Maths for computer scientists Books

Book SynopsisThis book teaches algebra and geometry. The authors dedicate chapters to the key issues of matrices, linear equations, matrix algorithms, vector spaces, lines, planes, second-order curves, and elliptic curves. The text is supported throughout with problems, and the authors have included source code in Python in the book. The book is suitable for advanced undergraduate and graduate students in computer science. Trade Review“It is most interesting to combine a classical mathematical topic with a new evolving programming language and exactly this is obtained by this book. … This material is used as a case study for their implementation for solving problems in theoretical and practical cryptography. The ‘roadmap’ of the content of this also quite interesting.” (Panayiotis Vlamos, zbMATH 1480.00002, 2022)Table of ContentsMatrices and Matrix Algorithms.- Matrix Algebra.- Systems of Linear Equations.- Complex Numbers and Matrices.- Vector Spaces.- Vectors in a Three-Dimensional Space.- Equation of a Straight Line on a Plane.- Equation of a Plane in Space.- Equation of a Line in Space.- Bilinear and Quadratic Forms.- Curves of the Second-Order.- Elliptic Curves.- Appendix A, Basic Operators in Python and C.- Appendix B, Trigonometric Formulae.- Appendix C, The Greek Alphabet.- References.- Name Index.- Subject Index.

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Book SynopsisThis fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.Table of ContentsPart I: FundamentalsIntroductionProbability TheoryGraph TheoryPart II: Probabilistic ModelsBayesian ClassifiersHidden Markov ModelsMarkov Random FieldsBayesian Networks: Representation and InferenceBayesian Networks: LearningDynamic and Temporal Bayesian NetworksPart III: Decision ModelsDecision GraphsMarkov Decision ProcessesPartially Observable Markov Decision Processes Part IV: Relational, Causal and Deep ModelsRelational Probabilistic Graphical ModelsGraphical Causal ModelsCausal DiscoveryDeep Learning and Graphical ModelsA: A Python Library for Inference and LearningGlossaryIndex

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Book SynopsisThis book constitutes the refereed conference proceedings of the 12th International Conference on Algorithms and Complexity, CIAC 2019, held as a virtual event, in May 2021. The 28 full papers presented together with one invited lecture and 2 two abstracts of invited lectures were carefully reviewed and selected from 78 submissions. The International Conference on Algorithms and Complexity is intended to provide a forum for researchers working in all aspects of computational complexity and the use, design, analysis and experimentation of efficient algorithms and data structures. The papers present original research in the theory and applications of algorithms and computational complexity.Due to the Corona pandemic the conference was held virtually.Table of ContentsAbundant Extensions.- Three Problems on Well-Partitioned Chordal Graphs.- Distributed Distance-r Covering Problems on Sparse High-Girth Graphs.- Reconfiguration of Connected Graph Partitions via Recombination.- Algorithms for Energy Conservation in Heterogeneous Data Centers.- On Vertex-Weighted Graph Realizations.- On the Role of 3's for the 1-2-3 Conjecture.- Upper Tail Analysis of Bucket Sort and Random Tries.- Throughput Scheduling with Equal Additive Laxity.- Fragile Complexity of Adaptive Algorithms.- FPT and Kernelization Algorithms for the Induced Tree Problem.- A Tight Lower Bound for Edge-Disjoint Paths on Planar DAGs.- Upper Dominating Set: Tight Algorithms for Pathwidth and Sub-Exponential Approximation.- A Multistage View on 2-Satisfiability.- The Weisfeiler-Leman Algorithm and Recognition of Graph Properties.- The Parameterized Suffix Tray.- Exploring the Gap Between Treedepth and Vertex Cover Through Vertex Integrity.- Covering a Set of Line Segments with a Few Squares.- Circumventing Connectivity for Kernelization.- Online and Approximate Network Construction from Bounded Connectivity Constraints.- Globally Rigid Augmentation of Minimally Rigid Graphs in \(R^2\).- Extending Partial Representations of Rectangular Duals with Given Contact Orientations.- Can Local Optimality be Used for Efficient Data Reduction.- Colouring Graphs of Bounded Diameter in the Absence of Small Cycles.- Online Two-Dimensional Vector Packing with Advice.- Temporal Matching on Geometric Graph Data.

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This stimulating textbook presents a broad and accessible guide to the fundamentals of discrete mathematics, highlighting how the techniques may be applied to various exciting areas in computing. The text is designed to motivate and inspire the reader, encouraging further study in this important skill. Features: This book provides an introduction to the building blocks of discrete mathematics, including sets, relations and functions; describes the basics of number theory, the techniques of induction and recursion, and the applications of mathematical sequences, series, permutations, and combinations; presents the essentials of algebra; explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability; reviews the history of logic, discussing propositional and predicate logic, as well as advanced topics such as the nature of theorem proving; examines the field of software engineering, including software reliability and dependability and describes formal methods; investigates probability and statistics and presents an overview of operations research and financial mathematics.

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Book Synopsis The greatly expanded and updated 3rd edition of this textbook offers the reader a comprehensive introduction to the concepts of logic functions and equations and their applications across computer science and engineering. The authors’ approach emphasizes a thorough understanding of the fundamental principles as well as numerical and computer-based solution methods. The book provides insight into applications across propositional logic, binary arithmetic, coding, cryptography, complexity, logic design, and artificial intelligence.Updated throughout, some major additions for the 3rd edition include: a new chapter about the concepts contributing to the power of XBOOLE; a new chapter that introduces into the application of the XBOOLE-Monitor XBM 2; many tasks that support the readers in amplifying the learned content at the end of the chapters; solutions of a large subset of these tasks to confirm learning success; challenging tasks that need the power of the XBOOLE software for their solution. The XBOOLE-monitor XBM 2 software is used to solve the exercises; in this way the time-consuming and error-prone manipulation on the bit level is moved to an ordinary PC, more realistic tasks can be solved, and the challenges of thinking about algorithms leads to a higher level of education.Table of ContentsPart I Theoretical Foundations 1. Basic Algebraic Structures 2. Logic Functions 3. Logic Equations 4. Boolean Differential Calculus 5. Sets, Lattices, and Classes Logic Functions Part II Applications 6. Logics, Arithmetic, and Special Functions 7. SAT-Problems 8. Extremely Complex Problems 9. Combinational Circuits 10. Sequential Circuits References Index

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Book SynopsisThis book is about the harmonious synthesis of functional programming and numerical computation. It shows how the expressiveness of OCaml allows for fast and safe development of data science applications. Step by step, the authors build up to use cases drawn from many areas of Data Science, Machine Learning, and AI, and then delve into how to deploy at scale, using parallel, distributed, and accelerated frameworks to gain all the advantages of cloud computing environments.To this end, the book is divided into three parts, each focusing on a different area. Part I begins by introducing how basic numerical techniques are performed in OCaml, including classical mathematical topics (interpolation and quadrature), statistics, and linear algebra. It moves on from using only scalar values to multi-dimensional arrays, introducing the tensor and Ndarray, core data types in any numerical computing system. It concludes with two more classical numerical computing topics, the solution of Ordinary Differential Equations (ODEs) and Signal Processing, as well as introducing the visualization module we use throughout this book. Part II is dedicated to advanced optimization techniques that are core to most current popular data science fields. We do not focus only on applications but also on the basic building blocks, starting with Algorithmic Differentiation, the most crucial building block that in turn enables Deep Neural Networks. We follow this with chapters on Optimization and Regression, also used in building Deep Neural Networks. We then introduce Deep Neural Networks as well as topic modelling in Natural Language Processing (NLP), two advanced and currently very active fields in both industry and academia. Part III collects a range of case studies demonstrating how you can build a complete numerical application quickly from scratch using Owl. The cases presented include computer vision and recommender systems. This book aims at anyone with a basic knowledge of functional programming and a desire to explore the world of scientific computing, whether to generally explore the field in the round, to build applications for particular topics, or to deep-dive into how numerical systems are constructed. It does not assume strict ordering in reading – readers can simply jump to the topic that interests them most. Table of ContentsPart I: Numerical Techniques.- 1. Introduction.- 2. Numerical Algorithms.- 3. Statistics.- 4. Linear Algebra.- 5. N-Dimensional Arrays.- 6. Ordinary Differential Equations.- 7. Signal Processing.- Part II: Advanced Data Analysis Techniques.- 8. Algorithmic Differentiation.- 9. Optimisation.- 10. Regression.- 11. Neural Network.- 12. Vector Space Modelling.- Part III: Use Cases.- 13. Case Study: Image Recognition.- 14. Case Study: Instance Segmentation.- 15. Case Study: Neural Style Transfer.- 16. Case Study: Recommender System.

