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Java: Data Structures and Programming
This introduction to the Java language integrates a discussion of object-oriented programming with the design and implementation of data structures. It covers the most important topics, including algorithm analysis; time and space complexities; Java built-in data structure classes; input and output, data, and access streams; and the persistency of data.
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Parallel Programming Using C++
Foreword by Bjarne Stroustrup Software is generally acknowledged to be the single greatest obstacle preventing mainstream adoption of massively-parallel computing. While sequential applications are routinely ported to platforms ranging from PCs to mainframes, most parallel programs only ever run on one type of machine. One reason for this is that most parallel programming systems have failed to insulate their users from the architectures of the machines on which they have run. Those that have been platform-independent have usually also had poor performance. Many researchers now believe that object-oriented languages may offer a solution. By hiding the architecture-specific constructs required for high performance inside platform-independent abstractions, parallel object-oriented programming systems may be able to combine the speed of massively-parallel computing with the comfort of sequential programming. Parallel Programming Using C++ describes fifteen parallel programming systems based on C++, the most popular object-oriented language of today. These systems cover the whole spectrum of parallel programming paradigms, from data parallelism through dataflow and distributed shared memory to message-passing control parallelism. For the parallel programming community, a common parallel application is discussed in each chapter, as part of the description of the system itself. By comparing the implementations of the polygon overlay problem in each system, the reader can get a better sense of their expressiveness and functionality for a common problem. For the systems community, the chapters contain a discussion of the implementation of the various compilers and runtime systems. In addition to discussing the performance of polygon overlay, several of the contributors also discuss the performance of other, more substantial, applications. For the research community, the contributors discuss the motivations for and philosophy of their systems. As well, many of the chapters include critiques that complete the research arc by pointing out possible future research directions. Finally, for the object-oriented community, there are many examples of how encapsulation, inheritance, and polymorphism can be used to control the complexity of developing, debugging, and tuning parallel software.
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Modular Calculus for the Average Cost of Data Structuring
This book introduces the first programming language for which average-case time analysis of its programs is guaranteed to be modular. The main time measure currently used for real-time languages (worst-case time) is well-known not to be modular in general, which makes average-case analysis notoriously difficult. Schellekens includes sample programs as well as derivations of the average-case time of these programs to illustrate this radically different approach.
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Computing in Cause-Effect Structures
This book focuses on numerous examples of tasks represented by c-e structure. Cause–effect (c-e) structures are dynamic objects devised for algebraic and graphic description of realistic tasks. They constitute a formal system providing means to specify or implement (depending on degree of description generality) the tasks. They can be transformed, thus come under simplification, in accordance with rules-axioms of their algebra. Also, their properties can be inferred from the axioms. One objective of this book is presentation, by many realistic examples, of computing capability of c-e structures, without entering into mathematical details of their algebra. In particular, how computing with natural numbers and in propositional calculus can be performed by c-e structures and how to specify behavior of data structures. But also demonstration of many other tasks taken from the area of parallel processing, specified as c-e structures. Another objective is modelling or simulation by means of c-e structures, of other descriptive systems, devised for tasks from various fields. Also without formalizing by usage of functions between the systems. This concerns formalisms such as reaction systems, rough sets, Petri nets and CSP-like languages. Also on such, where temporal interdependence between actions matters. The presentation of examples is prevalently graphic, in the form of peculiar nets, but accompanied by their algebraic and set-theoretic expressions. A fairly complete exposition of concepts and properties of the algebra of cause-effect structures is in the previous book appeared in the Lecture Notes in Networks and Systems series. But basic notions of c-e structures are here provided for understanding the examples.
