The studies by Cyrille Vogel (1919-1982) collected here provide a detailed exposition of the penitential system of the Latin Church and its evolution during the Middle Ages. They complement in this way the general treatment of his books and document the stages of the system's development - from the early forms of Late Antiquity, to the tariffed system that emerged in the early Middle Ages, and its eventual replacement by the practices of modern times. The work is based on the systematic exploitation and analysis of all available sources, archeological as well as literary and hagiographic, and on careful attention to their dating; access to this store of material will now be facilitated by the detailed indexes to the present volume. Les études de Cyrille Vogel (1919-1982) rassemblées ici, sont un exposé détaillé du système pénitenciaire de l’Eglise Latine et de son évolution au cours du moyen âge. Elles viennent ainsi en complément de ses ouvrages et documentent les différents stades du développement de ce système - des premières formes de pénitence non-réitérable durant l’antiquité tardive, au système tarifaire qui fit surface au début du moyan-âge, jusqu’aux pratiques modernes qui finirent par le remplacer. Cet ouvrage repose sur l’exploitation et l’analyse systèmatique de l’ensemble des sources disponibles, archéologiques ainsi que littéraires et hagiographiques, et sur une attention minutieuse quand aux dates qui leur ont été assignées; l’accès à cette source de documentation sera dorénavant facilité par les indexes détaillés contenus dans le présent volume.
This unique volume collects articles and contributions to edited books published throughout his distinguished career by Professor Cyrille Fijnaut, one of the world's leading experts in the fields of organised crime, security and criminology. It makes clear what issues the author systematically explored over the years and how he helped to shape the fields in which he has worked, and continues to work. The texts, reflecting the author's profound understanding of these complex fields and wealth of experience on a practical level, are presented according to topic. In addition, the volume offers English translations of seminal articles published originally in Dutch, thus making these important texts accessible to international scholars for the first time. The volume thus constitutes a unique and indispensable resource for scholars and practitioners, inside and outside the Netherlands.
Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations Book Description Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. What you will learn Master all features of the Jupyter Notebook Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments Visualize data and create interactive plots in the Jupyter Notebook Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Simulate deterministic and stochastic dynamical systems in Python Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory Who this book is for This book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
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