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Book SynopsisApplication of Machine Learning to Increase the Efficiency of the Global Search Algorithm for Solving Multicriterial Problems.- Sequential Decision Modeling for Dynamic Pricing and Revenue Management in Hotels.- Resource Allocation via Bayesian Optimization: An Efficient Alternative to Semi-Bandit Feedback.- Multi-Objective and Multiple Information Source Optimization for Fair & Green Machine Learning.- Extended Optimal Control Problem for Practical Application.- Explainable Process Deviance Discovery with Data-Efficient Deep Learning.- Line Search Stochastic Gradient Algorithm with a-Priori Rule for Monitoring the Control of the Variance.- A Machine Learning Approach to Speed up the Solution of the Distributor's Pallet Loading Problem.- Combined First- and Second-Order Directions for Deep Neural Networks Training.- Constrained Global Optimization by Smoothing.- The Unreasonable Effectiveness of Optimal Transport Distance in the Design of Multi-Objective Evolutionary Optimization Algorithms.- An Improved Modified Jaya Optimization Algorithm: Application to the Solution of Nonlinear Equation Systems.- GPU Acceleration of the Enhanced Jaya Optimization Algorithm for Solving Large Systems of Nonlinear Equations.- Effective Resistance Based Community Detection in Complex Networks.- A Comparison of Formulations for Aircraft Deconfliction.- Optimal Recombination Problem in Genetic Programming for Boolean Functions.- Heuristics with Local Improvements for Two-Processor Scheduling Problem with Energy Constraint and Parallelization.- Numerical Analysis of Optimal Control of Assets and Liabilities by a Bank.- Optimal Control for Stochastic Multi-Agent Systems with the Use of Parallel Hybrid Genetic Algorithm.- DC Optimization in Adversarial Support Vector Machine.- A First-Order Optimality Condition in Nonsmooth Generalized Semi-Infinite Programming (GSIP).- Miniaturisation of Binary Classifiers through Sparse Neural Networks.- Price Forecasting for Bitcoin: Linear Regression and SVM Approaches.- Inside the Box: 0-1 Linear Programming under Interval Uncertainty.- Machine Learning Techniques for Branch-and-Cut Methods: The Selection of Cutting Planes.- The Critical Cone and Second-Order Optimality Conditions for a State-Constrained Optimal Control Problem.- Local Information in Global Optimization with Dimensionality Reduction Schemes.- A Heuristic Solution Approach for Bulk Port Routing Optimization.- A Genetic Algorithm to Optimize the Dispatch of Firefighting Resources.- Robust Non-Convex Model-Based Approach for Deep Learning-Based Image Processing.- A DCA-Like Based Algorithm for the Merkle Tree Construction Problem in Ethereum Cryptocurrency System.- Numerical Optimization in Hyperbolic Space - Applications to Drug-Target Interaction Prediction.- Improving Feasibility of Optimal Control via Obtaining High-precision Model.- Dimensionality Reduction for Quadratic Convex Maximization.- Population Local Search for Single Processor Energy Efficient Scheduling Problem.

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Book SynopsisDimensionality Reduction with Proper Symplectic Decomposition for Learning Hamiltonian Dynamics.- Aspects of Optimization in Domestic Drinking Water Purification Systems.- Parallel Cyclic Reduction of Bordered Almost Block Diagonal Matrices.- A Direct/Indirect Approach to Optimal Control Problems.- Metaplectic Gabor Frames of Wigner-Decomposable Distributions.- Identifying Dengue Hotspots in Baguio City, Philippines through Spatiotemporal Analysis.- Discontinuous Galerkin Schemes for Master Equations Modeling Open Quantum Systems.- The Scaling Behavior of High-Order Structure Functions for a Turbulent Jet Induced by a Rotating Propeller.- Analysis of Computational Fluid Dynamics Approaches for the Development of Microfluidic Devices.- Extragradient Method For Monotone Variational Inequality in CAT(0) Spaces.- A Symbolic-Numerical Approach to Index Reduction and Solution of Differential-Algebraic Equations.- A MATLAB Code for Handling Efficiently Discontinuous BVPs.- Optimal Transport Flow Distribution and Construction of Wardrop Optimal Networks.- Optimizing the Reuse of Building Demolition Materials for Coastal Dunes Reestablishment.- A Simple Mathematical Solution to an Invariance Problem in Water Networks.- Scalability of Saharan Dust Outbreak Modelling with the Advanced Weather Research and Forecasting Model Coupled with Chemistry (WRF-Chem).- Choosing Kernel Shape Parameters in Partition of Unity Methods by Univariate Global Optimization Techniques.- A Bayesian Approach for Simultaneously Radial Kernel Parameter Tuning in the Partition of Unity Method.- Application of Deep Learning for Wildfire Risk Management: Preliminary Results.- Node-Binded Communities for Interpolation on Graphs.- A Fast Algorithm for Numerical Differentiation from Scattered Data.- Preconditioning Strategies for RBF Interpolation.- An Alternative Nonlinear Diffusion Algorithm for Image Denoising and Deblurring.- Some Numerical Approaches for Bifurcation of Nonlinear Dynamic Systems.- The Effects of Different Mesh Sizes on a Cellular Automata-Based Hydrological Model.- Computation of the Confluent Hypergeometric Function M(a,b,x).- Multi-Objective Support Vector Machine Classification Algorithm for Estimation of the Potential Atmospheric Water Harvesting.- IoT and Artificial Intelligence Integration for a Stormwater Monitoring and Management System.- Optimization Method Development for Water Management of Green Roof Systems.- Weighted Finite Element Method and Body of Optimal Parameters for One Problem of the Fracture Mechanics.- A Shallow Water Model: Analytical and Numerical Study for Simulating the Coastline Evolution.- On the Division in the Computation of Binary BBP-Type Formulas for Mathematical Constants.- Numerical Analysis of Sedimentation Tanks Efficiency Using CFD Simulation.- A Probabilistic Note on the WY Representation of Householder QR.- Applying Machine Learning to Improve Weather Forecasting in a Mediterranean Area: Preliminary Experiments.

