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In the new world of international trade, the playing field is hardly level. The richer countries have relatively low trade barriers for most goods and commerce, but cling to protection for selected sectors of the economy, especially their farmers, services, and their airlines. These countries enjoy the trade advantages of having high-quality transportation and communication networks, the best technology, and the most educated workers. Developing countries, in contrast, seek to achieve equality in infrastructure, capital, and labor, but know it will take decades to catch up. In the meantime, they also feel the need to protect many of their young industries from the full force of the industrial nations, while demanding access to the markets of those countries for their agricultural and food products.
Two World Wars were sufficient to create a large void in international trade for about forty years. While a cold war and various hostilities prevailed after the late 1940s, international trade and capital flows resumed and grew remarkably. The fall of the Berlin Wall, symbolic of the end of the Cold War, intensified interest and growth in trade in the early 1990s and we find us now in a world where almost all countries feel quite free to trade with any of the others. We trade anything and everything. We trade old and new goods. We trade each other’s financial assets and we open up factories, stores, and offices almost any place we want.
Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.
Virgil's Aeneid XI is an important, yet sometimes overlooked, book which covers the funerals following the fierce fighting in Book X and a council of the Latins before they and the Trojans resume battle after the end of the truce. This edition contains a thorough Introduction which provides context for Book XI both within and beyond the rest of the poem, explores key characters such as Aeneas and Camilla, and deals with issues of metre and textual transmission. The line-by-line Commentary will be indispensable for students and instructors wishing to enhance their understanding of the poem and especially of Virgil's language and syntax. Accessible and comprehensive, the volume will help readers to appreciate features of Virgilian style as well as deepening their engagement with the content and themes of the Aeneid as a whole.
Using a modern, pedagogical approach, this textbook gives students and engineers a comprehensive and rigorous knowledge of CMOS phase-locked loop (PLL) design for a wide range of applications. It features intuitive presentation of theoretical concepts, built up gradually from their simplest form to more practical systems; broad coverage of key topics, including oscillators, phase noise, analog PLLs, digital PLLs, RF synthesizers, delay-locked loops, clock and data recovery circuits, and frequency dividers; tutorial chapters on high-performance oscillator design, covering fundamentals to advanced topologies; and extensive use of circuit simulations to teach design mentality, highlight design flaws, and connect theory with practice. Including over 200 thought-provoking examples highlighting best practices and common pitfalls, 250 end-of-chapter homework problems to test and enhance the readers' understanding, and solutions and lecture slides for instructors, this is the perfect text for senior undergraduate and graduate-level students and professional engineers who want an in-depth understanding of PLL design.
With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.