The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. This is particularly harmful for machine learning algorithms, since gathering new ground truth data to train models for each sensor is costly and requires experienced manpower. In this work, we propose a domain adaptation transformation to reduce the statistical differences between images of two satellite sensors in order to boost the performance of transfer learning models. The proposed methodology is based on the Cycle Consistent Generative Adversarial Domain Adaptation (CyCADA) framework that trains the transformation model in an unpaired manner. In particular, Landsat-8 and Proba-V satellites, which present different but compatible spatio-spectral characteristics, are used to illustrate the method. The obtained transformation significantly reduces differences between the image datasets while preserving the spatial and spectral information of adapted images, which is hence useful for any general purpose cross-sensor application. In addition, the training of the proposed adversarial domain adaptation model can be modified to improve the performance in a specific remote sensing application, such as cloud detection, by including a dedicated term in the cost function. Results show that, when the proposed transformation is applied, cloud detection models trained in Landsat-8 data increase cloud detection accuracy in Proba-V.

In order to help physicists to expand their knowledge of the climate in the Lesser Antilles, we aim to identify the spatio-temporal configurations using clustering analysis on wind speed and cumulative rainfall datasets. But we show that using the L2 norm in conventional clustering methods as K-Means (KMS) and Hierarchical Agglomerative Clustering (HAC) can induce undesirable effects. So, we propose to replace Euclidean distance (L2) by a dissimilarity measure named Expert Deviation (ED). Based on the symmetrized Kullback-Leibler divergence, the ED integrates the properties of the observed physical parameters and climate knowledge. This measure helps comparing histograms of four patches, corresponding to geographical zones, that are influenced by atmospheric structures. The combined evaluation of the internal homogeneity and the separation of the clusters obtained using ED and L2 was performed. The results, which are compared using the silhouette index, show five clusters with high indexes. For the two available datasets one can see that, unlike KMS-L2, KMS-ED discriminates the daily situations favorably, giving more physical meaning to the clusters discovered by the algorithm. The effect of patches is observed in the spatial analysis of representative elements for KMS-ED. The ED is able to produce different configurations which makes the usual atmospheric structures clearly identifiable. Atmospheric physicists can interpret the locations of the impact of each cluster on a specific zone according to atmospheric structures. KMS-L2 does not lead to such an interpretability, because the situations represented are spatially quite smooth. This climatological study illustrates the advantage of using ED as a new approach.

It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs; a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz-`96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly-sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.

Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline. In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet dataset. We study how specific design choices in the ImageNet creation process impact the fidelity of the resulting dataset---including the introduction of biases that state-of-the-art models exploit. Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for. Finally, our findings emphasize the need to augment our current model training and evaluation toolkit to take such misalignments into account. To facilitate further research, we release our refined ImageNet annotations at https://github.com/MadryLab/ImageNetMultiLabel.

Building on recent research for prediction of hurricane trajectories using recurrent neural networks (RNNs), we have developed improved methods and generalized the approach to predict Bayesian intervals in addition to simple point estimates. Tropical storms are capable of causing severe damage, so accurately predicting their trajectories can bring significant benefits to cities and lives, especially as they grow more intense due to climate change effects. By implementing the Bayesian interval using dropout in an RNN, we improve the actionability of the predictions, for example by estimating the areas to evacuate in the landfall region. We used an RNN to predict the trajectory of the storms at 6-hour intervals. We used latitude, longitude, windspeed, and pressure features from a Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset of about 500 tropical storms in the Atlantic Ocean. Our results show how neural network dropout values affect predictions and intervals.

British archaeologists who discovered hundreds of artefacts from a cluster of 17th century shipwrecks in the Mediterranean Sea have had their cargo seized and been accused of an'illicit excavation'. Enigma Recoveries, which led an expedition into the Levantine Basin off the coast of Cyprus, found 12 shipwrecks filled with Chinese porcelain, jugs, coffee pots, peppercorns and illicit tobacco pipes. The ships and their priceless cargo, hailed as the'archaeological equivalent of finding a new planet' were recovered in ancient'shipping lanes' that served spice and silk trades from 300 BC onwards. But in a strongly-worded statement, the Cypriot government accused the company of being well known to both Cyprus and UNESCO for its'illicit underwater excavations' and its'violent extraction of objects causing destruction to their context'. Cyprus's Department of Antiquities accused the company of intending to sell the objects, as allegedly evident in documents filed with the United States Securities and Exchange Commission (NASDAQ).

Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP). With the burst of deep neural networks and the evolution of contextualized language models (CLMs), the research of MRC has experienced two significant breakthroughs. MRC and CLM, as a phenomenon, have a great impact on the NLP community. In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with a particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches. We propose a full-view categorization and new taxonomies on these topics. The primary views we have arrived at are that 1) MRC boosts the progress from language processing to understanding; 2) the rapid improvement of MRC systems greatly benefits from the development of CLMs; 3) the theme of MRC is gradually moving from shallow text matching to cognitive reasoning.

The last German military communications decoded at Bletchley Park in World War Two have been revealed to mark the 75th anniversary of VE Day. They were broadcast on 7 May 1945 by a military radio network making its final stand in Cuxhaven on Germany's North Sea coast. The message reports the arrival of British troops and ends: "Closing down for ever - all the best - goodbye." After Germany surrendered, VE Day was declared the next day. In 1944, this German military radio network, codenamed BROWN, had extended across Europe sending reports about the development of experimental weapons.

The Internet-of-Things generates vast quantities of data, much of it attributable to an individual's activity and behaviour. Holding and processing such personal data in a central location presents a significant privacy risk to individuals (of being identified or of their sensitive data being leaked). However, analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high performance predictive models. Traditionally, data and models are stored and processed in a data centre environment where models are trained in a single location. This work reviews research around an alternative approach to machine learning known as federated learning which seeks to train machine learning models in a distributed fashion on devices in the user's domain, rather than by a centralised entity. Furthermore, we review additional privacy preserving methods applied to federated learning used to protect individuals from being identified during training and once a model is trained. Throughout this review, we identify the strengths and weaknesses of different methods applied to federated learning and finally, we outline future directions for privacy preserving federated learning research, particularly focusing on Internet-of-Things applications.

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.