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Machine and Deep Learning Using MATLAB
MACHINE AND DEEP LEARNING In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues. Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning)Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response)Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing stepsRetraining and creation for image labeling, object identification, regression classification, and text recognition Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.
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Fundamentals of Stream Processing
Stream processing is a novel distributed computing paradigm that supports the gathering, processing and analysis of high-volume, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field. This book introduces the key components of the stream computing paradigm, including the distributed system infrastructure, the programming model, design patterns and streaming analytics. The explanation of the underlying theoretical principles, illustrative examples and implementations using the IBM InfoSphere Streams SPL language and real-world case studies provide students and practitioners with a comprehensive understanding of such applications and the middleware that supports them.
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Algebraic Approach to Data Processing
The book explores a new general approach to selecting—and designing—data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms.The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique—or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.
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Foundations of Software Science and Computation Structures
This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems.
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Research Issues in Structured and Semistructured Database Programming
The Seventh International Workshop on Database Programming Languages (DBPL99) took place in Kinloch Rannoch,Perthshire,UK from the 1st to the 3rd of September 1999. This series of workshops focuses on the interaction of theory and practice in the design and development of database programming languages. The workshop has occurred biennially since 1987,and was previously held in: Rosco?,Finist'ere,France (1987) Salishan,Oregon,USA (1989) Nafplion,Argolida,Greece (1991) Manhattan,New York,USA (1993) Gubbio,Umbria,Italy (1995) Estes Park,Colorado,USA (1997) The workshop,as always,was organised as a mixture of invited speakers, informal paper presentations and discussion. Attendance at the workshop was limited to those who submitted papers and members of the Programme C- mittee,to ensure a su?ciently small forum for useful discussion. Before ?nding their way into this volume,papers were refereed by at least three members of the Programme Committee. Sixteen of the 31 submitted papers were accepted for presentation at the workshop.In the tradition of the series,authors were - couraged to improve their papers based on both referees' comments and ensuing discussion at the workshop,and resubmit them for publication in this volume, after which a further stage of refereeing took place. The result,we believe,is a volume of high-quality and well-polished papers. Two invited presentations were given,by Luca Cardelli (Microsoft Research Labs,Cambridge,UK) and Alon Levy (University of Washington).
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Fitting Equations to Data
Helps any serious data analyst with a computer to recognize the strengths and limitations of data, to test the assumptions implicit in the least squares methods used to fit the data, to select appropriate forms of the variables, to judge which combinations of variables are most influential, and to state the conditions under which the fitted equations are applicable. This edition includes numerous extensions and new devices such as component and component-plus-residual plots, cross verification with a second sample, and an index of required x-precision; also, the search for better subset equations is enlarged to cover 262,144 alternatives. The methods described have been applied in agricultural, environmental, management, marketing, medical, physical, and social sciences. Mathematics is kept to the level of college algebra.
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Algorithms and Data Structures
This book constitutes the refereed proceedings of the 17th International Symposium on Algorithms and Data Structures, WADS 2021, held in virtually in August 2021. The 47 full papers, presented together with two invited lectures, were carefully reviewed and selected from a total of 123 submissions. They present original research on the theory, design and application of algorithms and data structures.
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Mining of Data with Complex Structures
Mining of Data with Complex Structures:- Clarifies the type and nature of data with complex structure including sequences, trees and graphs- Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining.- Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints.- Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.)- Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees.- Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees.- Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach.- Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies.- Details the extension of the TMG framework for sequence mining- Provides an overview of the future research direction with respect to technical extensions and application areasThe primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry. In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains.
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Shell and Spatial Structures: Computational Aspects
In recent years powerful engineering workstations for a reasonable price become a valuable tool for the design of complicated constructions such as shell and spatial structures. This availability causes an increasing use of advanced numerical techniques for the static and dynamic analysis of these structures, also in the non-linear range. The I.A.S.S. Working Group nO 13 concerned with "Numerical Methods in Shell and Spatial Structures" and the Department of Civil Engineering of the Katholieke Universiteit Leuven have taken the initiative to organise an International Symposium, providing a forum for discussion and exchange of views between researchers, specialists in numerical analysis on one hand and designers, practising engineer ings on the other hand. These Proceedings contain the papers presented at the Symposium, held in Leuven, July 14-16 1986. The papers are organised in five sections 1. Shell structures 2. Spatial structures 3. Dynamic analysis 4. Non-linear analysis 5. Presentation and interpretation of results The papers covering more than one domain are classified following the main subject. We hope that researchers as well as practising engineers will find a lot of useful information in the book.
