LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the efficacy of our generative model, we applied it to seismic field data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.

With the fire hose of imagery that's streaming daily from a variety of sensors, the need for using artificial intelligence (AI) to automate feature extraction is only increasing. The ability to train more than a dozen deep learning models on geospatial datasets and derive information products has been available using the ArcGIS API for Python or ArcGIS Pro, and users can scale up processing using ArcGIS Image Server. Esri is taking AI to the next level with ready-to-use geospatial AI models in the ArcGIS Living Atlas of the World. Initially, three models have been made available. Two of the models use satellite imagery.

Prediction based on Irregularly Sampled Time Series (ISTS) is of wide concern in the real-world applications. For more accurate prediction, the methods had better grasp more data characteristics. Different from ordinary time series, ISTS is characterised with irregular time intervals of intra-series and different sampling rates of inter-series. However, existing methods have suboptimal predictions due to artificially introducing new dependencies in a time series and biasedly learning relations among time series when modeling these two characteristics. In this work, we propose a novel Time Encoding (TE) mechanism. TE can embed the time information as time vectors in the complex domain. It has the the properties of absolute distance and relative distance under different sampling rates, which helps to represent both two irregularities of ISTS. Meanwhile, we create a new model structure named Time Encoding Echo State Network (TE-ESN). It is the first ESNs-based model that can process ISTS data. Besides, TE-ESN can incorporate long short-term memories and series fusion to grasp horizontal and vertical relations. Experiments on one chaos system and three real-world datasets show that TE-ESN performs better than all baselines and has better reservoir property.

Electric vehicles are becoming increasingly popular, and self-driving cars are also on the way. When will they be mature enough to meet climate challenges and take to the roads en masse? Writes about the impact of new technologies on society: are we aware of the revolution in progress and its consequences? Many countries are seeking to achieve carbon neutrality within the coming decades. In Europe, the Green DealExternal link has laid down a plan to achieve zero emissions by 2050, and Switzerland has set itself the same deadline. This is an ambitious goal that puts the spotlight on the transport sector, which is responsible for around 16% of global CO2 emissions.External link So what will mobility look like in the future?

The "Mayflower 400", the world's first intelligent ship, bobs gently in a light swell as it stops its engines in Plymouth Sound, off England's southwest coast, before self-activating a hydrophone designed to listen to whales. The 50-foot (15-metre) trimaran, which weighs nine tonnes and navigates with complete autonomy, is preparing for a transatlantic voyage. On its journey, the vessel, covered in solar panels, will study marine pollution and analyse plastic in the water, as well as track aquatic mammals. Eighty per cent of the underwater world remains unexplored. Brett Phaneuf, the co-founder of the charity ProMare and the mastermind behind the Mayflower project, said the ocean exerts "the most powerful force" on the global climate.

LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the e?cacy of our generative model, we applied it to seismic?eld data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.

The world's first fully autonomous ship is set to make its maiden voyage across the Atlantic next month. Inspired by the ship that brought the Pilgrims to North America, 'Mayflower 400' will be guided by artificial intelligence rather than a human crew. If all goes well, it will depart from Plymouth, England on May 15 and arrive at Plymouth, Massachusetts, about 3,000 miles and two weeks later. The original Mayflower, which transported 102 Pilgrims and other passengers, took 10 weeks to reach its destination in 1620. Mayflower 400 was set to embark on its transatlantic cruise last September for the Mayflower's 400th anniversary, but was delayed because of the coronavirus pandemic.

YOU HAVE to hand it to the "technoking". For all his impish self-aggrandisement, mockery of deadlines, baiting of regulators and soon-to-be sideline as a "Saturday Night Live" comedy host, Elon Musk is deadly serious about technology. So serious, in fact, that as he was discussing the nitty-gritty of neural networks on an earnings call on April 26th, Tesla's boss did not miss a beat when what sounded like his infant son let out a wail in the background. The electric-car maker's record net profit of $438m in the first quarter, the seventh straight in the black, came as an afterthought. Your browser does not support the audio element.

As the offshore oil and gas industry becomes more competitive, it actively pursues increased efficiency through innovative approaches while streamlining production, reducing costs, and improving safety. Many companies are looking at digitization to insulate themselves from market shocks, remain profitable at lower oil prices, and generate competitive advantage during recovery. The path forward lies in leveraging machine learning-based technologies that are maturing quickly and are being adopted across the value chain. The use of Machine Learning (ML) models is particularly promising for the resolution of problems involving processes that are not completely understood or where it is not feasible to run mechanistic models at desired resolutions in space and time. With these growing technologies and solutions to complex science and engineering problems require novel methodologies that can integrate physics-based modeling approaches with state-of-the-art ML techniques.

The human voice conveys unique characteristics of an individual, making voice biometrics a key technology for verifying identities in various industries. Despite the impressive progress of speaker recognition systems in terms of accuracy, a number of ethical and legal concerns has been raised, specifically relating to the fairness of such systems. In this paper, we aim to explore the disparity in performance achieved by state-of-the-art deep speaker recognition systems, when different groups of individuals characterized by a common sensitive attribute (e.g., gender) are considered. In order to mitigate the unfairness we uncovered by means of an exploratory study, we investigate whether balancing the representation of the different groups of individuals in the training set can lead to a more equal treatment of these demographic groups. Experiments on two state-of-the-art neural architectures and a large-scale public dataset show that models trained with demographically-balanced training sets exhibit a fairer behavior on different groups, while still being accurate. Our study is expected to provide a solid basis for instilling beyond-accuracy objectives (e.g., fairness) in speaker recognition.