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Category Archives: Books

Books worth a look

Book Memo: “From Human Attention to Computational Attention”

15 Thursday Nov 2018

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A Multidisciplinary Approach
This both accessible and exhaustive book will help to improve modeling of attention and to inspire innovations in industry. It introduces the study of attention and focuses on attention modeling, addressing such themes as saliency models, signal detection and different types of signals, as well as real-life applications. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering and computer science, amongst other disciplines. What is attention? We all pay attention every single moment of our lives. Attention is how the brain selects and prioritizes information. The study of attention has become incredibly complex and divided: this timely volume assists the reader by drawing together work on the computational aspects of attention from across the disciplines. Those working in the field as engineers will benefit from this book’s introduction to the psychological and biological approaches to attention, and neuroscientists can learn about engineering work on attention. The work features practical reviews and chapters that are quick and easy to read, as well as chapters which present deeper, more complex knowledge. Everyone whose work relates to human perception, to image, audio and video processing will find something of value in this book, from students to researchers and those in industry.
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Book Memo: “The World of Open Data”

14 Wednesday Nov 2018

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Concepts, Methods, Tools and Experiences
This book discusses the latest developments in the field of open data. The opening of data by public organizations has the potential to improve the public sector, inspire business innovation, and establish transparency. With this potential comes unique challenges; these developments impact the operation of governments as well as their relationship with private sector enterprises and society. Changes at the technical, organizational, managerial, and political level are taking place, which, in turn, impact policy-making and traditional institutional structures. This book contributes to the systematic analysis and publication of cutting-edge methods, tools, and approaches for more efficient data sharing policies, practices, and further research. Topics discussed include an introduction to open data, the open data landscape, the open data life cycle, open data policies, organizational issues, interoperability, infrastructure, business models, open data portal evaluation, and research directions, best practices, and guidelines. Written to address different perspectives, this book will be of equal interest to students and researchers, ICT industry staff, practitioners, policy makers and public servants.

Book Memo: “Handbook of Signal Processing Systems”

13 Tuesday Nov 2018

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In this new edition of the Handbook of Signal Processing Systems, many of the chapters from the previous editions have been updated, and several new chapters have been added. The new contributions include chapters on signal processing methods for light field displays, throughput analysis of dataflow graphs, modeling for reconfigurable signal processing systems, fast Fourier transform architectures, deep neural networks, programmable architectures for histogram of oriented gradients processing, high dynamic range video coding, system-on-chip architectures for data analytics, analysis of finite word-length effects in fixed-point systems, and models of architecture. There are more than 700 tables and illustrations; in this edition over 300 are in color. This new edition of the handbook is organized in three parts. Part I motivates representative applications that drive and apply state-of-the art methods for design and implementation of signal processing systems; Part II discusses architectures for implementing these applications; and Part III focuses on compilers, as well as models of computation and their associated design tools and methodologies.

Book Memo: “A Beginners Guide for Machine Learning”

12 Monday Nov 2018

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This book includes basic concepts of Machine Learning. When we get Processing from given input and output then it’s called Machine Learning. There are two types of learning as Supervised and Unsupervised Learning. This book is helpful to learn concepts in machine learning. This is just beginners guide for machine learning. Machine Learning is the need of today’s world. We can execute ML programs in Python or R programming.

Book Memo: “Open Data Exposed”

11 Sunday Nov 2018

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The main objectives of this book are to expose key aspects that have a relevance when dealing with open data viewed from different perspectives and to provide appealing examples of how open data is implemented worldwide. The concept of open data as we know it today is the result of many different initiatives, both of a legislative and non-legislative nature, and promoted by a wide range of actors. Numerous regulatory antecedents to foster the concept of open data and embed it in national and international policy agendas have been undertaken on both sides of the Atlantic, as well as at a supranational level. The book highlights a number of the efforts made to promote open data in Europe, Asia and the United States. In addition to new insights, practical guidance and multiple disciplinary perspectives on open data, the book also addresses the transformation of current developments towards open data, which may be referred to as the democratisation of data. This book will support open data practitioners as well as open data scholars in their endeavours to promote open data implementation and research.

Book Memo: “Fundamental Statistical Inference”

10 Saturday Nov 2018

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A Computational Approach
This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student’s t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided. The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution.

