Autonomous vessel software and systems provider Sea Machines Robotics today closed a $15 million funding round to accelerate deployment of its technologies in the unmanned naval boat and ship market. Sea Machines boldly claims this is one of the largest rounds for a tech company tackling marine and maritime use cases. Self-steering vessels aren't a new idea -- but they are gaining steam. Earlier this year, IBM and Promare -- a U.K.-based marine research and exploration charity -- trialed a prototype of an AI-powered maritime navigation system ahead of a September 6th venture to send a ship across the Atlantic Ocean. In Norway, a crewless cargo ship called the Yara Birkeland is expected to go into commercial operation later in 2020.

Autonomous vessel software and systems provider Sea Machines Robotics today closed a $15 million funding round to accelerate deployment of its technologies in the unmanned naval boat and ship market. Sea Machines boldly claims this is one of the largest rounds for a tech company tackling marine and maritime use cases. Self-steering vessels aren't a new idea -- but they are gaining steam. Earlier this year, IBM and Promare -- a U.K.-based marine research and exploration charity -- trialed a prototype of an AI-powered maritime navigation system ahead of a September 6th venture to send a ship across the Atlantic Ocean. In Norway, a crewless cargo ship called the Yara Birkeland is expected to go into commercial operation later in 2020.

We present a new data-driven model to reconstruct nonlinear flow from spatially sparse observations. The model is a version of a conditional variational auto-encoder (CVAE), which allows for probabilistic reconstruction and thus uncertainty quantification of the prediction. We show that in our model, conditioning on the measurements from the complete flow data leads to a CVAE where only the decoder depends on the measurements. For this reason we call the model as Semi-Conditional Variational Autoencoder (SCVAE). The method, reconstructions and associated uncertainty estimates are illustrated on the velocity data from simulations of 2D flow around a cylinder and bottom currents from the Bergen Ocean Model. The reconstruction errors are compared to those of the Gappy Proper Orthogonal Decomposition (GPOD) method.

Though these methods can be adapted from single-label predictors, they ignore the correlation Multi-label classification is the challenging task among labels. To improve this, classifier chains [Read of predicting the presence and absence of multiple et al., 2009] stack the binary classifiers into a chain and reuse targets, involving representation learning and the outputs of previous classifiers as extra information to improve label correlation modeling. We propose a novel the prediction of the current label. Followup works extend framework for multi-label classification, Multivariate the classifier chains to recurrent neural networks [Wang Probit Variational AutoEncoder (MPVAE), that et al., 2016] to increase capacity and better model the label effectively learns latent embedding spaces as well correlation. Label ordering is critical to these methods as label correlations. MPVAE learns and aligns two since long-term dependencies are typically weaker than shortterm probabilistic embedding spaces for labels and features dependencies. The model structure also restricts parallel respectively. The decoder of MPVAE takes in computation. Another straightforward method is to find the samples from the embedding spaces and models nearest neighbors in the feature space and assign labels to the joint distribution of output targets under a Multivariate test samples by Bayesian inference [Zhang and Zhou, 2007; Probit model by learning a shared covariance Chiang et al., 2012].

Many of the world's most important fisheries are experiencing increases in illegal fishing, undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending illegal, unreported, and unregulated (IUU) fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time-consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict illegal fishing on the Patagonian Shelf, one of the world's most productive regions for fisheries. Specifically, we focus on Chinese fishing vessels, which have consistently fished illegally in this region. We combine vessel location data with oceanographic seascapes -- classes of oceanic areas based on oceanographic variables -- as well as other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a Chinese vessel is operating illegally with 69-96% confidence, depending on the year and predictor variables used. These results offer a promising step towards preempting illegal activities, rather than reacting to them forensically.

Convolutional Neural Networks (CNN) possess many positive qualities when it comes to spatial raster data. Translation invariance enables CNNs to detect features regardless of their position in the scene. However, in some domains, like geospatial, not all locations are exactly equal. In this work, we propose localized convolutional neural networks that enable convolutional architectures to learn local features in addition to the global ones. We investigate their instantiations in the form of learnable inputs, local weights, and a more general form. They can be added to any convolutional layers, easily end-to-end trained, introduce minimal additional complexity, and let CNNs retain most of their benefits to the extent that they are needed. In this work we address spatio-temporal prediction: test the effectiveness of our methods on a synthetic benchmark dataset and tackle three real-world wind prediction datasets. For one of them, we propose a method to spatially order the unordered data. We compare the recent state-of-the-art spatio-temporal prediction models on the same data. Models that use convolutional layers can be and are extended with our localizations. In all these cases our extensions improve the results, and thus often the state-of-the-art. We share all the code at a public repository.

Extreme waves in the Arctic typically occur every 20 years, but as climate change continues to plague the region these events could happen every two to five years, a new study reveals. Much of this area is frozen for a majority of the year, but rising temperatures have increased periods of open water that could result in catastrophic waves. Using computer models, researchers found the area hit the hardest was in the Greenland Sea, which could experience maximum annual wave heights of more than 19 feet. The team also warns coastal flooding might increase by a factor of four to 10 by the end of this century. Extreme waves in the Arctic typically occur every 20 years, but as climate change continues to plague the region these events could happen every two to five years, a new study reveals.

The long-awaited sequel to Top Gun is due to hit cinemas in December, but the virtuoso fighter pilots at its heart could soon be a thing of the past. The trustworthy wingman will soon be replaced by artificial intelligence, built into a drone, or an existing fighter jet with no one in the cockpit. Since 2010, the US Air Force and Boeing's QF-16 programme has been converting old F-16 fighter jets into unmanned drones, which can fly preset routes without a pilot. This year, 32 of these autonomous planes – rescued from retirement in the "boneyard" at an Air Force base near Arizona – will be used as targets in weapons testing over the Gulf of Mexico. In the future, self-flying fighter jets such as these could transform aerial combat. The Air Force's Skyborg programme, which could be in operation as soon as 2023, is developing AI systems for its unmanned Valkyrie drones which would enable them to communicate with and operate in tandem with a manned F-35 jet.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Maybe it will take out missiles beyond the earth's atmosphere, incinerate targets well beyond the horizon with high-powered laser weapons and instantly stop a multi-faceted series of incoming attacks all at the same time? Perhaps it will use AI-empowered algorithms to launch a large fleet of networked surface, air and undersea drones, able to launch coordinated attacks at long ranges? All of these capabilities, advanced well beyond the current state-of-the-art into a new generation of maritime warfare weapons, are likely to figure prominently in the Navy's current conceptual work on a new generation of destroyers to emerge more than a decade from now – the Future Surface Combatant.

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would have immense societal value, in areas such as agricultural productivity, water resource management, transportation and aviation systems, and emergency planning for extreme weather events. However, SSF is considered more challenging than either weather prediction or even seasonal prediction. In this paper, we carefully study a variety of machine learning (ML) approaches for SSF over the US mainland. While atmosphere-land-ocean couplings and the limited amount of good quality data makes it hard to apply black-box ML naively, we show that with carefully constructed feature representations, even linear regression models, e.g., Lasso, can be made to perform well. Among a broad suite of 10 ML approaches considered, gradient boosting performs the best, and deep learning (DL) methods show some promise with careful architecture choices. Overall, suitable ML methods are able to outperform the climatological baseline, i.e., predictions based on the 30-year average at a given location and time. Further, based on studying feature importance, ocean (especially indices based on climatic oscillations such as El Nino) and land (soil moisture) covariates are found to be predictive, whereas atmospheric covariates are not considered helpful.