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Book SynopsisCircuit-Based Numerical Solutions of Transmission Lines: Application to Korteweg-de Vries Equations.- Deep Learning Methods for fMRI Classification.- Deep Learning for Scoliosis Diagnosis: Methods and Databases.- Understanding Spreading Dynamics of COVID-19 by Mining Human Mobility Patterns.- Variational Quantum Algorithms for Gibbs State Preparation.- Towards a Parallel Code for Cellular Behavior in Vitro Prediction.- Combinators as Observable Presheaves: A Characterization in the Grossone Framework.- Applying Variational Quantum Classifier on Acceptability Judgements: A QNLP experiment.- Unimaginable Numbers: A Case Study as a Starting Point for an Educational Experimentation.- Some Notes on a Continuous Class of Octagons.- Game Theory Presented to Italian High School Students in Connection with Infinity Computing.- Meta Discussion Pedagogical Model to Foster Mathematics Teacher's Professional Development.- A Variational Quantum Soft Actor-Critic Algorithm for Continuous Control Tasks.- Named Entity Recognition to Extract Knowledge from Clinical Texts.- Applied Mathematical Modelling in the Physics Problem-Solving Classroom.- AIR SAFE: Leveraging IoT Sensors and AI Models to Foster Optimal Indoor Conditions.- Unimaginable Numbers and Infinity Computing at School: An Experimentation in Northern Italy.- The Cantor-Vitali Function and Infinity Computing.- New Probabilistic Methods for Generating Risk Maps.- How to Deal with Different Densities of Urban Spatial Data? A Comparison of Clustering Approaches to Detect City Hotspots.- The Impact of Vectorization on The Efficiency of a Parallel PIC Code for Numerical Simulation of Plasma Dynamics in Open Trap.- Algorithms for Design with CNC Machines: The Case Study of Wood Furniture.- PyGrossone: A Python Library for the Infinity Computer.- Towards Reproducible Research in Machine Learning via Blockchain.- Dossier Classification to Support Workflow Management Optimization.- Self-Sovereign Identification of IoT Devices by Using Physically Unclonable Functions and Blockchain.- Introducing Nondum, a Mathematical Notation for Computation with Approximations.- Visualization of Multilayer Networks.- Legal Systems and Fractals, Towards Infinity Computing.- Exploit Innovative Computer Architecture with Molecular Dynamics.- A Sentiment Analysis on Reviews of Italian Healthcare.-A Numerical Approach to Basic Calculus.- Modelling Hyperentanglement for Quantum Information Processes.- An Innovative Sentiment Analysis Model for COVID-19 Tweets.- Exploring Hierarchical MPI Reduction Collective Algorithms Targeted to Multicore Node Clusters.

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Book Synopsis .- Invited talks..- The restricted decoding problem and its application to post-quantum cryptography..- Algebraic curves over finite fields: rational points and birational invariants..- An overview of mathematical problems, cryptosystems, and their interconnection..- Making and breaking post-quantum cryptography from elliptic curve..- Coding theory..- Determining the complete weight distributions of some families of cyclic codes..- Central limit theorem for linear eigenvalue statistics of random matrices from binary linear codes..- On decoding hyperbolic codes..- Fast decoding of group testing results from Reed-Solomon d-disjunct matrices..- Quantum CSS Duadic and Triadic Codes: New Insights and Properties..- Cryptography and Boolean functions..- Prescribing traces of primitive elements in finite fields..- On Cryptographic Properties of a Class of Power Permutations in Odd Characteristic..- Generating Gaussian pseudorandom noise with binary sequences..- An FPGA Accelerated Search Method for Maximum Period NLFSRs File..- On fat linearized polynomials..- Counting polynomials with distinct roots in finite fields using the subset sum problem..- Generalized class group actions on oriented elliptic curves with level structure..-  Differential biases, c-differential uniformity, and their relation to differential attacks..- On the Walsh and Fourier-Hadamard Supports of Boolean functions from a quantum viewpoint..- Postquantum Cryptography..- Efficient Batch Post-Quantum Signatures with Crystals Dilithium..- A Practical Group Signature Scheme based on Rank Metric..- SMALL: Scalable Matrix OriginAted Large Integer PoLynomial Multiplication Accelerator for Lattice-based Post-Quantum Cryptography.

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Book Synopsis.- A Runtime Analysis of the Multi-Valued Compact Genetic Algorithm on Generalized LeadingOnes..- Evolutionary Anytime Algorithms..- Studies on Survival Strategies to Protect Expert Knowledge in Evolutionary Algorithms for Interactive Role Mining..- Diversification through Candidate Sampling for a Non-Iterated Lin-Kernighan-Helsgaun Algorithm..- Instance Space Analysis and Algorithm Selection for a Parallel Batch Scheduling Problem..- Meta-learning of Univariate Estimation-of-Distribution Algorithms for Pseudo-Boolean Problems..- A Selective Vehicle Routing Problem for the Bloodmobile System..- A Genetic Approach to the Operational Freight-on-Transit problem..- LON/D — Sub-problem Landscape Analysis in Decomposition-based Multi-objective Optimization..- Visualizing Pseudo-Boolean Functions: Feature Selection and Regularization for Machine Learning..- Mixed-Binary Problems Optimized with Fast Discrete Solver..- Feature-based Evolutionary Diversity Optimization of Discriminating Instances for Chance-constrained Optimization Problems..- Adaptive neighborhood search based on landscape learning: a TSP study..- Healthcare Facility Location Problem and Fitness Landscape Analysis..- Generating (Semi-)Active Schedules for Dynamic Multi-mode Project Scheduling Using Genetic Programming Hyper-heuristics..- Price-and-branch Heuristic for Vector Bin Packing.

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Book Synopsis.- On the application of a queuing network to simulate container transshipment in a sea container terminal..- On a limiting structure of discrete-time stochastic branching systems with the eventually awaiting death moment..- A Power-Law Adjustable Coefficient Dynamic Pricing Model Considering Shortages and Leftovers..- Inferences of Modularity for Graphs Evolved by the Clustering Attachment Model..- Queueing model with correlated arrival process, simultaneous service of a finite number of customers, and pre-processing, post-processing and co-processing of customers..- Reliability Analysis of a k-out-of-n System with External Service and Non-Preemptive Priority under N-Policy with Multi-Server Vacations..- On Recursive Marginal and MAP Inference in State Observation Models..- Asymptotic Analysis of Sojourn Time in Retrial Queueing System with Non-persistent Customers and Feedback..- Implementation of a Convolution Algorithm to the Evaluation of Stationary Characteristics of Resource Loss System with Resource-Dependent Service Times..- Diffusion Approximation for the MAP/GI/1 Retrial Queue with Two-Way Communication..- Investigation of M/G/1//N system with impatient customers, unreliable primary and a backup server..- G-Network with Rewards as a Cluster System Model..- On Application of Karamata Slowly Varying Functions in the Theory of Noncritical Markov Branching Systems..- Asymptotic analysis of a multiserver retrial queue with disasters..- Analysis of a Batch Arrival Queue with Power Saving Mode..- Lumping and Numerical Analysis for Multi-Server Job Model..- Unloading time martingale relations in a cyclic queueing system in random environment..- On estimation of some functional of the distribution function for dependent incomplete observations in queuing theory..- Analysis of a queueing system providing service to regular and ad hoc clients..- Modeling and analysis of cyclic control of periodic conflict flows..- Statistical Testing for Long-Range Dependence in the Workload of a Single-Server Queue..- Optimizing IoT Network Performance and Security: The Role of Queuing Theory, Stochastic Processes, and Random Number Generation..- Algorithmic Approach to Study Queueing-Inventory Systems with Queue-Dependent Hybrid Replenishment Policy..- A Stochastic Approach for Optimizing a Discrete Time (s, S) Perishable Inventory System with Modified N-Policy..- Algorithm for calculating the stationary probability distribution of a system M2—1—(N1, N2) with priorities..- Profit Optimization of a Perishable Inventory System with Retrial of Customers and Unreliable Server.