Book Memo: “Reproducible Finance with R”

09 Friday Nov 2018

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Code Flows and Shiny Apps for Portfolio Analysis
Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

Book Memo: “Complex Agent-Based Models”

08 Thursday Nov 2018

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This book offers a thorough introduction to the highly promising complex agent-based approach to economics, in which agent-based models (ABMs) are used to represent economic systems as complex and evolving systems composed of heterogeneous agents of limited rationality who interact with each other, generating the system’s emergent properties in the process. This approach represents a response to the limitations of the dominant theory in economics, which does not consider the possibility of a major crisis, and to the inability of dynamic stochastic general equilibrium theory to generate empirically falsifiable propositions. In the new perspective, the focus is on identifying the elements of instability rather than the triggering event. As the theory of complexity demonstrates, the interactions of heterogeneous agents produce non-linearity: this puts an end to the age of certainties. With ABMs, the methodology is ‘from the bottom up’. The individual parameters and their distribution are estimated, and then evaluated to verify whether aggregate regularities emerge on the whole. In short, not only micro, but also meso and macro empirical validation are employed. Moreover, it shows that the mantra of growth should be supplanted by the concept of a growth¿. Given its depth of coverage, the book will enable students at the undergraduate and Master’s level to gain a firm grasp of this important emerging approach.

Book Memo: “Fourier Analysis of Economic Phenomena”

07 Wednesday Nov 2018

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This is the first monograph that discusses in detail the interactions between Fourier analysis and dynamic economic theories, in particular, business cycles.Many economic theories have analyzed cyclical behaviors of economic variables. In this book, the focus is on a couple of trials: (1) the Kaldor theory and (2) the Slutsky effect. The Kaldor theory tries to explain business fluctuations in terms of nonlinear, 2nd-order ordinary differential equations (ODEs). In order to explain periodic behaviors of a solution, the Hopf-bifurcation theorem frequently plays a key role. Slutsky’s idea is to look at the periodic movement as an overlapping effect of random shocks. The Slutsky process is a weakly stationary process, the periodic (or almost periodic) behavior of which can be analyzed by the Bochner theorem. The goal of this book is to give a comprehensive and rigorous justification of these ideas. Therefore, the aim is first to give a complete theory that supports the Hopf theorem and to prove the existence of periodic solutions of ODEs; and second to explain the mathematical structure of the Bochner theorem and its relation to periodic (or almost periodic) behaviors of weakly stationary processes.Although these two targets are the principal ones, a large number of results from Fourier analysis must be prepared in order to reach these goals. The basic concepts and results from classical as well as generalized Fourier analysis are provided in a systematic way.Prospective readers are assumed to have sufficient knowledge of real, complex analysis. However, necessary economic concepts are explained in the text, making this book accessible even to readers without a background in economics.

Book Memo: “BERRU Predictive Modeling”

06 Tuesday Nov 2018

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Best Estimate Results with Reduced Uncertainties
This book addresses the experimental calibration of best-estimate numerical simulation models. The results of measurements and computations are never exact. Therefore, knowing only the nominal values of experimentally measured or computed quantities is insufficient for applications, particularly since the respective experimental and computed nominal values seldom coincide. In the author’s view, the objective of predictive modeling is to extract ‘best estimate’ values for model parameters and predicted results, together with ‘best estimate’ uncertainties for these parameters and results. To achieve this goal, predictive modeling combines imprecisely known experimental and computational data, which calls for reasoning on the basis of incomplete, error-rich, and occasionally discrepant information. The customary methods used for data assimilation combine experimental and computational information by minimizing an a priori, user-chosen, ‘cost functional’ (usually a quadratic functional that represents the weighted errors between measured and computed responses). In contrast to these user-influenced methods, the BERRU (Best Estimate Results with Reduced Uncertainties) Predictive Modeling methodology developed by the author relies on the thermodynamics-based maximum entropy principle to eliminate the need for relying on minimizing user-chosen functionals, thus generalizing the ‘data adjustment’ and/or the ‘4D-VAR’ data assimilation procedures used in the geophysical sciences. The BERRU predictive modeling methodology also provides a ‘model validation metric’ which quantifies the consistency (agreement/disagreement) between measurements and computations. This ‘model validation metric’ (or ‘consistency indicator’) is constructed from parameter covariance matrices, response covariance matrices (measured and computed), and response sensitivities to model parameters.