The six astronauts at the International Space Station got an early Easter treat this weekend with the arrival of a supply ship full of fresh food and experiments. Instead of the usual bunny, Saturday's delivery came via a swan -- Orbital ATK's Cygnus capsule, named after the swan constellation. The cargo carrier rocketed away from Cape Canaveral on Tuesday night. NASA astronaut Timothy Kopra used the station's big robot arm to grab the capsule, as the two craft soared 250 miles above the Indian Ocean. A Russian cargo ship will lift off Thursday, followed by a SpaceX supply run on April 8. NASA has turned to private industry to keep the space station stocked.

An unmanned spacecraft carrying 6,300 pounds of supplies and science experiments caught up Saturday morning with the International Space Station as it flew 252 miles above the Indian Ocean. The Cygnus spacecraft, made by Dulles-based Orbital ATK, had launched late Tuesday from Cape Canaveral on its journey to the orbiting laboratory. Flying at more than 15,000 mph, the spacecraft was captured by the station's robotic arm at 6:51 a.m. Eastern in what a NASA official called a "textbook rendezvous." The spacecraft was launched atop an Atlas V rocket that Oribtal ATK hired from the United Launch Alliance, the joint venture between Lockheed Martin and Boeing.

The six astronauts at the International Space Station got an early Easter treat this weekend with the arrival of a supply ship full of fresh food and experiments. Instead of the usual bunny, Saturday's delivery came via a swan -- Orbital ATK's Cygnus capsule, named after the swan constellation. The cargo carrier rocketed away from Cape Canaveral on Tuesday night. NASA astronaut Timothy Kopra used the station's big robot arm to grab the capsule, as the two craft soared 250 miles above the Indian Ocean. A Russian cargo ship will lift off in a few days, followed by a SpaceX supply run on April 8. NASA has turned to private industry to keep the space station stocked.

Linguistic borrowing is the phenomenon of transferring linguistic constructions (lexical, phonological, morphological, and syntactic) from a donor language to a recipient language as a result of contacts between communities speaking different languages. Borrowed words are found in all languages, andin contrast to cognate relationshipsborrowing relationships may exist across unrelated languages (for example, about 40% of Swahilis vocabulary is borrowed from the unrelated language Arabic). In this work, we develop a model of morpho-phonological transformations across languages. Its features are based on universal constraints from Optimality Theory (OT), and we show that compared to several standardbut linguistically more naïvebaselines, our OT-inspired model obtains good performance at predicting donor forms from borrowed forms with only a few dozen training examples, making this a cost-effective strategy for sharing lexical information across languages. We demonstrate applications of the lexical borrowing model in machine translation, using resource-rich donor language to obtain translations of out-of-vocabulary loanwords in a lower resource language. Our framework obtains substantial improvements (up to 1.6 BLEU) over standard baselines.

Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predefined causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans.

Our own early attempts (as devoted users of LISP) to use PROLOG convinced us that it would be worth the effort to create within LISP a faithful implementation of Kowalski's logic programming idea. We felt it would be very convenient to be able to set up a knowledge base of assertions inside a LISP workspace, and to compute the answers to queries simply by executing appropriate function calls.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.