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Book Synopsis.- Automated Reasoning for Data-Aware Petri Nets..- Petri Nets and Higher-Dimensional Automata..- Discovering the Influence of Exogenous Data on Decisions in Processes..- Synthesizing Petri Nets from Labelled Petri Nets..- Coverability in Well-Formed Free-Choice Nets..- High-Level Message Sequence Charts: Satisfiability and RealizabilityRevisited..- Distributed Reference Net Simulation based on Event Streaming..- Persistent Permutations, Fairness, Asymmetric Choice Petri Nets, andOchmanski’s Conjecture..- Statistical Model Checking of Stochastic Timed-Arc Petri Nets..- Energy Transfer in timed cyclic networks..- Leveraging Petri Nets for Workflow Anomaly Detection in MicroserviceArchitectures..- Translating Workflow Nets into the Partially Ordered Workflow Language..- Distributed Places and Safe Net Reduction..- Analysing Probabilistic Hornets..- Enjoy the Silence, Part II: Probability-Based Queries on StochasticLabelled Petri Nets..- Decidability problems for weak Time Petri Nets with read, reset andtransfer arcs..- SkiNet: a User-Oriented Tool for Petri Net-based Analysis of Robotic Skills..- Deciding (Sub-Marking) Reachability in O(Pˆ2 + Tˆ2) for SoundAcyclic Free-Choice Workflow Nets..- Complexity of Alignments on Sound Free-Choice Workflow Nets..- Computing Alignments for Partially-ordered Traces Through Petri NetUnfoldings ..- Simplifying LTL Model Checking Given Prior Knowledge..- Failure Resilience of strongly synchronized Processes..- Symbolic Model Checking in the Modular State Space using BinaryDecision Diagrams.

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Book SynopsisGraph Transformation Theory: Semantics and Static Analysis: Termination of Injective DPO Graph Rewriting Systems using Subgraph Counting.- Rewriting for Traced Monoidal Closed Categories.- Parallel Rule Application with Doubling Avoidance.- Granular Conflict Analysis for Transformation Rules with Application Conditions. Specifying Graph Properties via Automata and Logic: Specifying and Checking Graph Properties with Alternating Graph Automata.- Graph Formulas and their Translation to Alternating Graph Automata. Applications of Graph Transformation for Program Verification and Testing: Fuzzing Graph Database Applications with Graph Transformations.- Counterexample-Guided Abstraction Refinement for Generalized Graph Transformation Systems.- Test Case Generation from Graph Transformation Systems using Deep Reinforcement Learning. Applications of Graph Transformation for Modeling: Graph-transformational Threat Modeling.- Graph Rewriting for User State-Based Dialogue Adaption in Real-Time: An Application in Personalized Interview Training.

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Book Synopsis.- Ant Colony Optimization as a Core Strategy in Efficient Heuristics Development for Location Problems with Two Contradictory Objectives..- Sustainable Production Scheduling: An Overview of Models and Resolution Methods..- Equilibrium Analysis and Social Optimization of a Selectable Single or Batch Services with General Service Time Distribution..- An Efficient Average Project Based Approximate Dynamic Programming Approach for Stochastic Resource-Constrained Project Scheduling..- A Scatter Search Metaheuristic and Improvements to an Exact Algorithm for the Weighted Safe Set Problem..- Beam-Layout for a Telecommunication Satellite: Comparison of a Matheuristic Approach and a Merge-and-Split Heuristic..- Serial, Simultaneous or Mixed? Attack Strategies with an Arsenal of Tools..- ML-Enhanced Bilevel Optimization for Real-Time Electricity Pricing.

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Book SynopsisGuarding a 1.5D terrain with Imprecise Viewpoints.- Extending simple monotone drawings.- Guarding Terrains with Guards on a Line.- Minimum-Complexity Graph Simplification under the Fréchet-Like Distance.- Drawing Reeb Graphs.- Monotone Partitions of Simple Polygons.- A Linear Delay Algorithm of Enumerating Strongly-Connected Induced Subgraphs Based on SSD Set System.- Exact Learning of Weighted Graphs Using Composite Queries.- Monotone classes, even graphs and the Hamiltonian cycle problem.- Covering vertices by $4^+$-paths: A simpler local search coupled with a more delicate amortization.- Bicluster Editing with Overlaps: A Vertex Splitting Approach.- Vector spaces of graphs closed under isomorphism.- Average Sensitivity of Breadth-First Search Algorithms on Grids.- Permanent of bipartite graphs in terms of determinants.- Improved Approximation for Unpopularity in (3,3)-Hypergraph Matching with one-sided preferences.- Inverting Parameterized Burrows-Wheeler Transform.- A Space-Efficient Algorithm for  Longest Common Almost Increasing Subsequence of Two Sequences.- Fast Pattern Matching with Epsilon Transitions.- Reconstructing Sets of Strings from Their k-way Projections: Algorithms & Complexity (Extended Abstract).- The Closed Geodetic Game: algorithms and strategies.- ETH Lower Bounds for $n$-Queens: Time Waits for Nobody.- On Solving Simple Curved Nonograms.- Tile-based Knot Assembly with Celtic!.- On the existence of a subgroup magic rectangle.- Parameterized Algorithms for Power Edge Set and Zero Forcing Set.- Minimizing $ell_2$ Norm of Flow Time by Starvation Mitigation.- Recoverable Robust Cardinality Constrained Maximization with Commitment of a Submodular Function.- Bicriteria FPT-Approximation Algorithms for Vertex Deletion to Bounded Degeneracy Graphs.- Optimal Random Bit Sampling for Set Partition-like Structures.- Exact Set Packing in Multimodal Transportation with Ridesharing System for First/Last Mile.- Linear Search with Probabilistic Detection and Variable Speeds.- Streaming Algorithms for Scheduling Jobs with Priorities.

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Book SynopsisThis book provides an introduction to hypergraphs, its aim being to overcome the lack of recent manuscripts on this theory. In the literature hypergraphs have many other names such as set systems and families of sets. This work presents the theory of hypergraphs in its most original aspects, while also introducing and assessing the latest concepts on hypergraphs. The variety of topics, their originality and novelty are intended to help readers better understand the hypergraphs in all their diversity in order to perceive their value and power as mathematical tools. This book will be a great asset to upper-level undergraduate and graduate students in computer science and mathematics. It has been the subject of an annual Master's course for many years, making it also ideally suited to Master's students in computer science, mathematics, bioinformatics, engineering, chemistry, and many other fields. It will also benefit scientists, engineers and anyone else who wants to understand hypergraphs theory.Trade ReviewFrom the reviews:“This book addresses the mathematics and theory of hypergraphs. The target audience includes graduate students and researchers with an interest in math and computer science (CS). … I expect readers of this book will be motivated to advance this field, which in turn can advance other sciences.” (Hsun-Hsien Chang, Computing Reviews, January, 2014)“The aim of this book is to introduce the basic concepts of hypergraphs, to present the knowledge of the theory and applications of hypergraphs in other fields. … This book is useful for anyone who wants to understand the basics of hypergraph theory. It is mainly for math and computer science majors, but it may also be useful for other fields which use the theory. … appropriate for both researchers and graduate students. It is very well-written and proofs are stated in a clear manner.” (Somayeh Moradi, zbMATH, Vol. 1269, 2013)Table of ContentsHypergraphs: basic concepts.- Hypergraphs: first properties.- Hypergraph coloring.- Some particular hypergraphs.- Reduction-contraction of Hypergraph.- Dirhypergraphs: basic concepts.- Applications of hypergraph theory : a brief overview.

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Book SynopsisThis book provides an introduction to hypergraphs, its aim being to overcome the lack of recent manuscripts on this theory. In the literature hypergraphs have many other names such as set systems and families of sets. This work presents the theory of hypergraphs in its most original aspects, while also introducing and assessing the latest concepts on hypergraphs. The variety of topics, their originality and novelty are intended to help readers better understand the hypergraphs in all their diversity in order to perceive their value and power as mathematical tools. This book will be a great asset to upper-level undergraduate and graduate students in computer science and mathematics. It has been the subject of an annual Master's course for many years, making it also ideally suited to Master's students in computer science, mathematics, bioinformatics, engineering, chemistry, and many other fields. It will also benefit scientists, engineers and anyone else who wants to understand hypergraphs theory.Trade ReviewFrom the reviews:“This book addresses the mathematics and theory of hypergraphs. The target audience includes graduate students and researchers with an interest in math and computer science (CS). … I expect readers of this book will be motivated to advance this field, which in turn can advance other sciences.” (Hsun-Hsien Chang, Computing Reviews, January, 2014)“The aim of this book is to introduce the basic concepts of hypergraphs, to present the knowledge of the theory and applications of hypergraphs in other fields. … This book is useful for anyone who wants to understand the basics of hypergraph theory. It is mainly for math and computer science majors, but it may also be useful for other fields which use the theory. … appropriate for both researchers and graduate students. It is very well-written and proofs are stated in a clear manner.” (Somayeh Moradi, zbMATH, Vol. 1269, 2013)Table of ContentsHypergraphs: basic concepts.- Hypergraphs: first properties.- Hypergraph coloring.- Some particular hypergraphs.- Reduction-contraction of Hypergraph.- Dirhypergraphs: basic concepts.- Applications of hypergraph theory : a brief overview.

