Civil Rights
Movements, leaders, victories and the continuing fight for equality.
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
In 1968 Shirley Chisholm became the first Black woman elected to the United States Congress. In 1972 she launched a campaign for the Democratic presidential nomination, breaking another political barrier.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
In machine learning, manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space. For example, a facial recognition system may not need to classify any possible image, but only the subset of images that contain faces. The technique of manifold learning assumes that the relevant subset of data comes from a manifold, a mathematical structure with useful properties. The technique also assumes that the function to be learned is smooth: data with different labels are not likely to be close together, and so the labeling function should not change quickly in areas where there are likely to be many data points. Because of this assumption, a manifold regularization algorithm can use unlabeled data to inform where the learned function is allowed to change quickly and where it is not, using an extension of the technique of Tikhonov regularization. Manifold regularization algorithms can extend supervised learning algorithms in semi-supervised learning and transductive learning settings, where unlabeled data are available. The technique has been used for applications including medical imaging, geographical imaging, and object recognition.
Nonlinear dimensionality reduction (NLDR), also known as manifold learning, is any of various related techniques that aim to project high-dimensional data, potentially existing across non-linear manifolds (non-affine subspaces) which cannot be adequately captured by linear decomposition methods, onto lower-dimensional latent manifolds, with the goal of either visualizing the data in the low-dimensional space, or learning the mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa) itself. The techniques described below can be understood as generalizations of linear decomposition methods used for dimensionality reduction, such as singular value decomposition and principal component analysis.
Fakhreddine (Fakhri) Karray is a Tunisian-Canadian artificial intelligence scientist, electrical and computer engineer, author, and academic. He served as the Loblaws Research Chair of Artificial Intelligence at the University of Waterloo's (UWaterloo) Department of Electrical and Computer Engineering, and as the inaugural co-director of the Waterloo AI Institute at UWaterloo. Having previously served as the provost of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), he serves as a professor of machine learning at the university and as an emeritus professor at the University of Waterloo's Department of Electrical and Computer Engineering. Karray's research interests encompass operational and generative AI, cognitive machines, natural human-machine interaction, and autonomous and intelligent systems. He has published in the fields of pattern analysis and machine intelligence and is the co-author of Elements of Dimensionality Reduction and Manifold Learning and Soft Computing and Intelligent Systems Design. His work on operational AI has been applied to intelligent transportation systems, virtual healthcare, and driver safety with him being featured in The Washington Post, Wired, The Globe and Mail, and CBC. He holds twenty US patents and has won the IEEE Vehicular Technology Society's Best Land Transportation Award for his work on improving traffic flow prediction with weather Information in connected cars and the MeditCom Conference Best Paper Award for his study on federated learning in communication systems. Karray is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Kavli Frontiers of Science, the Canadian Academy of Engineering. and the Engineering Institute of Canada.
In mathematics, the Johnson–Lindenstrauss lemma is a result named after William B. Johnson and Joram Lindenstrauss concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space. The lemma states that a set of points in a high-dimensional space can be embedded into a space of much lower dimension in such a way that distances between the points are nearly preserved. In the classical proof of the lemma, the embedding is a random orthogonal projection. The lemma has applications in compressed sensing, manifold learning, dimensionality reduction, graph embedding, cryptography and natural language processing. Much of the data stored and manipulated on computers, including text and images, can be represented as points in a high-dimensional space (see vector space model for the case of text). However, the essential algorithms for working with such data tend to become bogged down very quickly as dimension increases. It is therefore desirable to reduce the dimensionality of the data in a way that preserves its relevant structure.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Mae Jemison, aboard Space Shuttle Endeavour in 1992.