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Structure Determination from Powder Diffraction Data
The art of solving a structure from powder diffraction data has developed rapidly over the last ten years to the point where numerous crystal structures, both organic and inorganic, have been solved directly from powder data. However, it is still an art and, in contrast to its single crystal equivalent, is far from routine. The art lies not only in the correct application of a specific experimental technique or computer program, but also in the selection of theoptimal path for the problem at hand. Written and edited by experts active in the field, and covering both the fundamental and applied aspects of structure solution from powder diffraction data, this book guides both novices and experienced practitioners alike through the maze ofpossibilities.
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Computer Architectures for Spatially Distributed Data
These are the proceedings of a NATO Advanced Study Institute (ASI) held in Cetraro, Italy during 6-17 June 1983. The title of the ASI was Computer Arehiteetures for SpatiaZZy vistributed Vata, and it brouqht together some 60 participants from Europe and America. Presented ~ere are 21 of the lectures that were delivered. The articles cover a wide spectrum of topics related to computer architecture s specially oriented toward the fast processing of spatial data, and represent an excellent review of the state-of-the-art of this topic. For more than 20 years now researchers in pattern recognition, image processing, meteorology, remote sensing, and computer engineering have been looking toward new forms of computer architectures to speed the processing of data from two- and three-dimensional processes. The work can be said to have commenced with the landmark article by Steve Unger in 1958, and it received a strong forward push with the development of the ILIAC III and IV computers at the University of Illinois during the 1960's. One clear obstacle faced by the computer designers in those days was the limitation of the state-of-the-art of hardware, when the only switching devices available to them were discrete transistors. As aresult parallel processing was generally considered to be imprae tieal, and relatively little progress was made.
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Foundations of Software Science and Computational Structures
ETAPS 2010 was the 13th instance of the European Joint Conferences on T- oryand Practiceof Software. ETAPS is anannual federatedconference that was establishedin1998bycombininganumberofexistingandnewconferences. This yearitcomprisedtheusual?vesisterconferences(CC,ESOP,FASE,FOSSACS, TACAS), 19 satellite workshops (ACCAT, ARSPA-WITS, Bytecode, CMCS, COCV, DCC, DICE, FBTC, FESCA, FOSS-AMA, GaLoP, GT-VMT, LDTA, MBT, PLACES, QAPL, SafeCert, WGT, and WRLA) and seven invited l- tures (excluding those that were speci?c to the satellite events). The ?ve main conferences this year received 497 submissions (including 31 tool demonstration papers), 130 of which were accepted (10 tool demos), giving an overall acc- tance rate of 26%, with most of the conferences at around 24%. Congratulations thereforetoalltheauthorswhomadeittothe?nalprogramme!Ihopethatmost of the other authors will still have found a way of participating in this exciting event, and that you will all continue submitting to ETAPS and contributing to make of it the best conference on software science and engineering. The events that comprise ETAPS address various aspects of the system - velopment process, including speci?cation, design, implementation, analysis and improvement.The languages, methodologies and tools which support these - tivities are all well within its scope. Di?erent blends of theory and practice are represented,withaninclinationtowardtheorywithapracticalmotivationonthe one hand and soundly based practice on the other. Many of the issues involved in software design apply to systems in general, including hardware systems, and the emphasis on software is not intended to be exclusive. ETAPS is a confederation in which each event retains its own identity, with a separate Programme Committee and proceedings.
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Principles of Big Graph: In-depth Insight
Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph.
