? geneticists, molecular biologists, molecular geneticists, and more ??? who routinely use PCR in their research.
This book brings together most of the information available concerning two species that diverged 2-3 million years ago. The objective was to try to understand why two sibling species so similar in several characteristics can be so different in others. To this end, it was crucial to confront all data from their ecology and biogeography with their behavior and DNA polymorphism. Drosophila melanogaster and Drosophila simulans are among the two sibling species for which a large set of data is available. In this book, ecologists, physiologists, geneticists, behaviorists share their data on the two sibling species, and several scenarios of evolution are put forward to explain their similarities and divergences. This is the first collection of essays of its kind. It is not the final point of the analyses of these two species since several areas remain obscure. However, the recent publication of the complete genome of D. melanogaster opens new fields for research. This will probably help us explain why D. melanogaster and D. simulans are sibling species but false friends.
This volume contains contributions from participants in the 2007 International Multiconference of Engineers and Computer Scientists. It covers a variety of subjects in the frontiers of intelligent systems and computer engineering and their industrial applications. The book offers up-to-date information on advances in intelligent systems and computer engineering and also serves as an excellent reference work for researchers and graduate students working in the field.
John Earman,Professor of the History and Philosophy of Science John Earman
This primer presents a systematic introduction to the structure of Modern Standard Hindi. It is intended to provide the student with a thorough foundation in the grammatical structure of that variety of Hindi that is commonly taught in Indian schools and that is the common vehicle of publication in Hindi. Although much emphasis is placed on the written language, discussion is also provided of aspects of conversational Hindi. The core of the work contains thirty on chapters. The first four offer discussions of the linguistic status of Hindi as well as comprehensive descriptions of Hindi phonetics and the Devanagari syllabary in which Hindi is written. Chapters 5 through 31 each contains descriptions of fundamental aspects of Hindi grammar. These chapters have extensive translation and grammatical exercises appended to them. The work as a whole introduces a core vocabulary of approximately fifteen hundred entries, incorporating lexical items found on most standard elementary word lists for the language. Supplemental materials in this book include graded reading passages, a guide to further study in Hindi, and Hindi English glossary. Although the Devanagari syllabary is used throughout the book, Roman transliteration is also provided through Chapter 15. A Primer of Modern Standard Hindi can be used in several different ways. It can be used as part of a university-level course as a text for Hindi grammar and writing. As such it will nicely supplement other materials addressing more conversational aspects of the language. It can also be used for self-study purposes by the student who does not have access to a formal instructional program.
Linear Spaces, Topological Spaces, Metric Spaces, Normed Spaces, and Topological Groups
Author: Carlo Alabiso,Ittay Weiss
This book is an introduction to the theory of Hilbert space, a fundamental tool for non-relativistic quantum mechanics. Linear, topological, metric, and normed spaces are all addressed in detail, in a rigorous but reader-friendly fashion. The rationale for an introduction to the theory of Hilbert space, rather than a detailed study of Hilbert space theory itself, resides in the very high mathematical difficulty of even the simplest physical case. Within an ordinary graduate course in physics there is insufficient time to cover the theory of Hilbert spaces and operators, as well as distribution theory, with sufficient mathematical rigor. Compromises must be found between full rigor and practical use of the instruments. The book is based on the author's lessons on functional analysis for graduate students in physics. It will equip the reader to approach Hilbert space and, subsequently, rigged Hilbert space, with a more practical attitude. With respect to the original lectures, the mathematical flavor in all subjects has been enriched. Moreover, a brief introduction to topological groups has been added in addition to exercises and solved problems throughout the text. With these improvements, the book can be used in upper undergraduate and lower graduate courses, both in Physics and in Mathematics.
Gene sequence data is the most abundant type of data available, and if you're interested in analyzing it, you'll find a wealth of computational methods and tools to help you. In fact, finding the data is not the challenge at all; rather it is dealing with the plethora of flat file formats used to process the sequence entries and trying to remember what their specific field codes mean. If you survive by surrounding yourself with well-thumbed hard copies of readme files or remembering exactly where to look for the details when you need them, then Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases is for you. This book is a handy resource, as well as an invaluable reference, for anyone who needs to know about the practical aspects and mechanics of sequence analysis. Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases pulls together all of the vital information about the most commonly used databases, analytical tools, and tables used in sequence analysis. The book is partitioned into three fundamental areas to help you maximize your use of the content. The first section, "Databases" contains examples of flatfiles from key databases (GenBank, EMBL, SWISS-PROT), the definitions of the codes or fields used in each database, and the sequence feature types/terms and qualifiers for the nucleotide and protein databases. The second section, "Tools" provides the command line syntax for popular applications such as ReadSeq, MEME/MAST, BLAST, ClustalW, and the EMBOSS suite of analytical tools. The third section, "Appendixes" concentrates on information essential to understanding the individual components that make up a biological sequence. The tables in this section include nucleotide and protein codes, genetic codes, as well as other relevant information. Written in O'Reilly's enormously popular, straightforward "Nutshell" format, this book draws together essential information for bioinformaticians in industry and academia, as well as for students. If sequence analysis is part of your daily life, you'll want this easy-to-use book on your desk.
