Autoconstrucci N

With the 2006 election of Michelle Bachelet as the first female president and women claiming fifty percent of her cabinet seats, the political influence of Chilean women has taken a major step forward. Despite a seemingly liberal political climate, Chile has a murky history on women's rights, and progress has been slow, tenuous, and in many cases, non-existent. Chronicling an era of unprecedented modernization and political transformation, Jadwiga E. Pieper Mooney examines the negotiations over women's rights and the politics of gender in Chile throughout the twentieth century. Centering her study on motherhood, Pieper Mooney explores dramatic changes in health policy, population paradigms, and understandings of human rights, and reveals that motherhood is hardly a private matter defined only by individual women or couples. Instead, it is intimately tied to public policies and political competitions on nation-state and international levels. The increased legitimacy of women's demands for rights, both locally and globally, has led to some improvements in gender equity. Yet feminists in contemporary Chile continue to face strong opposition from neoconservatism in the Catholic Church and a mixture of public apathy and legal wrangling over reproductive rights and health.

Modernity Disavowed is a pathbreaking study of the cultural, political, and philosophical significance of the Haitian Revolution (1791–1804). Revealing how the radical antislavery politics of this seminal event have been suppressed and ignored in historical and cultural records over the past two hundred years, Sibylle Fischer contends that revolutionary antislavery and its subsequent disavowal are central to the formation and understanding of Western modernity. She develops a powerful argument that the denial of revolutionary antislavery eventually became a crucial ingredient in a range of hegemonic thought, including Creole nationalism in the Caribbean and G. W. F. Hegel’s master-slave dialectic. Fischer draws on history, literary scholarship, political theory, philosophy, and psychoanalytic theory to examine a range of material, including Haitian political and legal documents and nineteenth-century Cuban and Dominican literature and art. She demonstrates that at a time when racial taxonomies were beginning to mutate into scientific racism and racist biology, the Haitian revolutionaries recognized the question of race as political. Yet, as the cultural records of neighboring Cuba and the Dominican Republic show, the story of the Haitian Revolution has been told as one outside politics and beyond human language, as a tale of barbarism and unspeakable violence. From the time of the revolution onward, the story has been confined to the margins of history: to rumors, oral histories, and confidential letters. Fischer maintains that without accounting for revolutionary antislavery and its subsequent disavowal, Western modernity—including its hierarchy of values, depoliticization of social goals having to do with racial differences, and privileging of claims of national sovereignty—cannot be fully understood.

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

Since Tate Modern opened, the Turbine Hall has hosted some of the most memorable and acclaimed site-specific art installations of the twenty-first century, reaching an audience of millions. This book is published to accompany the inaugaral Hyundai Commission, the first in a new series of annual exhibitions that will give renowned international contemporary artists an opportunity to create new work for one of the world's most iconic museum spaces. Abraham Cruzvillegas (b.1968), one of the key figures to have emerged in Mexico among a new wave of conceptual artists, is best known for his sculptural works made from local found objects and materials. He has titled this body of work autoconstruccion or 'self-construction'.

As a linguist, Julia Kristeva has pioneered a revolutionary theory of the sign in its relation to social and political emancipation; as a practicing psychoanalyst, she has produced work on the nature of the human subject and sexuality, and on the "new maladies" of today's neurotic. The Portable Kristeva is the only fully comprehensive compilation of Kristeva's key writings. The second edition includes added material from Kristeva's most important works of the past five years, including The Sense and Non-Sense of Revolt, Intimate Revolt, and Hannah Arendt. Editor Kelly Oliver has also added new material to the introduction, summarizing Kristeva's latest intellectual endeavors and updating the bibliography.

This groundbreaking and irreverent history of motherhood is worth a hundred advice books for any mother who’s ever been made to feel guilty or frazzled by society’s impossible expectations. Analyzing data from the psychoanalyst’s couch to the hidden history of wet nursing, psychologist Shari L. Thurer wends her way from the Stone Age to the age of Hillary Rodham Clinton, painting a vivid, often frightening picture of life for mothers and children in a time when their roles were constructed by men. Along the way, she debunks myth after myth—exposing the not-so-golden ages of Classical Greece and the Italian Renaissance, and revealing the pervasive ideal of Dr. Spock’s selfless, stay-at-home mother as the historical aberration it actually was. A work of impassioned scholarship and astonishing range, The Myths of Motherhood does nothing less than recast our conception of good mothering.

Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.

The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about