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Beginning Java Data Structures and Algorithms
Though your application serves its purpose, it might not be a high performer. Learn techniques to accurately predict code efficiency, easily dismiss inefficient solutions, and improve the performance of your application.Key FeaturesExplains in detail different algorithms and data structures with sample problems and Java implementations where appropriateIncludes interesting tips and tricks that enable you to efficiently use algorithms and data structuresCovers over 20 topics using 15 practical activities and exercises Book DescriptionLearning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried, and tested. By knowing how these solutions work, you can ensure that you choose the right tool when you face these problems.This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications.What you will learnUnderstand some of the fundamental concepts behind key algorithmsExpress space and time complexities using Big O notation.Correctly implement classic sorting algorithms such as merge and quicksortCorrectly implement basic and complex data structuresLearn about different algorithm design paradigms, such as greedy, divide and conquer, and dynamic programmingApply powerful string matching techniques and optimize your application logicMaster graph representations and learn about different graph algorithmsWho this book is forIf you want to better understand common data structures and algorithms by following code examples in Java and improve your application efficiency, then this is the book for you. It helps to have basic knowledge of Java, mathematics and object-oriented programming techniques.
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Live Data Structures in Logic Programs
A common hazard for implementations of applicativeprogramming languages is the excessive creation of garbagecells during program execution. The available run-timegarbage collecting processes are expensive in time and oftenrequire a periodic disruption of the program execution. Thepresent book addresses the problem of memory re-use forlogic programs through program analysis rather than byrun-time garbage collection. The static analysis isconstructed as an application of abstractinterpretation forlogic programs. The starting point is a previously developedapplication of integrated type and mode analysis, whichbasically supplies a description of the logical terms towhich program variables can be bound at run time. Thecontribution of the book consists of a modular extension ofthe abstract domain and operations in order to deriverun-time properties concerning the sharing and liveness ofterm substructures dynamically created during programexecution. Alsoillustrated is how such information can beused by the compiler to improve the allocation of run-timestructures and to automatically introduce destructiveassignments in logic languages in a safe and transparentway, thereby shifting some part of the run-time storagereclamation overhead to compile time.
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Organic Structures from Spectra
The derivation of structural information from spectroscopic data is now an integral part of organic chemistry courses at all Universities. A critical part of any such course is a suitable set of problems to develop the students’ understanding of how organic structures are determined from spectra. The book builds on the very successful teaching philosophy of learning by hands-on problem solving; carefully graded examples build confidence and develop and consolidate a student’s understanding of organic spectroscopy. Organic Structures from Spectra, 6th Edition is a carefully chosen set of about 250 structural problems employing the major modern spectroscopic techniques, including Mass Spectrometry, 1D and 2D 13C and 1H NMR Spectroscopy and Infrared Spectroscopy. There are 25 problems specifically dealing with the interpretation of spin–spin coupling in proton NMR spectra and 10 problems based on the quantitative analysis of mixtures using proton and carbon NMR spectroscopy. The accompanying text is descriptive and only explains the underlying theory at a level that is sufficient to tackle the problems. The text includes condensed tables of characteristic spectral properties covering the frequently encountered functional groups. The examples themselves have been selected to include all important structural features and to emphasise connectivity arguments and stereochemistry. Many of the compounds were synthesised specifically for this book. In this collection, there are many additional easy problems designed to build confidence and to demonstrate basic principles. The Sixth Edition of this popular textbook: now incorporates many new problems using 2D NMR spectra (C–H Correlation spectroscopy, HMBC, COSY, NOESY and TOCSY);has been expanded and updated to reflect the new developments in NMR spectroscopy;has an additional 40 carefully selected basic problems;provides a set of problems dealing specifically with the quantitative analysis of mixtures using NMR spectroscopy;features proton NMR spectra obtained at 200, 400 and 600 MHz and 13C NMR spectra including routine 2D C–H correlation, HMBC spectra and DEPT spectra;contains a selection of problems in the style of the experimental section of a research paper;includes examples of fully worked solutions in the appendix;has a complete set of solutions available to instructors and teachers from the authors. Organic Structures from Spectra, Sixth Edition will prove invaluable for students of Chemistry, Pharmacy and Biochemistry taking a first course in Organic Chemistry.
