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Materializing the Web of Linked Data
by Nikolaos Konstantinou Dimitrios-Emmanuel SpanosThis book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data. It presents how Linked Data is used for web-scale data integration, information management and search. Special emphasis is given to the publication of Linked Data from relational databases as well as from real-time sensor data streams. The authors also trace the transformation from the document-based World Wide Web into a Web of Data. Materializing the Web of Linked Data is addressed to researchers and professionals studying software technologies, tools and approaches that drive the Linked Data ecosystem, and the Web in general.
Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering (The Materials Research Society Series)
by Stefan SandfeldThis text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced are implemented “from scratch” using Python and NumPy. The book starts with an introduction to statistics and probabilities, explaining important concepts such as random variables and probability distributions, Bayes’ theorem and correlations, sampling techniques, and exploratory data analysis, and puts them in the context of materials science and engineering. Therefore, it serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers. The second part provides an in-depth introduction of (statistical) machine learning. It begins with outlining fundamental concepts and proceeds to explore a variety of supervised learning techniques for regression and classification, including advanced methods such as kernel regression and support vector machines. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) and clustering techniques. Additionally, feature engineering, feature importance, and cross-validation are introduced. The final part on neural networks and deep learning aims to promote an understanding of these methods and dispel misconceptions that they are a “black box”. The complexity gradually increases until fully connected networks can be implemented. Advanced techniques and network architectures, including GANs, are implemented “from scratch” using Python and NumPy, which facilitates a comprehensive understanding of all the details and enables the user to conduct their own experiments in Deep Learning.
Materials Discovery and Design: By Means of Data Science and Optimal Learning (Springer Series in Materials Science #280)
by Turab Lookman Stephan Eidenbenz Frank Alexander Cris BarnesThis book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.
Materials with Complex Behaviour
by Holm Altenbach Lucas F. SilvaThe increasing demand for materials with superb properties in all types of technical applications requires the development of advanced materials and respective tools to characterise and predict the material properties and behaviour. This monograph highlights the latest developments and trends in advanced materials and their properties, the modelling and simulation of non-classical materials and structures, and new technologies for joining materials.
Math Adventures with Python: An Illustrated Guide to Exploring Math with Code
by Peter FarrellLearn math by getting creative with code! Use the Python programming language to transform learning high school-level math topics like algebra, geometry, trigonometry, and calculus!Math Adventures with Python will show you how to harness the power of programming to keep math relevant and fun. With the aid of the Python programming language, you'll learn how to visualize solutions to a range of math problems as you use code to explore key mathematical concepts like algebra, trigonometry, matrices, and cellular automata.Once you've learned the programming basics like loops and variables, you'll write your own programs to solve equations quickly, make cool things like an interactive rainbow grid, and automate tedious tasks like factoring numbers and finding square roots. You'll learn how to write functions to draw and manipulate shapes, create oscillating sine waves, and solve equations graphically. You'll also learn how to: - Draw and transform 2D and 3D graphics with matrices- Make colorful designs like the Mandelbrot and Julia sets with complex numbers- Use recursion to create fractals like the Koch snowflake and the Sierpinski triangle- Generate virtual sheep that graze on grass and multiply autonomously - Crack secret codes using genetic algorithms As you work through the book's numerous examples and increasingly challenging exercises, you'll code your own solutions, create beautiful visualizations, and see just how much more fun math can be!