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Book SynopsisTransactions are a concept related to the logical database as seen from the perspective of database application programmers: a transaction is a sequence of database actions that is to be executed as an atomic unit of work. The processing of transactions on databases is a well- established area with many of its foundations having already been laid in the late 1970s and early 1980s.The unique feature of this textbook is that it bridges the gap between the theory of transactions on the logical database and the implementation of the related actions on the underlying physical database. The authors relate the logical database, which is composed of a dynamically changing set of data items with unique keys, and the underlying physical database with a set of fixed-size data and index pages on disk. Their treatment of transaction processing builds on the “do-redo-undo” recovery paradigm, and all methods and algorithms presented are carefully designed to be compatible with this paradigm as well as with write-ahead logging, steal-and-no-force buffering, and fine-grained concurrency control.Chapters 1 to 6 address the basics needed to fully appreciate transaction processing on a centralized database system within the context of our transaction model, covering topics like ACID properties, database integrity, buffering, rollbacks, isolation, and the interplay of logical locks and physical latches. Chapters 7 and 8 present advanced features including deadlock-free algorithms for reading, inserting and deleting tuples, while the remaining chapters cover additional advanced topics extending on the preceding foundational chapters, including multi-granular locking, bulk actions, versioning, distributed updates, and write-intensive transactions.This book is primarily intended as a text for advanced undergraduate or graduate courses on database management in general or transaction processing in particular.Table of Contents1 Transactions on the Logical Database.- 2 Operations on the Physical Database.- 3 Logging and Buffering.- 4 Transaction Rollback and Restart Recovery.- 5 Transactional Isolation.- 6 Lock-Based Concurrency Control.- 7 B-Tree Traversals.- 8 B-Tree Structure Modifications.- 9 Advanced Locking Protocols.- 10 Bulk Operations on B-Trees.- 11 Online Index Construction and Maintenance.- 12 Concurrency Control by Versioning.- 13 Distributed Transactions.- 14 Transactions in Page-Server Systems.- 15 Processing of Write-Intensive Transactions.

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Book SynopsisThis clearly written textbook presents an accessible introduction to discrete mathematics for computer science students, offering the reader an enjoyable and stimulating path to improve their programming competence. The text empowers students to think critically, to be effective problem solvers, to integrate theory and practice, and to recognize the importance of abstraction. Its motivational and interactive style provokes a conversation with the reader through a questioning commentary, and supplies detailed walkthroughs of several algorithms.This updated and enhanced new edition also includes new material on directed graphs, and on drawing and coloring graphs, in addition to more than 100 new exercises (with solutions to selected exercises).Topics and features: assumes no prior mathematical knowledge, and discusses concepts in programming as and when they are needed; designed for both classroom use and self-study, presenting modular and self-contained chapters that follow ACM curriculum recommendations; describes mathematical processes in an algorithmic manner, often supported by a walkthrough demonstrating how the algorithm performs the desired task; includes an extensive set of exercises throughout the text, together with numerous examples, and shaded boxes highlighting key concepts; selects examples that demonstrate a practical use for the concept in question.Students embarking on the start of their studies of computer science will find this book to be an easy-to-understand and fun-to-read primer, ideal for use in a mathematics course taken concurrently with their first programming course.Table of ContentsAlgorithms, Numbers and MachinesSets, Sequences and CountingBoolean Expressions, Logic and ProofSearching and SortingGraphs and TreesRelations: Especially on (Integer) SequencesSequences and SeriesGenerating Sequences and SubsetsDiscrete Probability and Average Case ComplexityTuring Machines

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Book SynopsisModern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections.A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.Trade ReviewFrom the reviews:“This book is a complete study of ℓ1-penalization based statistical methods for high-dimensional data … . Definitely, this book is useful. … its strong level in mathematics makes it more suitable to researchers and graduate students who already have a strong background in statistics. … it gives the state-of-the-art of the theory, and therefore can be used for an advanced course on the topic. … the last part of the book is an exciting introduction to new research perspectives provided by ℓ1-penalized methods.” (Pierre Alquier, Mathematical Reviews, Issue 2012 e)“All Classical Statisticians interested in the very popular but a bit old methodologies like the Lasso (Tibshirani, 1996), its modifications like adaptive Lasso (Zou, 2006), and their theory, computational algorithms, applications to bioinformatics and other high dimensional applications. All such researchers would find this book worth buying. It is written by two outstanding theoreticians with flair for clear writing and excellent applications. … theory depends a lot on new concentration inequalities coming from the French probabilists. The book has good collection of these, with proofs.” (Jayanta K. Ghosh, International Statistical Review, Vol. 80 (3), 2012)Table of ContentsIntroduction.- Lasso for linear models.- Generalized linear models and the Lasso.- The group Lasso.- Additive models and many smooth univariate functions.- Theory for the Lasso.- Variable selection with the Lasso.- Theory for l1/l2-penalty procedures.- Non-convex loss functions and l1-regularization.- Stable solutions.- P-values for linear models and beyond.- Boosting and greedy algorithms.- Graphical modeling.- Probability and moment inequalities.- Author Index.- Index.- References.- Problems at the end of each chapter.

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Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the 21st Workshop on Logic, Language, Information and Communication, WoLLIC 2014, held in Valparaiso, Chile, in September 2014. The 15 contributed papers presented together with 6 invited lectures were carefully reviewed and selected from 29 submissions. The focus of the workshop was on the following subjects Inter-Disciplinary Research involving Formal Logic, Computing and Programming Theory, and Natural Language and Reasoning.

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Book SynopsisChapter 1 Signals.- Chapter 2 The Discrete Fourier Transform.- Chapter 3 Properties of the DFT.- Chapter 4 Two-Dimensional DFT.- Chapter 5 Aliasing and Leakage.- Chapter 6 Convolution and Correlation.- Chapter 7 Fourier Series.- Chapter 8 The Discrete-Time Fourier Transform.- Chapter 9 The Fourier Transform.- Cahpter 10 Fast Computation of the DFT.

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Book SynopsisDomination in Diameter Two Graphs and the 2 Club Cluster Vertex Deletion Parameter.- $k$-Universality of Regular Languages Revisited.- Comparing the Hardness of Online Minimization and Maximization Problems with Predictions.- Complexity Classes for Online Problems with and without Predictions.- Scheduling with Testing: Competitive Algorithms for Minimizing the Total Weighted Completion Time in the Adversarial Model.- Mixed Graph Covering with Target Constraints.- Multiplication of 0-1 matrices via clustering.- From MAXCUT to MAXNAESAT: Elegant Proofs and Algorithmic Advances.- Exact Algorithms for the Maximum $k$-Balanced Weighted Biclique Problem.- Approximation Algorithms for Individual Preference Facility Location.- The online power cover problem on a line.- The Subinterval Cover Problem.- Oblivious Robots Under Round Robin: Gathering on Rings.- Finding a Set of Long Common Substrings with Repeats from m Input Strings.- A LP-rounding based algorithm for soft capacitated facility location problem with submodular penalties.- Less-excludable Mechanism for DAOs in Public Good Auctions.- TBDS: Transaction-Based Data Sharing.- Pure Nash Equilibria of Weighted Picking Sequence Protocol is WEF1 for Two Agents.- A Comparative Study of Waitlist Mechanisms: Deferral Versus Pay-Per-Offer.- Optimal Repurchasing Contract Design for Efficient Utilization of Computing Resources.- Characterizing Strategyproofness Through Score Functions in Voting Mechanisms.- Minimizing Blocking Agents for Stable Matching with Partial Approval Information.- The Capacity-Constrained Facility Location Problem with Ordinal Preferences: Algorithmic and Mechanism Design Perspectives.- Regularized Minimax-V Learning for Solving Randomly Terminating Two-player Zero-sum Markov Games.- Improved Approximation of Maximin Share Fair Allocation under Generalized Assignment Constraints.- Optimal Hiring Strategy in Auction-Based Crowdsourcing Systems.- Large-Scale Contextual Market Equilibrium Computation through Deep Learning.- Fair Value Distribution in Cooperative Committee Election.- A Payoff-Based Policy Gradient Method in Stochastic Games with Long-Run Average Payoffs.- Mechanism Design for Auctions with Externalities on Budgets.

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Book Synopsis.- On the Problem of Best Arm Retention..- Clustering with a Knapsack Constraint: Parameterized Approximation Algorithms for the Knapsack Median Problem..- On the Existence of EFX (and Pareto-Optimal) Allocations for Binary Chores..- How to Play Old Maid with Virtual Players..- Algorithms for Optimally Shifting Intervals under Intersection Graph Models..- On the Fine-grained Complexity of Approximating Max k-Coverage..- Nested and Interleaved Ticketing for Multiple Travelers..- Longest (k]-tuple Common Substrings..- Scheduling two types of jobs with minimum makespan..- Blockchain Technology for Digital Asset Ownership..- On the Optimal Mixing Problem of Approximate Nash Equilibria in Bimatrix Games..- Finding Fair and Efficient Allocations Under Budget Constraints..- Computations and Complexities of Tarski's Fixed Points and Supermodular Games..- Parity-Constrained k-Supplier Problem..- Approximating Principal-Agent Problem under Bayesian..- Robust Facility Leasing Problem with Penalties..- Randomized Strategyproof Mechanisms for Multi-stage Facility Location Problem with Capacity Constraints..- From Evolutionary Game Dynamics to Non-negative Matrix Factorization: Acceleration with Hessian Geometry..- A case for Copeland: from theory to practice..- Deterministic and Universal Truthful Mechanism for Fair Matching..- Equilibrium Strategies of Carbon Emission Reduction in Agricultural Product Supply Chain under Carbon Sink Trading..- Active Learning Supported Iterative Combinatorial Auctions..- Locating Two Facilities on a Square with a Minimum Distance Requirement.

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Book SynopsisAlgebra und Diskrete Mathematik gehören zu den wichtigsten mathematischen Grundlagen der Informatik. In diese mathematischen Teilgebiete führt Band 1 des zweibändigen Lehrbuchs umfassend ein. Dabei ermöglichen klar herausgearbeitete Lösungsalgorithmen, viele Beispiele und ausführliche Beweise einen raschen Zugang zum Thema. Die umfangreiche Sammlung von Übungsaufgaben hilft bei der Erarbeitung des Stoffs und zeigt darüber hinaus, welche unterschiedlichen Anwendungsmöglichkeiten es gibt. Die 3. Auflage wurde korrigiert und erweitert.Table of ContentsTeil I Grundbegriffe der Mathematik und Algebraische Strukturen.- Teil II Lineare Algebra und analytische Geometrie.- Teil III Numerische Algebra und Kombinatorik.- Teil IV Übungsaufgaben.

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Book SynopsisThis book constitutes the proceedings of the Third International Conference on Abstract State Machines, B, VDM, and Z, which took place in Pisa, Italy, in June 2012. The 20 full papers presented together with 2 invited talks and 13 short papers were carefully reviewed and selected from 59 submissions. The ABZ conference series is dedicated to the cross-fertilization of five related state-based and machine-based formal methods: Abstract State Machines (ASM), Alloy, B, VDM, and Z. They share a common conceptual foundation and are widely used in both academia and industry for the design and analysis of hardware and software systems. The main goal of this conference series is to contribute to the integration of these formal methods, clarifying their commonalities and differences to better understand how to combine different approaches for accomplishing the various tasks in modeling, experimental validation and mathematical verification of reliable high-quality hardware/software systems.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisPresents an account of graph theory. Written with students of mathematics and computer science in mind, this book reflects the state of the subject and emphasizes connections with other branches of pure mathematics. It presents a survey of fresh topics and includes more than 600 exercises.Trade Review"...This book is likely to become a classic, and it deserves to be on the shelf of everyone working in graph theory or even remotely related areas, from graduate student to active researcher."--MATHEMATICAL REVIEWSTable of Contents1: Fundamentals. 2: Electrical Networks. 3: Flows, Connectivity and Matching. 4: Extremal Problems. 5: Colouring. 6: Ramsey Theory. 7: Random Graphs. 8: Graphs, Groups and Matrices. 9: Random Walks on Graphs. 10: The Tutte Polynomial.

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Book SynopsisThis title develops introductory topics in probability and statistics with particular emphasis on concepts that arise in computer science. It starts with the basic definitions of probability distributions and random variables and elaborates their properties and applications.Trade Review"Undoubtedly, this is an excellent and well-organized book." (Computing Reviews, August 27, 2008)Table of ContentsPreface. 1. Combinatorics and Probability. 1.1 Combinatorics. 1.2 Summations. 1.3 Probability spaces and random variables. 1.4 Conditional probability. 1.5 Joint distributions. 1.6 Summary. 2. Discrete Distributions. 2.1 The Bernoulli and binomial distributions. 2.2 Power series. 2.3 Geometric and negative binomial forms. 2.4 The Poisson distribution. 2.5 The hypergeometric distribution. 2.6 Summary. 3. Simulation. 3.1 Random number generation. 3.2 Inverse transforms and rejection filters. 3.3 Client-server systems. 3.4 Markov chains. 3.5 Summary. 4. Discrete Decision Theory. 4.1 Decision methods without samples. 4.2 Statistics and their properties. 4.3 Sufficient statistics. 4.4 Hypothesis testing. 4.5 Summary. 5. Real Line-Probability. 5.1 One-dimensional real distributions. 5.2 Joint random variables. 5.3 Differentiable distributions. 5.4 Summary. 6. Continuous Distributions. 6.1 The normal distributions. 6.2 Limit theorems. 6.3 Gamma and beta distributions. 6.4 The X2 and related distributions. 6.5 Computer simulations. 6.6 Summary. 7. Parameter Estimation. 7.1 Bias, consistency, and efficiency. 7.2 Normal inference. 7.3 Sums of squares. 7.4 Analysis of variance. 7.5 Linear regression. 7.6 Summary. A. Analytical Tools. B. Statistical Tables. Bibliography. Index.

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Book SynopsisPattern recognition is the construction of algorithms to decode and recognize images or data patterns in so-called random data. It is a vital and growing field with applications in artifical intelligence, machine learing, data mining, speech recognition, bioinformatics, and computer vision.Trade Review"...it provides a good introduction to the subject of Pattern Classification." (Journal of Classification, September 2007) "...a fantastic book! The presentation...could not be better, and I recommend that future authors consider...this book as a role model." (Journal of Statistical Computation and Simulation, March 2006) "...strongly recommended both as a professional reference and as a text for students..." (Technometrics, February 2002) "...provides information needed to choose the most appropriate of the many available technique for a given class of problems." (SciTech Book News, Vol. 25, No. 2, June 2001) "I do not believe anybody wishing to teach or do serious work on Pattern Recognition can ignore this book, as it is the sort of book one wishes to find the time to read from cover to cover!" (Pattern Analysis & Applications Journal, 2001) "This book is the unique text/professional reference for any serious student or worker in the field of pattern recognition." (Mathematical Reviews, Issue 2001k) "...gives a systematic overview about the major topics in pattern recognition, based whenever possible on fundamental principles." (Zentralblatt MATH, Vol. 968, 2001/18) "attractively presented and readable" (Journal of Classification, Vol.18, No.2 2001)Table of ContentsBayesian Decision Theory. Maximum-Likelihood and Bayesian Parameter Estimation. Nonparametric Techniques. Linear Discriminant Functions. Multilayer Neural Networks. Stochastic Methods. Nonmetric Methods. Algorithm-Independent Machine Learning. Unsupervised Learning and Clustering. Appendix. Index.

