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.

On Friday, SpaceX will attempt to launch an uncrewed Dragon spacecraft to the International Space Station, the first cargo run to the space laboratory since the company's Falcon 9 rocket disintegrated after launch above Florida in June. SEE ALSO: SpaceX misses its rocket landing on a drone ship... again The private spaceflight company founded by Elon Musk is also hoping to bring the first stage of the rocket back down to Earth, landing it on a drone ship in the Atlantic Ocean successfully for the first time. The Falcon 9 rocket is set to take flight at 4:43 p.m. ET, and you can watch the launch and landing live directly through SpaceX or in the window below. This means that if the rocket is launching a particularly heavy payload, or a smaller payload to a higher orbit, it will need to come back and land on the drone ship because of the high amount of fuel needed to make those missions successful. Definitely harder to land on a ship.

We present a system for online monitoring of maritime activity over streaming positions from numerous vessels sailing at sea. It employs an online tracking module for detecting important changes in the evolving trajectory of each vessel across time, and thus can incrementally retain concise, yet reliable summaries of its recent movement. In addition, thanks to its complex event recognition module, this system can also offer instant notification to marine authorities regarding emergency situations, such as risk of collisions, suspicious moves in protected zones, or package picking at open sea. Not only did our extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.

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.

We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatial-temporal processes such as natural resource estimation and pollution monitoring have been typically addressed using techniques based on Gaussian processes and co-Kriging. While the Gaussian prior assumption is convenient from analytical and computational perspectives, nature is dominated by non-Gaussian likelihoods. Copula processes are an elegant and flexible solution to handle various non-Gaussian likelihoods by capturing the dependence structure of random variables with cumulative distribution functions rather than their marginals. We show how multi-task learning for copula processes can be used to improve multivariable prediction for problems where the simple Gaussianity prior assumption does not hold. Then, we present a transductive approximation for multi-task learning and derive analytical expressions for the copula process model. The approach is evaluated and compared to other techniques in one artificial dataset and two publicly available datasets for natural resource estimation and concrete slump prediction.

A challenge to modeling and monitoring the health of the ocean environment is that it is largely under sensed and difficult to sense remotely. Autonomous underwater vehicles (AUVs) can improve observability, for example of algal bloom regions, ocean acidification, and ocean circulation. This AUV paradigm, however, requires robust operation that is cost effective and responsive to the environment. To achieve low cost we generate operational sequences automatically from science goals, and achieve robustness by reasoning about the discrete and continuous effects of actions. We introduce Kongming2, a generative planner for hybrid systems with temporally extended goals (TEGs) and temporally flexible actions. It takes as input high level goals and outputs trajectories and actions of the hybrid system, for example an AUV. Kongming2 makes two major extensions to Kongming1: planning for TEGs, and planning with temporally flexible actions. We demonstrated a proof of concept of the planner in the Atlantic ocean on Odyssey IV, an AUV designed and built by the MIT AUV Lab at Sea Grant.