Math and Architectures of Deep Learning
by Krishnendu ChaudhuryShine a spotlight into the deep learning &“black box&”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively.Inside Math and Architectures of Deep Learning you will find: Math, theory, and programming principles side by side Linear algebra, vector calculus and multivariate statistics for deep learning The structure of neural networks Implementing deep learning architectures with Python and PyTorch Troubleshooting underperforming models Working code samples in downloadable Jupyter notebooks The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you&’ll peer inside the &“black box&” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Foreword by Prith Banerjee. About the technology Discover what&’s going on inside the black box! To work with deep learning you&’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you&’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective. About the book Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You&’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research. What's inside The core design principles of neural networks Implementing deep learning with Python and PyTorch Regularizing and optimizing underperforming models About the reader Readers need to know Python and the basics of algebra and calculus. About the author Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. Table of Contents 1 An overview of machine learning and deep learning 2 Vectors, matrices, and tensors in machine learning 3 Classifiers and vector calculus 4 Linear algebraic tools in machine learning 5 Probability distributions in machine learning 6 Bayesian tools for machine learning 7 Function approximation: How neural networks model the world 8 Training neural networks: Forward propagation and backpropagation 9 Loss, optimization, and regularization 10 Convolutions in neural networks 11 Neural networks for image classification and object detection 12 Manifolds, homeomorphism, and neural networks 13 Fully Bayes model parameter estimation 14 Latent space and generative modeling, autoencoders, and variational autoencoders A Appendix
Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing
by Tim P. ChartierAn inviting collection of fun, hands-on applications in mathematics and computingThis book provides a fun, hands-on approach to learning how mathematics and computing relate to the world around us and help us to better understand it. How can reposting on Twitter kill a movie's opening weekend? How can you use mathematics to find your celebrity look-alike? What is Homer Simpson’s method for disproving Fermat’s Last Theorem? Each topic in this refreshingly inviting book illustrates a famous mathematical algorithm or result--such as Google’s PageRank and the traveling salesman problem--and the applications grow more challenging as you progress through the chapters. But don’t worry, helpful solutions are provided each step of the way.Math Bytes shows you how to do calculus using a bag of chocolate chips, and how to prove the Euler characteristic simply by doodling. Generously illustrated in color throughout, this lively and entertaining book also explains how to create fractal landscapes with a roll of the dice, pick a competitive bracket for March Madness, decipher the math that makes it possible to resize a computer font or launch an Angry Bird--and much, much more. All of the applications are presented in an accessible and engaging way, enabling beginners and advanced readers alike to learn and explore at their own pace--a bit and a byte at a time.
Math for Deep Learning: What You Need to Know to Understand Neural Networks
by Ronald T. KneuselMath for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You&’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You&’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.In addition you&’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
Math for Programmers: 3D graphics, machine learning, and simulations with Python
by Paul OrlandIn Math for Programmers you&’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today&’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you&’ll master the key Python libraries used to turn them into real-world software applications.Summary To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today&’s hottest programming fields. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code! About the book In Math for Programmers you&’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today&’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you&’ll master the key Python libraries used to turn them into real-world software applications. What's inside Vector geometry for computer graphics Matrices and linear transformations Core concepts from calculus Simulation and optimization Image and audio processing Machine learning algorithms for regression and classification About the reader For programmers with basic skills in algebra. About the author Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land. Table of Contents 1 Learning math with code PART I - VECTORS AND GRAPHICS 2 Drawing with 2D vectors 3 Ascending to the 3D world 4 Transforming vectors and graphics 5 Computing transformations with matrices 6 Generalizing to higher dimensions 7 Solving systems of linear equations PART 2 - CALCULUS AND PHYSICAL SIMULATION 8 Understanding rates of change 9 Simulating moving objects 10 Working with symbolic expressions 11 Simulating force fields 12 Optimizing a physical system 13 Analyzing sound waves with a Fourier series PART 3 - MACHINE LEARNING APPLICATIONS 14 Fitting functions to data 15 Classifying data with logistic regression 16 Training neural networks