Author: Lynn B. Jorde,John C. Carey,Michael J. Bamshad
Publisher: Elsevier Health Sciences
Medical Genetics is the clearest and most concise text on the subject, providing state-of-the-art coverage of clinically relevant molecular genetics. Lynn B. Jorde, PhD; John C. Carey, MD; and Michael J. Bamshad, MD integrate recent developments with clinical practice and emphasize the central principles of genetics and their clinical applications. Now in full color, this edition provides you with the stunning visual clarity so important in this field. Get the very latest on hot topics like gene identification, cancer genetics, gene testing and gene therapy, common diseases, ethical and social issues, personalized medicine, and much more. This is an indispensable resource that should be on every reading list. This title includes additional digital media when purchased in print format. For this digital book edition, media content is not included. . Features mini-summaries, study questions, suggested reading, and a detailed glossary to supplement and reinforce what you learn from the text. Demonstrates clinical relevance through over 230 photographs, illustrations, and tables, along with boxes containing patient/family vignettes. Enhances the visual impact of the material with full-color illustrations throughout the text for easier and more effective learning and retention. Presents a new chapter on genomics and personalized medicine for the latest on these hot topics. Provides you with the latest knowledge and research on gene identification, cancer genetics, gene testing and gene therapy, common disorders, ethical and social issues, and much more so you can keep up with current developments in genetics. Includes study questions at the end of every chapter so you can test yourself and retain the material. Features additional clinically commentary boxes throughout the text to show the relevance of genetics to everyday patient problems to prepare you for problem-based integrated courses.
Since the 1970’s, microprocessor-based digital platforms have been riding Moore’s law, allowing for doubling of density for the same area roughly every two years. However, whereas microprocessor fabrication has focused on increasing instruction execution rate, memory fabrication technologies have focused primarily on an increase in capacity with negligible increase in speed. This divergent trend in performance between the processors and memory has led to a phenomenon referred to as the “Memory Wall.” To overcome the memory wall, designers have resorted to a hierarchy of cache memory levels, which rely on the principal of memory access locality to reduce the observed memory access time and the performance gap between processors and memory. Unfortunately, important workload classes exhibit adverse memory access patterns that baffle the simple policies built into modern cache hierarchies to move instructions and data across cache levels. As such, processors often spend much time idling upon a demand fetch of memory blocks that miss in higher cache levels. Prefetching—predicting future memory accesses and issuing requests for the corresponding memory blocks in advance of explicit accesses—is an effective approach to hide memory access latency. There have been a myriad of proposed prefetching techniques, and nearly every modern processor includes some hardware prefetching mechanisms targeting simple and regular memory access patterns. This primer offers an overview of the various classes of hardware prefetchers for instructions and data proposed in the research literature, and presents examples of techniques incorporated into modern microprocessors.
Theaimofthisbookistoteachcomputerprogrammingusingexamples from mathematics and the natural sciences. We have chosen to use the Python programming language because it combines remarkable power with very clean, simple, and compact syntax. Python is easy to learn and very well suited for an introduction to computer programming. Python is also quite similar to Matlab and a good language for doing mathematical computing. It is easy to combine Python with compiled languages, like Fortran, C, and C++, which are widely used languages forscienti?ccomputations.AseamlessintegrationofPythonwithJava is o?ered by a special version of Python called Jython. The examples in this book integrate programming with appli- tions to mathematics, physics, biology, and ?nance. The reader is - pected to have knowledge of basic one-variable calculus as taught in mathematics-intensive programs in high schools. It is certainly an - vantage to take a university calculus course in parallel, preferably c- taining both classical and numerical aspects of calculus. Although not strictly required, a background in high school physics makes many of the examples more meaningful.
Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why one should consider diving into more detailed treatments of computer-modeling and latent-variable techniques, such as non-recursive path analysis, confirmatory factor analysis, and hierarchical linear modeling. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis.