Mes Cr Ations

Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

L'orf?vrerie alg?rienne et tunisienne.

A powerful and impassioned historical account of the largest successful revolt by enslaved people in history: the Haitian Revolution of 1791–1803 “One of the seminal texts about the history of slavery and abolition.... Provocative and empowering.” —The New York Times Book Review The Black Jacobins, by Trinidadian historian C. L. R. James, was the first major analysis of the uprising that began in the wake of the storming of the Bastille in France and became the model for liberation movements from Africa to Cuba. It is the story of the French colony of San Domingo, a place where the brutality of plantation owners toward enslaved people was horrifyingly severe. And it is the story of a charismatic and barely literate enslaved person named Toussaint L’Ouverture, who successfully led the Black people of San Domingo against successive invasions by overwhelming French, Spanish, and English forces—and in the process helped form the first independent post-colonial nation in the Caribbean. With a new introduction (2023) by Professor David Scott.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.