Fifth time's the charm: SpaceX has successfully landed the first stage of its Falcon 9 rocket on its droneship out in the Atlantic Ocean, the first time in history such a landing has ever been achieved. All four previous instances of this landing ended in failure. SpaceX hopes to use this type of rocket landing to be able to re-use the first stage of its rockets and dramatically cut the cost of space travel going forward. This landing is a huge step towards that goal, and came during an especially pivotal launch for SpaceX: SpaceX's first cargo re-supply mission to the International Space Station since a Falcon 9 exploded in mid-flight in the summer of 2015, thwarting that particular re-supply attempt. On this mission, SpaceX's uncrewed Dragon capsule -- which sits atop the Falcon 9 rocket and detaches to fly onward to the space station -- was also carrying some really exciting cargo: the first-ever inflatable space habitat designed for the space station, the Bigelow Expandable Activity Module (BEAM).

It is the first company--the first anybody to send a rocket to space and then land it on a floating barge. Sixth time is the charm, apparently. Or at least, anyone with an interest in low cost access to space hopes it will. At 4:43pm ET, the nine engines on board the Falcon 9's stage 1 rocket began pushing 1.53 million pounds of thrust against Earth. After about two and a half minutes, and several hundred thousand feet of elevation gain, the first stage detached and began a controlled fall back to Earth, arcing towards the football field-sized barge (charmingly-named "Of Course I Still Love You") in the Atlantic Ocean.

A Falcon 9 rocket took off from Cape Canaveral, carrying more supplies to the International Space Station. SpaceX was able to land its rocket on a barge April 8, 2016, about 200 miles off the shore of Cape Canaveral, Fla. (Photo: SpaceX) CAPE CANAVERAL -- The first stage of a SpaceX Falcon 9 rocket almost hit the bull's eye Friday, landing on a barge about 200 miles offshore. Though not exactly in the center of the platform, the maneuver was enough to keep the equipment from getting wet in the Atlantic Ocean. The experiment was the first successful landing. The booster possibly could have returned to land, like one did in December, SpaceX said.

Humans become aroused when touching robots in sensitive places, a new study has found. Far from seeing robots as just computers, humans can become physiologically aroused from touching a human-shaped robot in private places like their eyes and buttocks, the Stanford study found. The results could have huge consequences for the creation of robots in the future, such as ones that people live or even have sex with. It might also help people create "robot stand-ins", that allow people to touch others when actually being there isn't an option, the researchers said. Scientists have taken a leaf out of the script of The Martian by showing how easy it would be to grow your own veg on the Red Planet.

For the vast majority of queries (for example, navigation, simple fact lookup, and others), search engines do extremely well. Their ability to quickly provide answers to queries is a remarkable testament to the power of many of the fundamental methods of AI. They also highlight many of the issues that are common to sophisticated AI question-answering systems. It has become clear that people think of search programs in ways that are very different from traditional information sources. Rapid and ready-at-hand access, depth of processing, and the way they enable people to offload some ordinary memory tasks suggest that search engines have become more of a cognitive amplifier than a simple repository or front-end to the Internet. Like all sophisticated tools, people still need to learn how to use them. Although search engines are superb at finding and presenting information—up to and including extracting complex relations and making simple inferences—knowing how to frame questions and evaluate their results for accuracy and credibility remains an ongoing challenge. Some questions are still deep and complex, and still require knowledge on the part of the search user to work through to a successful answer. And the fact that the underlying information content, user interfaces, and capabilities are all in a continual state of change means that searchers need to continually update their knowledge of what these programs can (and cannot) do.

A central problem in applied data analysis is time series In this paper, we develop a tractable approach to maximum modeling--estimating and forecasting a discrete-time likelihood parameter estimation for stochastic multivariate stochastic process--for which the autoregressive moving ARMA models. To efficiently compute a globally average (ARMA) and stochastic ARMA (Thiesson et al. optimal estimate, the problem is re-expressed as a regularized 2012) are fundamental models. An ARMA model describes loss minimization, which then allows recent algorithmic the behavior of a linear dynamical system under advances in sparse estimation to be applied (Shah et al. latent Gaussian perturbations (Brockwell and Davis 2002; 2012; Candes et al. 2011; Bach, Mairal, and Ponce 2008; Lütkepohl 2007), which affords intuitive modeling capability, Zhang et al. 2011; White et al. 2012). Although there has efficient forecasting algorithms, and a close relationship been recent progress in global estimation for ARMA, such to linear Gaussian state-space models (Katayama 2006, approaches have either been restricted to single-input singleoutput pp.5-6).

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.

Swirls of ocean currents known as ocean eddies are a crucial component of the ocean's dynamics. In addition to dominating the ocean's kinetic energy, eddies play a significant role in the transport of water, salt, heat, and nutrients. Therefore, understanding current and future eddy patterns is a central climate challenge to address future sustainability of marine ecosystems. The emergence of sea surface height observations from satellite radar altimeter has recently enabled researchers to track eddies at a global scale. The majority of studies that identify eddies from observational data employ highly parametrized connected component algorithms using expert filtered data, effectively making reproducibility and scalability challenging. In this paper, we frame the challenge of monitoring ocean eddies as an unsupervised learning problem. We present a novel change detection algorithm that automatically identifies and monitors eddies in sea surface height data based on heuristics derived from basic eddy properties. Our method is accurate, efficient, and scalable. To demonstrate its performance we analyze eddy activity in the Nordic Sea (60-80N and 20W-20E), an area that has received limited attention and has proven to be difficult to analyze using other methods.

Existing ship scheduling approaches either ignore constraints on ship draft (distance between the waterline and the keel), or model these in very simple ways, such as a constant draft limit that does not change with time. However, in most ports the draft restriction changes over time due to variation in environmental conditions. More accurate consideration of draft constraints would allow more cargo to be scheduled for transport on the same set of ships. We present constraint programming (CP) and mixed integer programming (MIP) models for the problem of scheduling ships at a port with time-varying draft constraints so as to optimise cargo throughput at the port. We also investigate the effect of several variations to the CP model, including a model containing sequence variables, and a model with ordered inputs. Our model allows us to solve realistic instances of the problem to optimality in a very short time, and produces better schedules than both scheduling with constant draft, and manual scheduling approaches used in practice at ports.

Mapping and tracking biological ocean features, such as harmful algal blooms, is an important problem in the environmental sciences. The problem exhibits a high degree of uncertainty, because of both the dynamic ocean context and the challenges of sensing. Plan-based policy learning has been shown to be a powerful technique for obtaining robust intelligent behaviour in the face of uncertainty. In this paper we apply this technique in simulation, to the problem of tracking the outer edge of 2D biological features, such as the surfaces of harmful algal blooms. We show that plan-based policy-learning leads to highly accurate tracking in simulation, even in situations where the uncertainty governing the shape of the patch cannot be directly modelled. We present simulation results that give confidence that the approach could work in practice. We are now collaborating with ocean scientists at MBARI to perform physical tests at sea.