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Book SynopsisThis title develops introductory topics in probability and statistics with particular emphasis on concepts that arise in computer science. It starts with the basic definitions of probability distributions and random variables and elaborates their properties and applications.Trade Review"This text will fill a gap in the education of a sophisticated computer science student who has a firm base in mathematics and statistics." (Computing Reviews, May 7, 2009) "…this textbook would be ideal." (The American Statistician, February 2006) "This is really a statistics textbook written explicitly for undergraduate computer science majors…I found the numerous examples of the use of statistics within the field of computer science extremely informative." (Technometrics, November 2004) "Thorough, in-depth, relatively complete and rigorous introduction to the statistics a CS professional should know." (American Mathematical Monthly, August 2004) "This is a rigorous introductory text in probability and statistics, which also develops in a rigorous fashion all the necessary supporting mathematics beyond calculus and algebra." (Mathematical Reviews, issue 2004i) "...one-of-a-kind resource...proves an ideal resource for computer science students and practitioners interested in a probability study..." (Zentralblatt Math, Vol. 1027, 2004) “...presents introductory topics in probability and statistics with particular emphasis on concepts that arise in computer science...disguised also by the feature that it develops all necessary supporting mathematics in a thorough and rigorous fashion.” (Quarterly of Applied Mathematics, Vol. LXI, No. 4, December 2003)Table of ContentsPreface. 1. Combinatorics and Probability. 2. Discrete Distributions. 3. Simulation. 4. Discrete Decision Theory. 5. Real Line-Probability. 6. Continuous Distributions. 7. Parameter Estimation. Appendix A. Analytical Tools. Appendix B. Statistical Tables. Bibliography. Index.

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Book SynopsisProviding an accessible introduction to a range of modern computational techniques, this volume is perfect for anyone with only a limited knowledge of physics.Trade Review"? Dieses Buch mit seinem klar eingegrenzten Themenspektrum ist ausgezeichnet - gut lesbar und informativ zugleich!" Chemistry in Britain Table of ContentsPreface. Acknowledgments. About the Author. About the Book. Introduction. Numerical Solutions to Schrödinger's Equation. Approximate Methods. Matrix Methods. Deterministic Simulations. Stochastic Simulations. Percolation Theory. Evolutionary Methods. Molecular Dynamics. Appendix A: FORTRAN Implementation of the Shooting Method. Appendix B: ² in Spherical Polar Coordinates. Appendix C: A Comment on the Computer Sourcecodes. Appendix D: Note for Tutors. References. Index.

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Book SynopsisProviding an accessible introduction to a range of modern computational techniques, this book is perfect for anyone with only a limited knowledge of physics. It leads readers through a series of examples, problems, and practical--based tasks covering the basics to more complex ideas and techniques.Trade Review"within its tightly defined scope, the book is excellent, being both readable and informative" (Chemistry in Britain, January 2002) "...The book is fresh in its spirit..." (Zentralblatt Math, Vol.987, No. 12, 2002) "...an excellent book for undergraduate courses..." (Physical Sciences Educational Reviews, November 2002)"? Dieses Buch mit seinem klar eingegrenzten Themenspektrum ist ausgezeichnet - gut lesbar und informativ zugleich!" Chemistry in BritainTable of ContentsPreface Introduction Numerical Solutions to Schrö dinger's Equation Approximate Methods Matrix Methods Deterministic Simulations Stochastic Simulations Percolation Theory Evolutionary Methods Molecular Dynamics Appendices References Index

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Book SynopsisThis introductory level text is suitable for use by advanced undergraduate and graduate students of computational biology. Written by experienced authors, it provides detailed coverage of many algorithms, including applications and possible modifications.Trade Review"...much-needed introductory level text..." - La Doc Sti, July 2000Table of ContentsMolecular Biology. Math Primer. Sequence Alignment. All About Eve. Hidden Markov Models. Structure Prediction. Appendices. References. Index.

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Book SynopsisRecently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology. * Provides the background mathematics required to understand why certain algorithms work * Guides the reader through probability theory, entropy and combinatorial optimization * In-depth coverage of molecular biology and protein structure prediction * Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction * Includes class tested exercises useful for self-study * Source code of programs available on a Web site Primarily aimed at advanced undergradTrade Review"...much needed introductory level text on the subject..." (La Doc STI, July 2000) "...very concise and compact..." (Mathematical Reviews, 2002h)Table of ContentsMolecular Biology. Math Primer. Sequence Alignment. All About Eve. Hidden Markov Models. Structure Prediction. Appendices. References. Index.

