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Those who dream of getting an Amazon package, a prescription drug, or even a beer delivered to their doorsteps via drone might have their wishes fulfilled sooner than expected.

The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters. Additionally, due to changes in the system in time and in order to increase the accuracy and robustness of the prediction, the Ensemble Kalman Filter (EnKF) is used to update the flow rate predictions based on new observations. The developed approach was tested on the data from two mature gas production wells in which their production is highly dynamic and suffering from salt deposition. The results show that the flow predictions using the EnKF updated model leads to better Jeffreys' J-divergences than the predictions without the EnKF model updating scheme.

We study data-driven methods for community detection in graphs. This estimation problem is typically formulated in terms of the spectrum of certain operators, as well as via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the Stochastic Block Model, recent research has unified these two approaches, and identified both statistical and computational signal-to-noise detection thresholds. We embed the resulting class of algorithms within a generic family of graph neural networks and show that they can reach those detection thresholds in a purely data-driven manner, without access to the underlying generative models and with no parameter assumptions. The resulting model is also tested on real datasets, requiring less computational steps and performing significantly better than rigid parametric models.

Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. Most popular ICA methods use kurtosis as a metric of non-Gaussianity to maximize, such as FastICA and JADE. However, their assumption of fourth-order moment (kurtosis) may not always be satisfied in practice. One of the possible solution is to use third-order moment (skewness) instead of kurtosis, which was applied in $ICA_{SG}$ and EcoICA. In this paper we present a competitive approach to ICA based on the Split Generalized Gaussian distribution (SGGD), which is well adapted to heavy-tailed as well as asymmetric data. Consequently, we obtain a method which works better than the classical approaches, in both cases: heavy tails and non-symmetric data. \end{abstract}

Tue 13 Feb 2018 11.00 EST Last modified on Tue 13 Feb 2018 11.01 EST Drones invoke varying perceptions, from fun gadget to fly in the park to deadly military weapons. In the future, they may even be viewed as a handy tool in the battle to fight climate change. Greenhouse gas emissions from the tra...

Drone delivery is expected to take off big time in the next few years. Chinese online retailer JD.com has already launched drone delivery in four provinces in China, while DHL and Zipline are delivering medicines with drones in rural and hard-to-reach areas. Amazon, Google, and UPS are all working on getting drone delivery service off the ground. There are a lot of issues to think about when it comes to package delivery using drones--safety, privacy, and logistics being some of the main concerns. In a new study, researchers tackle two other important aspects: energy use and greenhouse gas emissions.

If the idea of swarms of delivery drones dropping packages all over our cities started out as a joke, for some reason the punchline hasn't landed yet. Amazon applied for a patent in 2015 for a command center, like a beehive, plopped into your city, which isn't a worrying metaphor at all. Google has its own program in the works, which at least for the moment involves delivering burritos. Again, if this is a joke, it's got a very long fuse. Forget about the insane logistics of such a system for a moment, or if you'd even be keen on drones swarming your town.

When you have large data sets of satellite or drone imagery that you have to process to create predictions, classification, or clustering – machine learning (ML) is the way to go. Indeed, ML has started to play a critical role in spatial problem solving given its potential to rapidly scan and unlock insights from petabytes of pixels obtained from hundreds of satellites and drones that are constantly orbiting earth. Orbital Insight, for example, applies machine learning and computer vision technologies to interpret data at petabyte scale to make it actionable for better business and policy decisions. The California based company has developed a powerful method that blends satellite imagery, deep learning, and data science for monitoring fresh-water supplies at local and global scale. Good news is that you don't have to be Orbital or in California,USA to also deploy machine learning. The proliferation of opensource platforms has made machine learning a lot easier to implement both on single personal computers and at scale, and in most popular programming or scripting languages.

Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards. To tackle this problem, we present a diversity-driven approach for exploration, which can be easily combined with both off- and on-policy reinforcement learning algorithms. We show that by simply adding a distance measure to the loss function, the proposed methodology significantly enhances an agent's exploratory behaviors, and thus preventing the policy from being trapped in local optima. We further propose an adaptive scaling method for stabilizing the learning process. Our experimental results in Atari 2600 show that our method outperforms baseline approaches in several tasks in terms of mean scores and exploration efficiency.