As tribute to the life and works of world-renowned scientist Stephen Hawking, watch host Neil deGrasse Tyson's recent StarTalk interview with the groundbreaking theoretical physicist. Stephen Hawking, the British theoretical physicist who found a link between gravity and quantum theory, and who declared that black holes aren't really black at all, has died, a spokesperson for the family told the Guardian and the Associated Press. "He was a great scientist and an extraordinary man whose work and legacy will live on for many years," Hawking's children Lucy, Robert, and Tim said in a statement. "His courage and persistence with his brilliance and humor inspired people across the world. "He once said: 'It would not be much of a universe if it wasn't home to the people you love.'
The world today paid tribute to physicist Stephen Hawking, who died today at the age of 76. The famed British theoretical physicist passed away peacefully at his home in Cambridge this morning after a long battle with motor neurone disease, his family has revealed. And the celebrity and scientific world, including NASA, Katy Perry, and Piers Morgan, took to Twitter to pay their respects to the father of three. His theories unlocked a universe of possibilities that we & the world are exploring. The world just dropped a lot of IQ points.
Stephen Hawking was celebrating his birthday early this year by taking in a movie. He turned 75 on Sunday, but the British theoretical physicist went to see "Rogue One: A Star Wars Story" in Cambridge, England, on Thursday. The pairing of the movie and the man was somewhat appropriate, what with the celebrated scientist viewing the latest installment of a celebrated science fiction film franchise. The man who was diagnosed more than a half-century ago with ALS, also known as Lou Gehrig's disease, has achieved much in his life, including beating the odds of survival with his medical condition. Typically given just 10 years to live from the time of diagnosis, Hawking has gone on to live in excess of five times that figure.
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem of inferring information - or patterns in general - about events emerging in real life based on the contents of this textual stream. We show that it is possible to extract valuable information about social phenomena, such as an epidemic or even rainfall rates, by automatic analysis of the content published in Social Media, and in particular Twitter, using Statistical Machine Learning methods. An important intermediate task regards the formation and identification of features which characterise a target event; we select and use those textual features in several linear, non-linear and hybrid inference approaches achieving a significantly good performance in terms of the applied loss function. By examining further this rich data set, we also propose methods for extracting various types of mood signals revealing how affective norms - at least within the social web's population - evolve during the day and how significant events emerging in the real world are influencing them. Lastly, we present some preliminary findings showing several spatiotemporal characteristics of this textual information as well as the potential of using it to tackle tasks such as the prediction of voting intentions.