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Structured Programming Approach to Data
Much of current programming practice is basically empirical and ad hoc in approach. Each problem is tackled without relation to those that have gone before; experiences are made and stored as a series of fragments. Now, under the pressure of events, this unsatisfactory state of affairs is coming to an end. Programming is becoming a technology, a theory known as structured programming is developing. The purpose of a theory is to categorise and explain existing practice, thus enabling it to be improved through the development of new and sharper techniques. The resulting experiences have then to be fed back into the theory so that the process of enrichment may continue. This dialectical relationship between theory and practice is essential to a healthy programming technology. The lack of such a relationship in the 1950s and 60s and the accompanying software crisis certainly confirm the converse of this proposition. My aim in writing this book has been to explain the current state of the theory of structured programming, so that it may be used to improve the reader's practice. The book deals with two facets of programming - how to design a program in terms of abstract data structures and how to represent the data structures on real and bounded computers. The separation between program design and data structure representation leads to more reliable and flexible programs.
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Algorithms and Data Structures in VLSI Design
One of the main problems in chip design is the enormous number of possible combinations of individual chip elements within a system, and the problem of their compatibility. The recent application of data structures, efficient algorithms, and ordered binary decision diagrams (OBDDs) has proven vital in designing the computer chips of tomorrow. This book provides an introduction to the foundations of this interdisciplinary research area, emphasizing its applications in computer aided circuit design.
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Space-Efficient Data Structures, Streams, and Algorithms
This Festschrift volume, published in honour of J. Ian Munro, contains contributions written by some of his colleagues, former students, and friends. In celebration of his 66th birthday the colloquium "Conference on Space Efficient Data Structures, Streams and Algorithms" was held in Waterloo, ON, Canada, during August 15-16, 2013. The articles presented herein cover some of the main topics of Ian's research interests. Together they give a good overall perspective of the last 40 years of research in algorithms and data structures.
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Algorithms and Data Structures
This is a central topic in any computer science curriculum. To distinguish this textbook from others, the author considers probabilistic methods as being fundamental for the construction of simple and efficient algorithms, and in each chapter at least one problem is solved using a randomized algorithm. Data structures are discussed to the extent needed for the implementation of the algorithms. The specific algorithms examined were chosen because of their wide field of application.This book originates from lectures for undergraduate and graduate students. The text assumes experience in programming algorithms, especially with elementary data structures such as chained lists, queues, and stacks. It also assumes familiarity with mathematical methods, although the author summarizes some basic notations and results from probability theory and related mathematical terminology in the appendices. He includes many examples to explain the individual steps of the algorithms, and he concludes each chapter with numerous exercises.
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VLSI Architecture for Concurrent Data Structures
Concurrent data structures simplify the development of concurrent programs by encapsulating commonly used mechanisms for synchronization and commu nication into data structures. This thesis develops a notation for describing concurrent data structures, presents examples of concurrent data structures, and describes an architecture to support concurrent data structures. Concurrent Smalltalk (CST), a derivative of Smalltalk-80 with extensions for concurrency, is developed to describe concurrent data structures. CST allows the programmer to specify objects that are distributed over the nodes of a concurrent computer. These distributed objects have many constituent objects and thus can process many messages simultaneously. They are the foundation upon which concurrent data structures are built. The balanced cube is a concurrent data structure for ordered sets. The set is distributed by a balanced recursive partition that maps to the subcubes of a binary 7lrcube using a Gray code. A search algorithm, VW search, based on the distance properties of the Gray code, searches a balanced cube in O(log N) time. Because it does not have the root bottleneck that limits all tree-based data structures to 0(1) concurrency, the balanced cube achieves 0C.:N) con currency. Considering graphs as concurrent data structures, graph algorithms are pre sented for the shortest path problem, the max-flow problem, and graph parti tioning. These algorithms introduce new synchronization techniques to achieve better performance than existing algorithms.
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Probabilistic Data Structures for Blockchain-Based Internet of Things Applications
This book covers theory and practical knowledge of Probabilistic data structures (PDS) and Blockchain (BC) concepts. It introduces the applicability of PDS in BC to technology practitioners and explains each PDS through code snippets and illustrative examples. Further, it provides references for the applications of PDS to BC along with implementation codes in python language for various PDS so that the readers can gain confidence using hands on experience. Organized into five sections, the book covers IoT technology, fundamental concepts of BC, PDS and algorithms used to estimate membership query, cardinality, similarity and frequency, usage of PDS in BC based IoT and so forth.
