Scientists mimicked the neural networks of the brain to map phytoplankton types in the Mediterranean Sea. A new study published in the Journal of Geophysical Research: Oceans presented a new method of classifying phytoplankton that relies on artificial intelligence clustering. Phytoplankton blanket surface waters of the world's oceans, and pigments in their cells absorb certain wavelengths of light, like the chlorophyll that gives plants their green color. In the Mediterranean Sea, where the latest study focused its efforts, an array of phytoplankton species bloom throughout the year. Past research has mined satellite images of ocean color in the Mediterranean for common pigments found in phytoplankton.

In today's multifaceted energy world, a growing number of prosumer assets are increasing the complexity of power grids. This is even more important in an ever-changing climate that more and more generates huge storms such as the Typhoon Lekima which caused 9.3 Billion in damage (5th Costliest known Pacific typhoons) and more than 90 deaths in the Philippines, Taiwan and China earlier this year, or the recent monstrous Category 5 Hurricane Dorian in the Atlantic Ocean. The director-general of the Bahamas Ministry of Tourism and Aviation, Joy Jibrilu, details the damage left in the aftermath from Hurricane Dorian and what the Bahamas will need to move forward especially on the infrastructures. This looks too similar to what we've seen in Porto Rico two years ago which suffered severe damage from the category 5 hurricane Maria. The blackout as a result of Maria has been identified as the largest in US history and the second-largest in world history.

The cylindrical vessel sports a futuristic design like a surfaced submarine, it's sleek hull sculpted to slice through waves with ease. But step on board and things are out of the ordinary. The living quarters have been removed. Cars may dominate today's discussion about the future of autonomous transportation but some of the world's largest maritime companies are betting big on autonomous shipping. Within the next decade, driverless ships like the one just described could be hauling cargo around the world.

The modern world is becoming increasingly technology driven. Many areas, such as healthcare, have been quick to realise the possibilities. AI and machine learning in oil & gas focused sectors has been slower to establish itself. This is largely because the industry has been slow to realise the potential. However this is slowly changing. Machine learning in oil & gas can be used to enhance the capabilities of this increasingly competitive sector. Not only can it help to streamline the workforce. The technology can also be used to optimise extraction and deliver accurate models. These benefits are just some of the reasons why machine learning in oil & gas is becoming increasingly important. Here are 10 ways that the impact of machine learning in oil & gas industries is being felt. One of the most noticeable impacts of machine learning in oil & gas focused industries is how it transforms discovery processes. Applications employing machine learning in oil & gas enable computers to quickly and accurately analyse huge amounts of data. This includes being able to sift precisely through signals and noise in seismic data.

This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i.e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications, including long-term asymptotic patterns. Whereas state-of-the-art data-driven approaches rely on delay embeddings and linear decompositions of the underlying operators, we introduce a framework based on the data-driven identification of an augmented state-space model using a neural-network-based representation. For a given training dataset, it amounts to jointly learn an ODE (Ordinary Differential Equation) representation in the latent space and reconstructing latent states. Through numerical experiments, we demonstrate the relevance of the proposed framework w.r.t. state-of-the-art approaches in terms of short-term forecasting performance and long-term behaviour. We further discuss how the proposed framework relates to Koopman operator theory and Takens' embedding theorem.

Whether it is the Irish backstop or English Channel, the issue of how the UK and Europe are controlling their borders has been thrust into the public consciousness. And as with many of the globe's conundrums, countries and private companies are turning to ever more futuristic, and often controversial, technologies in order to protect their borders. There are, of course, immediate issues for Britain's borders with quandaries such as the potential hard border in Northern Ireland following Brexit, with the nebulous'technology' promised by some politicians either still being developed or put under question. One such future proposal is a satellite system that registered mobile phones as they pass the border, while sensors buried in the ground or radars on flying drones could detect possible unlawful breaches of the boundaries. But that would still leave the question of invasive, even if largely invisible, checks that run against the Good Friday Agreement.

Last year, after breaking the Guinness World Record for the Key West to Cuba run, we wondered what was next for the #77 Lucas Oil SilverHook ocean racing powerboat? We found the answer in the 50th anniversary of the Trinidad & Tobago Great Race, one of the most grueling races in the world. The 115-mile endurance course starts in Trinidad's Port of Spain, where you head north and then east near the island before popping into the Atlantic Ocean for a 50-mile sprint to the finish in Store Bay, Tobago. Because of the logistical difficulties of racing on foreign shores, we were the first American entry in 29 years. We knew we would face stiff competition from Jumbie, Cat Killer, Mr. Solo and other local rivals that know the course well.

This article explores the concepts of ocean wave multivariate multistep forecasting, reconstruction and feature selection. We introduce recurrent neural network frameworks, integrated with Bayesian hyperparameter optimization and Elastic Net methods. We consider both short- and long-term forecasts and reconstruction, for significant wave height and output power of the ocean waves. Sequence-to-sequence neural networks are being developed for the first time to reconstruct the missing characteristics of ocean waves based on information from nearby wave sensors. Our results indicate that the Adam and AMSGrad optimization algorithms are the most robust ones to optimize the sequence-to-sequence network. For the case of significant wave height reconstruction, we compare the proposed methods with alternatives on a well-studied dataset. We show the superiority of the proposed methods considering several error metrics. We design a new case study based on measurement stations along the east coast of the United States and investigate the feature selection concept. Comparisons substantiate the benefit of utilizing Elastic Net. Moreover, case study results indicate that when the number of features is considerable, having deeper structures improves the performance.

Continuous queries over data streams may suffer from blocking operations and/or unbound wait, which may delay answers until some relevant input arrives through the data stream. These delays may turn answers, when they arrive, obsolete to users who sometimes have to make decisions with no help whatsoever. Therefore, it can be useful to provide hypothetical answers - "given the current information, it is possible that X will become true at time t" - instead of no information at all. In this paper we present a semantics for queries and corresponding answers that covers such hypothetical answers, together with an online algorithm for updating the set of facts that are consistent with the currently available information.

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60-millisecond) and long-term (30-minute) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer, i.e. an affine layer whose weights are dynamically adapted at prediction time by an auxiliary network taking long-term summary statistics of spectrotemporal features as input. We show that both techniques are helpful and complementary. [...] We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.