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Advances in Non-destructive Evaluation
This book comprises the proceedings of the Conference and Exhibition on Non Destructive Evaluation, (NDE 2019). The contents of the book encompass a vast spectrum from Conventional to Advanced NDE including novel methods, instrumentation, sensors, procedures and data analytics as applied to all industry segments for quality control, periodic maintenance, life estimation, structural integrity and related areas. This book will be a useful reference for students, researchers and practitioners.
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Dependence Models via Hierarchical Structures
Bringing together years of research into one useful resource, this text empowers the reader to creatively construct their own dependence models. Intended for senior undergraduate and postgraduate students, it takes a step-by-step look at the construction of specific dependence models, including exchangeable, Markov, moving average and, in general, spatio-temporal models. All constructions maintain a desired property of pre-specifying the marginal distribution and keeping it invariant. They do not separate the dependence from the marginals and the mechanisms followed to induce dependence are so general that they can be applied to a very large class of parametric distributions. All the constructions are based on appropriate definitions of three building blocks: prior distribution, likelihood function and posterior distribution, in a Bayesian analysis context. All results are illustrated with examples and graphical representations. Applications with data and code are interspersed throughout the book, covering fields including insurance and epidemiology.
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SymbolicC++:An Introduction to Computer Algebra using Object-Oriented Programming
Symbolic C++: An Introduction to Computer Algebra Using Object-Oriented Programming provides a concise introduction to C++ and object-oriented programming, using a step-by-step construction of a new object-oriented designed computer algebra system - Symbolic C++. It shows how object-oriented programming can be used to implement a symbolic algebra system and how this can then be applied to different areas in mathematics and physics. This second revised edition:- * Explains the new powerful classes that have been added to Symbolic C++. * Includes the Standard Template Library. * Extends the Java section. * Contains useful classes in scientific computation. * Contains extended coverage of Maple, Mathematica, Reduce and MuPAD.
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Programming Rust
Rust combines low-level control with memory safety, making it an ideal choice for systems programming. This practical guide is for experienced programmers looking to write fast, reliable code while avoiding common pitfalls like data races and buffer overflows. Authors Jim Blandy, Jason Orendorff, and Leonora Tindall explain Rust's unique approach to ownership, borrowing, and concurrency, helping you build safe, high-performance applications.Fully updated for Rust's 2024 edition, this book introduces the latest language advancements while covering essential topics like generics, async programming, and C interoperability. Whether you're working on low-latency systems, embedded software, or multithreaded applications, this edition equips you with the knowledge to write efficient, production-ready Rust code.Learn Rust's core principles, including ownership, borrowing, and lifetimesWrite safe, efficient concurrent code without data racesUse powerful features like macros, closures, and async programmingIntegrate Rust with C and C++ through FFI and unsafe codeApply best practices to write performance-critical, memory-safe applications
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Data Science in Engineering, Volume 9
Data Science and Engineering Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the ninth volume of nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:Data Science in Engineering ApplicationsEngineering MathematicsComputational Methods in Engineering
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New Data Structures and Algorithms for Logic Synthesis and Verification
This book introduces new logic primitives for electronic design automation tools. The author approaches fundamental EDA problems from a different, unconventional perspective, in order to demonstrate the key role of rethinking EDA solutions in overcoming technological limitations of present and future technologies. The author discusses techniques that improve the efficiency of logic representation, manipulation and optimization tasks by taking advantage of majority and biconditional logic primitives. Readers will be enabled to accelerate formal methods by studying core properties of logic circuits and developing new frameworks for logic reasoning engines.
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Conceptual Structures: Theory and Implementation
This volume contains revised and expanded versions of paperspresented at the Seventh Annual Workshop on ConceptualGraphs, held at New Mexico State University in Las Cruces,and sponsored by the American Association for ArtificialIntelligence and the NMSU Computer Science Department. Thecontents of the volume fall in the areas of representationissues, reasoning, data modeling and databases, algorithmsand tools, and applications and natural language. One of thehighlights reported in the volume is the landmark meetingof the first PEIRCE Project Workshop. The PEIRCE Projectaims to build a state-of-the-art, industrial strengthconceptual graphs workbench.