Math for Security: From Graphs and Geometry to Spatial Analysis
by Daniel ReillyUse applied math to map fire stations, develop facial recognition software, solve the art gallery problem and more in this hands-on, real-world infosec book.Explore the intersection of mathematics and computer security with this engaging and accessible guide.Math for Security will equip you with essential tools to tackle complex security problems head on. All you need are some basic programming skills. Once you&’ve set up your development environment and reviewed the necessary Python syntax and math notation in the early chapters, you&’ll dive deep into practical applications, leveraging the power of math to analyze networks, optimize resource distribution, and much more. In the book&’s final chapters, you&’ll take your projects from proof of concepts to viable applications and explore options for delivering them to end users.As you work through various security scenarios, you&’ll:Employ packet analysis and graph theory to detect data exfiltration attempts in a networkPredict potential targets and find weaknesses in social networks with Monte Carlo simulationsUse basic geometry and OpenCell data to triangulate a phone&’s location without GPSApply computational geometry to Voronoi diagrams for use in emergency service planningTrain a facial recognition system with machine learning for real-time identity verificationUse spatial analysis to distribute physical security features effectively in an art galleryWhether you&’re an aspiring security professional, a social network analyst, or an innovator seeking to create cutting-edge security solutions, this book will empower you to solve complex problems with precision and confidence. Embrace the intricate world of math as your secret weapon in computer security!Covers Python 3.x
Math Physics Foundation of Advanced Remote Sensing Digital Image Processing
by Lei Yan Hongying Zhao Yi Lin Yanbiao SunThis book focuses on the mathematical and physical foundations of remote sensing digital image processing and introduces key algorithms utilized in this area. The book fully introduces the basic mathematical and physical process of digital imaging, the basic theory and algorithm of pixel image processing, and the higher-order image processing algorithm and its application. This book skillfully and closely integrates theory, algorithms, and applications, making it simple for readers to understand and use. Researchers and students working in the fields of remote sensing, computer vision, geographic information science, electronic information, etc., can profit from this book. For their work and research in digital image processing, they can master the fundamentals of imaging and image processing techniques.
Mathematica Cookbook
by Sal ManganoMathematica Cookbook helps you master the application's core principles by walking you through real-world problems. Ideal for browsing, this book includes recipes for working with numerics, data structures, algebraic equations, calculus, and statistics. You'll also venture into exotic territory with recipes for data visualization using 2D and 3D graphic tools, image processing, and music. Although Mathematica 7 is a highly advanced computational platform, the recipes in this book make it accessible to everyone -- whether you're working on high school algebra, simple graphs, PhD-level computation, financial analysis, or advanced engineering models. Learn how to use Mathematica at a higher level with functional programming and pattern matching Delve into the rich library of functions for string and structured text manipulation Learn how to apply the tools to physics and engineering problems Draw on Mathematica's access to physics, chemistry, and biology data Get techniques for solving equations in computational finance Learn how to use Mathematica for sophisticated image processing Process music and audio as musical notes, analog waveforms, or digital sound samples
Mathematica Data Visualization
by Nazmus SaquibIf you are planning to create data analysis and visualization tools in the context of science, engineering, economics, or social science, then this book is for you. With this book, you will become a visualization expert, in a short time, using Mathematica.
Mathematica for Bioinformatics: A Wolfram Language Approach To Omics
by George MiasThis book offers a comprehensive introduction to using Mathematica and the Wolfram Language for Bioinformatics. The chapters build gradually from basic concepts and the introduction of the Wolfram Language and coding paradigms in Mathematica, to detailed worked examples derived from typical research applications using Wolfram Language code. The coding examples range from basic sequence analysis, accessing genomic databases, differential gene expression, and machine learning implementations to time series analysis of longitudinal omics experiments, multi-omics integration and building dynamic interactive bioinformatics tools using the Wolfram Language. The topics address the daily bioinformatics needs of a broad audience: experimental users looking to understand and visualize their data, beginner bioinformaticians acquiring coding expertise in providing biological research solutions, and practicing expert bioinformaticians working on omics who wish to expand their toolset to include the Wolfram Language.