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Manufacturing Data Structures
Manufacturing Data Structures "Comprehensive yeteasy-to-read.Manufacturing Data Structures is filled withanecdotes, yet stresses the importance of maintaining dataaccuracy. It is valuable reading for all manufacturing managers."Jim Carnall Manufacturing Manager, Eastman Kodak "An entertainingand informative look at an important aspect of day to day businessin the MRP II environment. It clearly shows how data structuringmethodology can be directly applied to process industries such asthe Personal Products/Health and Beauty business." Jeff L. StevensManager, Packaging Sciences, Chesebrough-Ponds Canada"Manufacturing Data Structures shows, in a very practical way, howmanufacturing data can be used as a competitive weapon. It's acomprehensive guide, filled with solutions to everyday problems."Jim Hendrickson Plant Manager, Reckitt & Colman "An excellentbook.Very useful on the subject of data foundations formanufacturing. It has suggested further opportunities forimprovement in my own organisation." R.A. Watson Rolls-Royce MotorCars "Manufacturing Data Structures will be of immense value to thepractitioner." Chris Cage ICI Pharmaceuticals
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Implicit Curves and Surfaces: Mathematics, Data Structures and Algorithms
Implicit objects have gained increasing importance in geometric modeling, visualisation, animation, and computer graphics, because their geometric properties provide a good alternative to traditional parametric objects. This book presents the mathematics, computational methods and data structures, as well as the algorithms needed to render implicit curves and surfaces, and shows how implicit objects can easily describe smooth, intricate, and articulatable shapes, and hence why they are being increasingly used in graphical applications. Divided into two parts, the first introduces the mathematics of implicit curves and surfaces, as well as the data structures suited to store their sampled or discrete approximations, and the second deals with different computational methods for sampling implicit curves and surfaces, with particular reference to how these are applied to functions in 2D and 3D spaces.
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Practical Data Structures and Algorithms for Java Developers
Master data structures and algorithms in Java through a practical, engineering-focused approach that goes beyond theory. With modern examples and a structured approach, this book emphasizes not only how algorithms work, but how to choose, adapt, and apply them in real-world systems.To start, you will conquer fundamental concepts such as arrays and linked lists. From there, the book progresses through essential sorting and searching techniques such as QuickSort and Binary Search. Then, you will explore the implementation and use cases of stacks and queues, followed by tree and graph structures with traversal algorithms. The book ends with you tackling advanced topics, such as dynamic programming, tries, and advanced graph algorithms.Finally, you will be ready to put your skills to the test and apply your knowledge in real-world situations for performance optimization.What You Will Learn: Implement and optimize essential data structures in JavaSolve complex algorithmic problems with confidenceMaster best practices for writing efficient, scalable Java code for enterprise applicationsWho This Book Is For: Java developers with a basic understanding of programming concepts who want to deepen their knowledge of data structures and algorithms. Secondary audiences include students, competitive programmers, and software engineers preparing for technical interviews.
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Advanced Applications of Python Data Structures and Algorithms
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision.Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
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Longitudinal Data Analysis
By looking at the processes of change over time - by carrying out longitudinal studies - researchers answer questions about learning, development, educational growth, social change and medical outcomes.However, longitudinal research has many faces. This book examines all the main approaches as well as newer developments (such as structural equation modelling, multilevel modelling and optimal scaling) to enable the reader to gain a thorough understanding of the approach and make appropriate decisions about which technique can be applied to the research problem. Conceptual explanations are used to keep technical terms to a minimum; examples are provided for each approach; issues of design, measurement and significance are considered; and a standard notation is used throughout.
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Modern Data Mining Algorithms in C++ and CUDA C
Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You’ll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are:Forward selection component analysisLocal feature selectionLinking features and a target with a hidden Markov modelImprovements on traditional stepwise selectionNominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will LearnCombine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set.Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods.Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts.
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