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Algorithms and Data Structures
This volume presents the proceedings of the Second Workshop on Algorithms and Data Structures (WADS '91), held at Carleton University in Ottawa. The workshop was organized by the School of Computer Science at Carleton University. The workshop alternates with the Scandinavian Workshop on Algorithm Theory (SWAT), continuing the tradition of SWAT '88 (LNCS, Vol. 318), WADS '89 (LNCS, Vol. 382), and SWAT '90 (LNCS, Vol. 447). From 107 papers submitted, 37 were selected for presentation at the workshop. In addition, there were 5 invited presentations.
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Conceptual Structures: Standards and Practices
With all of the news about the Internet and the Y2K problem, it is easy to forget that other areas of computer science still exist. Reading the newspaper or watching the television conveys a very warped view of what is happening in computer science. This conference illustrates how a maturing subdiscipline of computer science can continue to grow and integrate within it both old and new approaches despite (or perhaps due to) a lack of public awareness. The conceptual graph community has basically existed since the 1984 publication of John Sowa's book, "Conceptual Structures: Information Processing In Mind and Machine." In this book, John Sowa laid the foundations for a knowledge representation model called conceptual graphs based on semantic networks and the existential graphs of C.S. Peirce. Conceptual graphs constitutes a very powerful and expressive knowledge representation scheme, inheriting the benefits of logic and the mathematics of graphs. The expressiveness and formal underpinnings of conceptual graph theory have attracted a large international community of researchers and scholars. The International Conferences on Conceptual Structures, and this is the seventh in the series, is the primary forum for these researchers to report their progress and activities. As in the past, the doors were open to admit alternate representation models and approaches.
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Multidimensional Analytical Techniques in Environmental Research
Multidimensional Analytical Techniques in Environmental Research is a comprehensive resource on the many multidimensional analytical strategies to qualitatively and quantitatively assess and map the organic and inorganic pollutants in complex atmospheric, water and soil matrices. During the past two decades, the rapidly-evolving field of analytical instrumentation has produced sophisticated multidimensional tools capable of providing unique and in-depth knowledge on the chemical features of complex mixtures from these different environmental matrices. This book brings together the wealth of information in the current literature, assisting in the decision-making process by covering both the fundamentals and applications of these methodologies. Sections cover the wide variety of multidimensional analytical techniques, including multidimensional solution- and solid-state nuclear magnetic resonance (NMR) spectroscopy, ultrahigh-resolution mass spectrometry (MS), two-dimensional correlation spectroscopy, two-dimensional liquid and gas chromatography and capillary electrophoresis coupled to high-resolution detection techniques, and excitation-emission (EEM) fluorescence spectroscopy assisted by multiway data analysis tools, and the use of synchrotron-radiation-based techniques combined with other spectroscopic approaches to explore and map the speciation of elements.
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Foundations of Software Science and Computational Structures
This book constitutes the refereed proceedings of the 9th International Conference on Foundations of Software Science and Computation Structures, FOSSACS 2006, held in Vienna, Austria in March 2006 as part of ETAPS.The 28 revised full papers presented together with one invited paper were carefully reviewed and selected from 107 submissions. The papers are organized in topical sections.
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New Structures for Physics
This volume provides a series of tutorials on mathematical structures which recently have gained prominence in physics, ranging from quantum foundations, via quantum information, to quantum gravity. These include the theory of monoidal categories and corresponding graphical calculi, Girard’s linear logic, Scott domains, lambda calculus and corresponding logics for typing, topos theory, and more general process structures. Most of these structures are very prominent in computer science; the chapters here are tailored towards an audience of physicists.
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Dynamic Mixed Models for Familial Longitudinal Data
This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalizedestimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up.The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents theillustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to thesociety including his special services for society’s annual meetings.
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Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R
Emphasizing the search for patterns within and between biological sequences, trees, and graphs, Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic, and interactomic data. It implements the algorithms in Perl and R, two widely used scripting languages in computational biology. The book provides a well-rounded explanation of traditional issues as well as an up-to-date account of more recent developments, such as graph similarity and search. It is organized around the specific algorithmic problems that arise when dealing with structures that are commonly found in computational biology, including biological sequences, trees, and graphs. For each of these structures, the author makes a clear distinction between problems that arise in the analysis of one structure and in the comparative analysis of two or more structures. He also presents phylogenetic trees and networks as examples of trees and graphs in computational biology.This book supplies a comprehensive view of the whole field of combinatorial pattern matching from a computational biology perspective. Along with thorough discussions of each biological problem, it includes detailed algorithmic solutions in pseudo-code, full Perl and R implementation, and pointers to other software, such as those on CPAN and CRAN.