Mathematica® in Action
by Stan WagonPlenty of examples and case studies utilize Mathematica 7's newest tools, such as dynamic manipulations and adaptive three-dimensional plotting. Emphasizes the breadth of Mathematica and the impressive results of combining techniques from different areas. Whenever possible, the book shows how Mathematica can be used to discover new things. Striking examples include the design of a road on which a square wheel bike can ride, the design of a drill that can drill square holes, and new and surprising formulas for p. Visualization is emphasized throughout, with finely crafted graphics in each chapter.
Mathematical Adventures in Performance Analysis
by Eitan Bachmat This book describes problems in the field of performance analysis, primarily the study of storage systems and the diverse mathematical techniques that are required for solving them. Topics covered include best practices for scheduling I/O requests to a disk drive, how this problem is related to airplane boarding, and how both problems can be modeled using space-time geometry. Also provided is an explanation of how Riemann's proof of the analytic continuation and functional equation of the Riemann zeta function can be used to analyze express line queues in a minimarket. Overall, the book displays the surprising relevance of abstract mathematics that is not usually associated with applied mathematics topics. Advanced undergraduate students or graduate students with an interest in the applications of mathematics will find this book to be a useful resource. It will also be of interest to professional mathematicians who want exposure to the surprising ways that theoretical mathematics may be applied to engineering problems. To encourage further study, each chapter ends with notes pointing to various related topics that the reader may want pursue. This mathematically rigorous work was noted in the news section of the journal Nature, and in popular media such as New Scientist, The Wall Street Journal, The Guardian, and USA Today.
Mathematical Analysis II: ICRAPAM 2018, New Delhi, India, October 23–25 (Springer Proceedings in Mathematics & Statistics #307)
by Vijay Gupta P. N. Agrawal Ana Maria Acu Naokant DeoThis book collects original research papers and survey articles presented at the International Conference on Recent Advances in Pure and Applied Mathematics (ICRAPAM), held at Delhi Technological University, India, on 23–25 October 2018. Divided into two volumes, it discusses major topics in mathematical analysis and its applications, and demonstrates the versatility and inherent beauty of analysis. It also shows the use of analytical techniques to solve problems and, wherever possible, derive their numerical solutions. This volume addresses major topics, such as multi-objective optimization problems, impulsive differential equations, mathematical modelling, fuzzy mathematics, graph theory, and coding theory. It is a valuable resource to students as well as researchers in mathematical sciences.
Mathematical Analysis of Continuum Mechanics and Industrial Applications III: Proceedings of the International Conference CoMFoS18 (Mathematics for Industry #34)
by Hiromichi Itou Shiro Hirano Masato Kimura Victor A. Kovtunenko Alexandr M. KhludnevThis book focuses on mathematical theory and numerical simulation related to various areas of continuum mechanics, such as fracture mechanics, (visco)elasticity, optimal shape design, modelling of earthquakes and Tsunami waves, material structure, interface dynamics and complex systems. Written by leading researchers from the fields of applied mathematics, physics, seismology, engineering, and industry with an extensive knowledge of mathematical analysis, it helps readers understand how mathematical theory can be applied to various phenomena, and conversely, how to formulate actual phenomena as mathematical problems. This book is the sequel to the proceedings of the International Conference of Continuum Mechanics Focusing on Singularities (CoMFoS) 15 and CoMFoS16.
Mathematical and Computational Modelling of Covid-19 Transmission (River Publishers Series in Mathematical, Statistical and Computational Modelling for Engineering)
by Nita H. ShahInfectious diseases are leading threats and are of highest risk to the human population globally. Over the last two years, we saw the transmission of Covid-19. Millions of people died or were forced to live with a disability. Mathematical models are effective tools that enable analysis of relevant information, simulate the related process and evaluate beneficial results. They can help to make rational decisions to lead toward a healthy society. Formulation of mathematical models for a pollution-free environment is also very important for society. To determine the system which can be modelled, we need to formulate the basic context of the model underlying some necessary assumptions. This describes our beliefs in terms of the mathematical language of how the world functions. This book addresses issues during the Covid phase and post-Covid phase. It analyzes transmission, impact of coinfections, and vaccination as a control or to decrease the intensity of infection. It also talks about the violence and unemployment problems occurring during the post-Covid period. This book will help societal stakeholders to resume normality slowly and steadily.
