Archaeologists have found shipwrecks in the Mediterranean filled with hundreds of artefacts including Chinese porcelain, jugs, coffee pots, peppercorns and illicit tobacco pipes. A British-led expedition found a cluster of 12 ships on the sea bed, 1.2 miles below the surface of the Levantine Sea, using sophisticated robots. The ships were recovered in ancient'shipping lanes' that served spice and silk trades of the Greek, Roman and Ottoman empires, from 300 BC onwards. The ancient ships – including the biggest ever found in the Med – were unearthed in a muddy part of the eastern seabed between Cyprus and Lebanon, where remnants are often hard to find. The cluster of shipwrecks were found in the Levantine Basin in the east of the Mediterranean Sea.

Earth Observation data is valuable for journalist's reports to the public. An example are the maps released in little time during or after the tsunami in Indian Ocean in 2004 or the Fukushima disaster in 2011, accompanying the verbal or text reports of theirs. Taking advantage of the improved temporal frequency and spatial cover of the Sentinel satellite sensors SnapEarth aims to assimilate latest spaceborne retrieved information to support journalists in their work in near real time. In this context, a dedicated services' module aims to leverage on Copernicus monitoring services, like the EMS's (Emergency Management Service) EFAS (European Flood Awareness System) and EFFIS (European Forest Fire Information System). It will add in tandem to them the ability to exploit latest AI (Artificial Intelligence) techniques to automatically and unsupervised query through big data piles to deliver in minimum time required products.

Managing the quality of water for present and future generations of coastal regions should be a central concern of both citizens and public officials. Remote sensing can contribute to the management and monitoring of coastal water and pollutants. Algal blooms are a coastal pollutant that is a cause of concern. Many satellite data, such as MODIS, have been used to generate water-quality products to detect the blooms such as chlorophyll a (Chl-a), a photosynthesis index called fluorescence line height (FLH), and sea surface temperature (SST). It is important to characterize the spatial and temporal variations of these water quality products by using the mathematical models of these products. However, for monitoring, pollution control boards will need nowcasts and forecasts of any pollution. Therefore, we aim to predict the future values of the MODIS Chl-a, FLH, and SST of the water. This will not be limited to one type of water but, rather, will cover different types of water varying in depth and turbidity. This is very significant because the temporal trend of Chl-a, FLH, and SST is dependent on the geospatial and water properties. For this purpose, we will decompose the time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary. We explore three such time series machine learning models that can characterize the non-stationary time series data and predict future values, including the Seasonal ARIMA (Auto Regressive Integrated Moving Average) (SARIMA), regression, and neural network. The results indicate that all these methods are effective at modelling Chl-a, FLH, and SST time series and predicting the values reasonably well. However, regression and neural network are found to be the best at predicting Chl-a in all types of water (turbid and shallow). Meanwhile, the SARIMA model provides the best prediction of FLH and SST.

While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies. We then present empirical results in two different contexts --- online content recommendation and sustainable abalone fisheries --- to underscore the applicability of our approach to a wide range of practical decisions. Taken together, these results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare.

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear models are often not sufficient to describe the complex relationship between input variables and a response. This relationship can be better described by non-linearities and complex functional interactions. Deep learning models have been extremely successful in terms of prediction although they are often difficult to specify and potentially suffer from overfitting. In this paper, we introduce a class of Bayesian generalized nonlinear regression models with a comprehensive non-linear feature space. Non-linear features are generated hierarchically, similarly to deep learning, but have additional flexibility on the possible types of features to be considered. This flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. A genetically modified Markov chain Monte Carlo algorithm is developed to make inference. Model averaging is also possible within our framework. In various applications, we illustrate how our approach is used to obtain meaningful non-linear models. Additionally, we compare its predictive performance with a number of machine learning algorithms.

DUBAI, UNITED ARAB EMIRATES – A small instrument inside the drones that targeted the heart of Saudi Arabia's oil industry and those in the arsenal of Yemen's Houthi rebels match components recovered in downed Iranian drones in Afghanistan and Iraq, two reports say. These gyroscopes have only been found inside drones manufactured by Iran, Conflict Armament Research said in a report released on Wednesday. That follows a recently released report from the United Nations saying its experts saw a similar gyroscope from an Iranian drone obtained by the U.S. military in Afghanistan, as well as in weapons shipments seized in the Arabian Sea bound for Yemen. The discovery further ties Iran to an attack that briefly halved Saudi Arabia's oil output and saw energy prices spike by a level unseen since the 1991 Gulf War. It also ties Iran to the arming of the rebel Houthis in Yemen's long civil war.

Since 2014, Russia has played a dominant role in the civil war hostilities in Syria where the testing of technology fresh out of research and development has been applied to measure results, graded by software systems. Such military upgrades launched in Syria and in Yemen include the SS-21 Scarab, the Uran-9 and the Ratnick-4 (robotics). A four day drill was held in December 2019 in the Gulf of Oman and the Indian Ocean. Participants were Russia, Iran, and China whose cooperation, unity, and military exchanges were evident during the drills. Russia's involvement should be considered in the light of a strategy.

True collectors are fascinating people; they're smart and persistent. As time goes on, everyday objects fall out of fashion and then, years later, clever collectors swoop in. Scouring the auction sites is a good way to find valuables and evaluate treasures. Tap or click here for 5 sneaky eBay scams to watch out for. When you're ready to look beyond eBay, I have you covered with links to government, law enforcement and Department of Treasury auctions.

Underwater photography is hard to get right. Special filters, artificial lights, and top-of-the-line underwater cameras can help, but there's still a lot of water between the camera and the object in the photo. We've become accustomed to the blue-green tint of underwater photography. How would the ocean look without water? What are the true colors of a coral reef?

The gradient boosting algorithm constructs a regression estimator using a linear combination of simple "base learners". In order to obtain a robust non-parametric regression estimator that is scalable to high dimensional problems we propose a robust boosting algorithm based on a two-stage approach, similar to what is done for robust linear regression: we first minimize a robust residual scale estimator, and then improve its efficiency by optimizing a bounded loss function. Unlike previous proposals, our algorithm does not need to compute an ad-hoc residual scale estimator in each step. Since our loss functions are typically non-convex, we propose initializing our algorithm with an $L_1$ regression tree, which is fast to compute. We also introduce a robust variable importance metric for variable selection that is calculated via a permutation procedure. Through simulated and real data experiments, we compare our method against gradient boosting with squared loss and other robust boosting methods in the literature. With clean data, our method works equally well as gradient boosting with the squared loss. With symmetric and asymmetrically contaminated data, we show that our proposed method outperforms in terms of prediction error and variable selection accuracy.