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Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering
This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages:Clustering a set of descriptive attributesClustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included.Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.
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Mining of Data with Complex Structures
Mining of Data with Complex Structures:- Clarifies the type and nature of data with complex structure including sequences, trees and graphs- Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining.- Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints.- Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.)- Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees.- Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees.- Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach.- Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies.- Details the extension of the TMG framework for sequence mining- Provides an overview of the future research direction with respect to technical extensions and application areasThe primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry. In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains.
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Structural Health Monitoring Based on Data Science Techniques
The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.
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Advances in Intelligent Data Analysis
Formanyyearstheintersectionofcomputing anddataanalysiscontainedme- based statistics packages and not much else. Recently, statisticians have - braced computing, computer scientists have started using statistical theories and methods, and researchers in all corners have invented algorithms to nd structure in vast online datasets. Data analysts now have access to tools for exploratory data analysis, decision tree induction, causal induction, function - timation,constructingcustomizedreferencedistributions,andvisualization,and thereareintelligentassistantsto adviseonmatters ofdesignandanalysis.There aretoolsfortraditional,relativelysmallsamples,andalsoforenormousdatasets. In all, the scope for probing data in new and penetrating ways has never been so exciting. The IDA-99 conference brings together a wide variety of researchers c- cerned with extracting knowledge from data, including people from statistics, machine learning, neural networks, computer science, pattern recognition, da- base management, and other areas.The strategiesadopted by people from these areas are often di erent, and a synergy results if this is recognized.The IDA series of conferences is intended to stimulate interaction between these di erent areas,sothatmorepowerfultoolsemergeforextractingknowledgefromdataand a better understanding is developed of the process of intelligent data analysis. The result is a conference that has a clear focus (one application area:intelligent data analysis) and a broad scope (many di erent methods and techniques).
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Organic Structures from 2D NMR Spectra
The derivation of structural information from spectroscopic data is now an integral part of organic chemistry courses at all Universities. Over recent years, a number of powerful two-dimensional NMR techniques (e.g. HSQC, HMBC, TOCSY, COSY and NOESY) have been developed and these have vastly expanded the amount of structural information that can be obtained by NMR spectroscopy. Improvements in NMR instrumentation now mean that 2D NMR spectra are routinely (and sometimes automatically) acquired during the identification and characterisation of organic compounds. Organic Structures from 2D NMR Spectra is a carefully chosen set of more than 60 structural problems employing 2D-NMR spectroscopy. The problems are graded to develop and consolidate a student’s understanding of 2D NMR spectroscopy. There are many easy problems at the beginning of the collection, to build confidence and demonstrate the basic principles from which structural information can be extracted using 2D NMR. The accompanying text is very descriptive and focussed on explaining the underlying theory at the most appropriate level to sufficiently tackle the problems. Organic Structures from 2D NMR Spectra Is a graded series of about 60 problems in 2D NMR spectroscopy that assumes a basic knowledge of organic chemistry and a basic knowledge of one-dimensional NMR spectroscopyIncorporates the basic theory behind 2D NMR and those common 2D NMR experiments that have proved most useful in solving structural problems in organic chemistryFocuses on the most common 2D NMR techniques – including COSY, NOESY, HMBC, TOCSY, CH-Correlation and multiplicity-edited C-H Correlation.Incorporates several examples containing the heteronuclei 31P, 15N and 19F Organic Structures from 2D NMR Spectra is a logical follow-on from the highly successful “Organic Structures from Spectra” which is now in its fifth edition. The book will be invaluable for students of Chemistry, Pharmacy, Biochemistry and those taking courses in Organic Chemistry.Also available: Instructors Guide and Solutions Manual to Organic Structures from 2D NMR Spectra
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