Mathematical and Computational Oncology: Second International Symposium, ISMCO 2020, San Diego, CA, USA, October 8–10, 2020, Proceedings (Lecture Notes in Computer Science #12508)
by George Bebis Max Alekseyev Heyrim Cho Jana Gevertz Maria Rodriguez MartinezThis book constitutes the refereed proceedings of the Second International Symposium on Mathematical and Computational Oncology, ISMCO 2020, which was supposed to be held in San Diego, CA, USA, in October 2020, but was instead held virtually due to the COVID-19 pandemic.The 6 full papers and 4 short papers presented together with 1 invited talk were carefully reviewed and selected from 28 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; general cancer computational biology; and posters.
Mathematical and Computational Oncology: First International Symposium, ISMCO 2019, Lake Tahoe, NV, USA, October 14–16, 2019, Proceedings (Lecture Notes in Computer Science #11826)
by George Bebis Takis Benos Ken Chen Katharina Jahn Ernesto LimaThis book constitutes the refereed proceedings of the First International Symposium on Mathematical and Computational Oncology, ISMCO'2019, held in Lake Tahoe, NV, USA, in October 2019. The 7 full papers presented were carefully reviewed and selected from 30 submissions. The papers are organized in topical sections named: Tumor evolvability and intra-tumor heterogeneity; Imaging and scientific visualization for cancer research; Statistical methods and data mining for cancer research (SMDM); Spatio-temporal tumor modeling and simulation (STTMS).
Mathematical and Computational Oncology: Third International Symposium, ISMCO 2021, Virtual Event, October 11–13, 2021, Proceedings (Lecture Notes in Computer Science #13060)
by George Bebis Terry Gaasterland Mamoru Kato Mohammad Kohandel Kathleen WilkieThis book constitutes the refereed proceedings of the Third International Symposium on Mathematical and Computational Oncology, ISMCO 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually.The 3 full papers and 4 short papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; spatio-temporal tumor modeling and simulation; general cancer computational biology; mathematical modeling for cancer research; computational methods for anticancer drug development.
Mathematical and Engineering Methods in Computer Science
by Tomáš Vojnar Jan KofroňThis volume contains the post-conference proceedings of the 10th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science, MEMICS 2015, held in Telč, Czech Republic, in October 2015. The 10 thoroughly revised full papers were carefully selected out of 25 submissions and are presented together with 3 invited papers. The topics covered include: security and safety, bioinformatics, recommender systems, high-performance and cloud computing, and non-traditional computational models (quantum computing, etc. ).
Mathematical and Numerical Modeling of the Cardiovascular System and Applications (SEMA SIMAI Springer Series #16)
by Daniele Boffi Luca F. Pavarino Gianluigi Rozza Simone Scacchi Christian VergaraThe book comprises contributions by some of the most respected scientists in the field of mathematical modeling and numerical simulation of the human cardiocirculatory system. It covers a wide range of topics, from the assimilation of clinical data to the development of mathematical and computational models, including with parameters, as well as their efficient numerical solution, and both in-vivo and in-vitro validation. It also considers applications of relevant clinical interest. This book is intended for graduate students and researchers in the field of bioengineering, applied mathematics, computer, computational and data science, and medicine wishing to become involved in the highly fascinating task of modeling the cardiovascular system.
Mathematical Approaches to Polymer Sequence Analysis and Related Problems
by Renato BruniAn edited volume describing the latest developments in approaching the problem of polymer sequence analysis, with special emphasis on the most relevant biopolymers (peptides and DNA) but not limited to them. The chapters will include peptide sequence analysis, DNA sequence analysis, analysis of biopolymers and nonpolymers, sequence alignment problems, and more.