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Beecher, Penna, and Bittinger's College Algebra is known for enabling students to "see the math" through its focus on visualization and early introduction to functions. With the Fourth Edition, the authors continue to innovate by incorporating more ongoing review to help students develop their understanding and study effectively. Mid-chapter Review exercise sets have been added to give students practice in synthesizing the concepts, and new Study Summaries provide built-in tools to help them prepare for tests. The MyMathLab course (access kit required) has been expanded so that the online content is even more integrated with the text's approach, with the addition of Vocabulary, Synthesis, and Mid-chapter Review exercises from the text as well as example-based videos created by the authors.
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.
In Figuring Space Gilles Châtelet seeks to capture the problem of intuition of mobility in philosophy, mathematics and physics. This he does by means of virtuality and intensive quantities (Oresme, Leibniz), wave-particle duality and perspective diagrams, philosophy of nature and Argand's and Grassman's geometric discoveries and, finally, Faraday's, Maxwell's and Hamilton's electrophilosophy. This tumultuous relationship between mathematics, physics and philosophy is presented in terms of a comparison between intuitive practices and Discursive practices. The following concepts are treated in detail: The concept of virtuality; thought experiments; diagrams; special relativity; German Naturphilosophie and `Romantic' science. Readership: The book does not require any considerable mathematical background, but it does insist that the reader quit the common instrumental conception of language. It will interest professional philosophers, mathematicians, physicists, and even younger scientists eager to understand the `unreasonable effectiveness of mathematics'.
The Architects' Handbook provides a comprehensive range of visual and technical information covering the great majority of building types likely to be encountered by architects, designers, building surveyors and others involved in the construction industry. It is organised by building type and concentrates very much on practical examples. Including over 300 case studies, the Handbook is organised by building type and concentrates very much on practical examples. It includes: · a brief introduction to the key design considerations for each building type · numerous plans, sections and elevations for the building examples · references to key technical standards and design guidance · a comprehensive bibliography for most building types The book also includes sections on designing for accessibility, drawing practice, and metric and imperial conversion tables. To browse sample pages please see http://www.blackwellpublishing.com/architectsdata
This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.
'What is a self and how can a self come out of inanimate matter?' This is the riddle that drove Douglas Hofstadter to write this extraordinary book. In order to impart his original and personal view on the core mystery of human existence - our intangible sensation of 'I'-ness - Hofstadter defines the playful yet seemingly paradoxical notion of 'strange loop', and explicates this idea using analogies from many disciplines.
The first Situationist text to be published in the UK in 1974, ‘Leaving the 20th Century’ was Chris Gray and the English situationists’ attempt to capture and distil the vibrant anti-art, anti-capitalist energy of the original International Situationist texts (1957-74). With its loose translations and irreverent commentary, Gray and co. attempted to capture the “terrorism, wit and general megalomania” of the original publications, whilst faithfully reprinting the “photographs of girls, soldiers, bombings, comic-strip frames, maps of cities and diagrams of labyrinths, cathedrals and gardens.” From the art/anti-art beginnings, to the role of the Situationists in the worker-student insurrection of May 68’, ‘Leaving the 20th Century’ remains the definitive English pro-situ text.
Adaptive filtering is a branch of digital signal processing which enables the selective enhancement of desired elements of a signal and the reduction of undesired elements. Change detection is another kind of adaptive filtering for non-stationary signals, and is the basic tool in fault detection and diagnosis. This text takes the unique approach that change detection is a natural extension of adaptive filtering, and the broad coverage encompasses both the mathematical tools needed for adaptive filtering and change detection and the applications of the technology. Real engineering applications covered include aircraft, automotive, communication systems, signal processing and automatic control problems. The unique integration of both theory and practical applications makes this book a valuable resource combining information otherwise only available in separate sources Comprehensive coverage includes many examples and case studies to illustrate the ideas and show what can be achieved Uniquely integrates applications to airborne, automotive and communications systems with the essential mathematical tools Accompanying Matlab toolbox available on the web illustrating the main ideas and enabling the reader to do simulations using all the figures and numerical examples featured This text would prove to be an essential reference for postgraduates and researchers studying digital signal processing as well as practising digital signal processing engineers.
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019. The chapter "Supervised Human-guided Data Exploration" is published open access under a Creative Commons Attribution 4.0 International license (CC BY).