Trevor N. Wedman seeks to understand the key assumptions underlying modern legal theory. Going back to Hobbes, but also making use of the developments in the theory of action and language philosophy over the past century, he breaks down the static conception of the state into one dependent on the actions and reflections of individuals, i.e., its citizens. He develops a social ontological theory of the law, in which the law is not taken as a mere given, but as an institutional fact. He criticizes both the Kelsenian conception of the Basic Norm and the Hartian notion of the Rule of Recognition as failing to account for the agency of individuals. The author turns to the work of one of Kelsen's contemporaries, Felix Somlo, in order to develop an alternative conception of the law that operates not from the top down, but from the bottom up. In this way, the law itself comes into focus as that which results from the reasoned jurisprudential reflection on the reality of meanings and actions.

Inverting the Norm describes how a few Christian congregations in apartheid South Africa achieved racial integration despite the state's legal enforcement of segregation. The book analyzes how this paradoxical racial integration, alongside state segregation, relates to historical shifts in global and national norms.

Discrete Signals and Inverse Problems examines fundamental concepts necessary to engineers and scientists working with discrete signal processing and inverse problem solving, and places emphasis on the clear understanding of algorithms within the context of application needs. Based on the original ‘Introduction to Discrete Signals and Inverse Problems in Civil Engineering’, this expanded and enriched version: combines discrete signal processing and inverse problem solving in one book covers the most versatile tools that are needed to process engineering and scientific data presents step-by-step ‘implementation procedures’ for the most relevant algorithms provides instructive figures, solved examples and insightful exercises Discrete Signals and Inverse Problems is essential reading for experimental researchers and practicing engineers in civil, mechanical and electrical engineering, non-destructive testing and instrumentation. This book is also an excellent reference for advanced undergraduate students and graduate students in engineering and science.

The first general theory of the influence of norms--moral, legal and social--on genocide and mass atrocity. How can we explain--and prevent--such large-scale atrocities as the Holocaust? In Unconscionable Crimes, Paul Morrow presents the first general theory of the influence of norms--moral, legal and social--on genocide and mass atrocity. After offering a clear overview of norms and norm transformation, rooted in recent work in moral and political philosophy, Morrow examines numerous twentieth-century cases of mass atrocity, drawing on documentary and testimonial sources to illustrate the influence of norms before, during, and after such crimes.

Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets Key FeaturesBecome familiar with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesBook Description C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems. What you will learnExplore how to load and preprocess various data types to suitable C++ data structuresEmploy key machine learning algorithms with various C++ librariesUnderstand the grid-search approach to find the best parameters for a machine learning modelImplement an algorithm for filtering anomalies in user data using Gaussian distributionImprove collaborative filtering to deal with dynamic user preferencesUse C++ libraries and APIs to manage model structures and parametersImplement a C++ program to solve image classification tasks with LeNet architectureWho this book is for You will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.