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. Four hours later, the capsule was bolted firmly to the complex.

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. Four hours later, the capsule was bolted firmly to the complex.

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.

Increasingly social networks are being used for citizen science, where members of the public contribute knowledge to scientific endeavours. Tasks can be presented and solved using human computation, termed groupsourcing, with users benefiting from community tuition and experts gaining knowledge from the crowd. This paper gives details of a prototype that utilises groupsourcing to solve image classification tasks, to support social learning and to facilitate knowledge discovery in the domain of marine biology.

Crowdsourcing and citizen science have established themselves in the mainstream of research methodology in recent years, employing a variety of methods to solve problems using human computation. An approach described here, termed "groupsourcing", uses social networks to present problems and collect solutions. This paper details a method for archiving social network messages and investigates messages containing an image classification task in the domain of marine biology. In comparison to other methods, groupsourcing offers a high accuracy, data-driven and low cost approach.

Mesoscale ocean eddies are a critical component of the Earth System as they dominate the ocean's kinetic energy and impact the global distribution of oceanic heat, salinity, momentum, and nutrients. Therefore, accurately representing these dynamic features is critical for our planet's sustainability. The majority of methods that identify eddies from satellite observations analyze the data in a frame-by-frame basis despite the fact that eddies are dynamic objects that propagate across space and time. We introduce the notion of spatio-temporal consistency to identify eddies in a continuous spatio-temporal field, to simultaneously ensure that the features detected are both spatially and temporally consistent. Our spatio-temporal consistency approach allows us to remove most of the expert criteria used in traditional methods to reduce false negatives. The removal of arbitrary heuristics enables us to render more complete eddy dynamics by identifying smaller and longer lived eddies compared to existing methods.

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.