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Book SynopsisA comprehensive exploration of the mathematics behind the modeling and rendering of computer graphics scenes Mathematical Structures for Computer Graphics presents an accessible and intuitive approach to the mathematical ideas and techniques necessary for two- and three-dimensional computer graphics.Trade Review“The book is suitable for undergraduate students of computer science, mathematics, and engineering, as well as an ideal reference for researchers and professionals in computer graphics.” (Zentralblatt MATH, 1 June 2015) Table of ContentsPreface xiii 1 Basics 1 1.1 Graphics Pipeline 2 1.2 Mathematical Descriptions 4 1.3 Position 5 1.4 Distance 8 1.5 Complements and Details 11 1.5.1 Pythagorean Theorem Continued 11 1.5.2 Law of Cosines Continued 12 1.5.3 Law of Sines 13 1.5.4 Numerical Calculations 13 1.6 Exercises 14 1.6.1 Programming Exercises 16 2 Vector Algebra 17 2.1 Basic Vector Characteristics 18 2.1.1 Points Versus Vectors 20 2.1.2 Addition 20 2.1.3 Scalar Multiplication 21 2.1.4 Subtraction 22 2.1.5 Vector Calculations 22 2.1.6 Properties 24 2.1.7 Higher Dimensions 25 2.2 Two Important Products 25 2.2.1 Dot Product 25 2.2.2 Cross Product 29 2.3 Complements and Details 34 2.3.1 Vector History 34 2.3.2 More about Points Versus Vectors 35 2.3.3 Vector Spaces and Affine Spaces 36 2.4 Exercises 38 2.4.1 Programming Exercises 39 3 Vector Geometry 40 3.1 Lines and Planes 40 3.1.1 Vector Description of Lines 40 3.1.2 Vector Description of Planes 44 3.2 Distances 46 3.2.1 Point to a Line 46 3.2.2 Point to a Plane 48 3.2.3 Parallel Planes and Line to a Plane 48 3.2.4 Line to a Line 50 3.3 Angles 52 3.4 Intersections 54 3.4.1 Intersecting Lines 54 3.4.2 Lines Intersecting Planes 56 3.4.3 Intersecting Planes 57 3.5 Additional Key Applications 61 3.5.1 Intersection of Line Segments 61 3.5.2 Intersection of Line and Sphere 65 3.5.3 Areas and Volumes 66 3.5.4 Triangle Geometry 68 3.5.5 Tetrahedron 69 3.6 Homogeneous Coordinates 71 3.6.1 Two Dimensions 72 3.6.2 Three Dimensions 73 3.7 Complements and Details 75 3.7.1 Intersection of Three Planes Continued 75 3.7.2 Homogeneous Coordinates Continued 77 3.8 Exercises 79 3.8.1 Programming Exercises 82 4 Transformations 83 4.1 Types of Transformations 84 4.2 Linear Transformations 85 4.2.1 Rotation in Two Dimensions 88 4.2.2 Reflection in Two dimensions 90 4.2.3 Scaling in Two Dimensions 92 4.2.4 Matrix Properties 93 4.3 Three Dimensions 95 4.3.1 Rotations in Three Dimensions 95 4.3.2 Reflections in Three Dimensions 101 4.3.3 Scaling and Shear in Three Dimensions 102 4.4 Affine Transformations 103 4.4.1 Transforming Homogeneous Coordinates 105 4.4.2 Perspective Transformations 107 4.4.3 Transforming Normals 110 4.4.4 Summary 111 4.5 Complements and Details 112 4.5.1 Vector Approach to Reflection in an Arbitrary Plane 113 4.5.2 Vector Approach to Arbitrary Rotations 115 4.6 Exercises 121 4.6.1 Programming Exercises 123 5 Orientation 124 5.1 Cartesian Coordinate Systems 125 5.2 Cameras 132 5.2.1 Moving the Camera or Objects 134 5.2.2 Euler Angles 137 5.2.3 Quaternions 141 5.2.4 Quaternion Algebra 143 5.2.5 Rotations 145 5.2.6 Interpolation: Slerp 148 5.2.7 From Euler Angles and Quaternions to Rotation Matrices 151 5.3 Other Coordinate Systems 152 5.3.1 Non-orthogonal Axes 152 5.3.2 Polar, Cylindrical, and Spherical Coordinates 154 5.3.3 Barycentric Coordinates 157 5.4 Complements and Details 158 5.4.1 Historical Note: Descartes 158 5.4.2 Historical Note: Hamilton 158 5.4.3 Proof of Quaternion Rotation 159 5.5 Exercises 161 5.5.1 Programming Exercises 163 6 Polygons and Polyhedra 164 6.1 Triangles 164 6.1.1 Barycentric Coordinates 165 6.1.2 Areas and Barycentric Coordinates 166 6.1.3 Interpolation 171 6.1.4 Key Points in a Triangle 172 6.2 Polygons 178 6.2.1 Convexity 179 6.2.2 Angles and Area 180 6.2.3 Inside and Outside 184 6.2.4 Triangulation 187 6.2.5 Delaunay Triangulation 189 6.3 Polyhedra 192 6.3.1 Regular Polyhedra 194 6.3.2 Volume of Polyhedra 196 6.3.3 Euler’s Formula 200 6.3.4 Rotational Symmetries 202 6.4 Complements and Details 205 6.4.1 Generalized Barycentric Coordinates 205 6.4.2 Data Structures 206 6.5 Exercises 208 6.5.1 Programming Exercises 211 7 Curves and Surfaces 212 7.1 Curve Descriptions 213 7.1.1 Lagrange Interpolation 218 7.1.2 Matrix Form for Curves 222 7.2 Bézier Curves 223 7.2.1 Properties for Two-Dimensional Bézier Curves 226 7.2.2 Joining Bézier Curve Segments 228 7.2.3 Three-Dimensional Bézier Curves 229 7.2.4 Rational Bézier Curves 230 7.3 B-Splines 232 7.3.1 Linear Uniform B-Splines 233 7.3.2 Quadratic Uniform B-Splines 235 7.3.3 Cubic Uniform B-Splines 240 7.3.4 B-Spline Properties 242 7.4 Nurbs 246 7.5 Surfaces 250 7.6 Complements and Details 260 7.6.1 Adding Control Points to Bézier Curves 260 7.6.2 Quadratic B-Spline Blending Functions 262 7.7 Exercises 264 7.7.1 Programming Exercises 266 8 Visibility 267 8.1 Viewing 267 8.2 Perspective Transformation 269 8.2.1 Clipping 273 8.2.2 Interpolating the z Coordinate 275 8.3 Hidden Surfaces 278 8.3.1 Back Face Culling 281 8.3.2 Painter’s Algorithm 283 8.3.3 Z-Buffer 286 8.4 Ray Tracing 287 8.4.1 Bounding Volumes 289 8.4.2 Bounding Boxes 289 8.4.3 Bounding Spheres 291 8.5 Complements and Details 293 8.5.1 Frustum Planes 293 8.5.2 Axes for Bounding Volumes 294 8.6 Exercises 297 8.6.1 Programming Exercises 298 9 Lighting 299 9.1 Color Coordinates 299 9.2 Elementary Lighting Models 303 9.2.1 Gouraud and Phong Shading 307 9.2.2 Shadows 311 9.2.3 BRDFs in Lighting Models 315 9.3 Global Illumination 319 9.3.1 Ray Tracing 319 9.3.2 Radiosity 323 9.4 Textures 325 9.4.1 Mapping 325 9.4.2 Resolution 332 9.4.3 Procedural Textures 333 9.5 Complements and Details 335 9.5.1 Conversion between RGB and HSV 335 9.5.2 Shadows on Arbitrary Planes 336 9.5.3 Derivation of the Radiosity Equation 337 9.6 Exercises 339 9.6.1 Programming Exercises 340 10 Other Paradigms 341 10.1 Pixels 342 10.1.1 Bresenham Line Algorithm 342 10.1.2 Anti-Aliasing 345 10.1.3 Compositing 347 10.2 Noise 350 10.2.1 Random Number Generation 350 10.2.2 Distributions 351 10.2.3 Sequences of Random Numbers 353 10.2.4 Uniform and Normal Distributions 354 10.2.5 Terrain Generation 356 10.2.6 Noise Generation 357 10.3 L-Systems 361 10.3.1 Grammars 362 10.3.2 Turtle Interpretation 363 10.3.3 Analysis of Grammars 365 10.3.4 Extending L-Systems 367 10.4 Exercises 368 10.4.1 Programming Exercises 369 Appendix A Geometry and Trigonometry 370 A.1 Triangles 370 A.2 Angles 372 A.3 Trigonometric Functions 373 Appendix B Linear Algebra 376 B.1 Systems of Linear Equations 376 B.1.1 Solving the System 377 B.2 Matrix Properties 379 B.3 Vector Spaces 381 References 383 Index 387

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Book SynopsisDEMYSTIFYING DEEP LEARNING Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention, to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhereon the news, in think tanks, and occupies government policy makers all over the world and ANNs often provide the backbone for AI. Relying on an informal and succinct approach, Demystifying Deep Learni

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Book SynopsisData science is a highly interdisciplinary field, incorporating ideas from applied mathematics, statistics, probability, and computer science, as well as many other areas. This book provides an introduction to the mathematical methods that form the foundations of machine learning and data science.Table of Contents P. Drineas and M. W. Mahoney, Lectures on randomized numerical linear algebra S. J. Wright, Optimization algorithms for data analysis J. C. Duchi, Introductory lectures on stochastic optimization P.-G. Martinsson, Randomized methods for matrix computations R. Vershynin, Four lectures on probabilistic methods for data science R. Ghrist, Homological algebra and data.

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Book SynopsisFit for students just starting to build a background in mathematics, this textbook provides an introduction to numerical methods for linear algebra problems.Introduction to Numerical Linear Algebra is ideal for a flipped classroom, as it provides detailed explanations that allow students to read on their own and instructors to go beyond lecturing, assumes that the reader has taken a course on linear algebra, but reviews background as needed, and covers several topics not commonly addressed in related introductory books, including diffusion, a toy model of computed tomography, global positioning systems, the use of eigenvalues in analyzing stability of equilibria, a detailed derivation and careful motivation of the QR method for eigenvalues starting from power iteration, a discussion of the use of the SVD for assigning grades, and multigrid methods. This textbook is appropriate for undergraduate and beginning graduate students in mathematics and related fields. It can be used in the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory

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