UPS Flight Forward (UPSFF) is collaborating with German drone-maker Wingcopter to develop the next generation of package delivery drones for a variety of use cases in the United States and internationally. UPSFF is a subsidiary of UPS dedicated to drone delivery. UPS chose Wingcopter for its unmanned aircraft technology and its track record in delivering a variety of goods over long distances in multiple international settings. "Drone delivery is not a one-size-fits-all operation," said Bala Ganesh, vice president of the UPS Advanced Technology Group. "Our collaboration with Wingcopter helps pave the way for us to start drone delivery service in new use-cases. UPS Flight Forward is building a network of technology partners to broaden our unique capability to serve customers and extend our leadership in drone delivery."

The sparse factorization of a large matrix is fundamental in modern statistical learning. In particular, the sparse singular value decomposition and its variants have been utilized in multivariate regression, factor analysis, biclustering, vector time series modeling, among others. The appeal of this factorization is owing to its power in discovering a highly-interpretable latent association network, either between samples and variables or between responses and predictors. However, many existing methods are either ad hoc without a general performance guarantee, or are computationally intensive, rendering them unsuitable for large-scale studies. We formulate the statistical problem as a sparse factor regression and tackle it with a divide-and-conquer approach. In the first stage of division, we consider both sequential and parallel approaches for simplifying the task into a set of co-sparse unit-rank estimation (CURE) problems, and establish the statistical underpinnings of these commonly-adopted and yet poorly understood deflation methods. In the second stage of division, we innovate a contended stagewise learning technique, consisting of a sequence of simple incremental updates, to efficiently trace out the whole solution paths of CURE. Our algorithm has a much lower computational complexity than alternating convex search, and the choice of the step size enables a flexible and principled tradeoff between statistical accuracy and computational efficiency. Our work is among the first to enable stagewise learning for non-convex problems, and the idea can be applicable in many multi-convex problems. Extensive simulation studies and an application in genetics demonstrate the effectiveness and scalability of our approach.

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.

Spatial prediction is commonly achieved under the assumption of a Gaussian random field (GRF) by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the Expectation-Maximization (EM) algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the Spatial Mixed Effects (SME) model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an Alternating Expectation Conditional Maximization (AECM) algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature data set archived by the United States National Climate Data Center, with improved results over previous methodology.

We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very high-dimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion.

A machine learning algorithm is developed to forecast the CO2 emission intensities in electrical power grids in the Danish bidding zone DK2, distinguishing between average and marginal emissions. The analysis was done on data set comprised of a large number (473) of explanatory variables such as power production, demand, import, weather conditions etc. collected from selected neighboring zones. The number was reduced to less than 50 using both LASSO (a penalized linear regression analysis) and a forward feature selection algorithm. Three linear regression models that capture different aspects of the data (non-linearities and coupling of variables etc.) were created and combined into a final model using Softmax weighted average. Cross-validation is performed for debiasing and autoregressive moving average model (ARIMA) implemented to correct the residuals, making the final model the variant with exogenous inputs (ARIMAX). The forecasts with the corresponding uncertainties are given for two time horizons, below and above six hours. Marginal emissions came up independent of any conditions in the DK2 zone, suggesting that the marginal generators are located in the neighbouring zones. The developed methodology can be applied to any bidding zone in the European electricity network without requiring detailed knowledge about the zone.

We look at a dataset of tropical storm data in the Atlantic Ocean from 1982 to 2017 and perform deep learning predictions with uncertainty bounds on trajectories of the storms. The result of these storms, particularly the strongest ones called hurricanes--defined as having wind speeds exceeding 74 mph--can be devastating because of their strong winds and heavy precipitation that can cause dangerous tides. Tropical storms can cause major environmental disasters when they reach land, such as the 2005 Hurricane Katrina that resulted in over 850 deaths and caused major economic damage and the 2012 Hurricane Sandy that caused almost $70 billion in damage across much of the eastern United States, with peak winds of 115 mph (Hurricane). According to the National Oceanic and Atmospheric Administration, it is likely that global warming will cause hurricanes in the upcoming century to be more intense by 1 to 10% globally (with higher peak winds and lower central pressures), which will result in a higher proportion of more severe storms (NOAA). Historically, hurricane trajectory predictions have used statistical methods that can be limiting because of the nonlinearity and complexity of atmospheric systems. Deep learning techniques and specifically recurrent neural networks have grown in popularity in recent years as a strong method for approaching prediction problems because of the ability to extract important features and relationships from complex high-dimensional data, especially for forecasting and classification (McDermott and Wikle, 2019). We implemented a number of improvements over previous deep learning prediction work (Alemany et al., 2019), including predicting exact storm locations in latitude/longitude instead of a grid value and using a prediction window that uses all previous hurricane data rather than a fixed-size sliding window. While hurricane trajectory predictions have seen improvements recently (SHIPS), we build on previous work to include a fundamental uncertainty measure in the prediction for the first time as part of a neural network framework. The uncertainty measure is especially valuable for understanding a defined location range rather than only a point estimate, which is important for evacuation and safety/preparation purposes.

The writing is on the wall. Every major global governmental agency is warning of the imminent tipping point towards catastrophic climate change, even the world's largest oil company Saudi Aramco is now talking about reaching peak oil within the next 20 years, and the International Energy Agency projects that it will happen in more like 10. Solar and wind are cheaper than ever, and large-scale solar mega-projects are quickly becoming the norm. It makes sense, then, that even the supermajor oil companies are diversifying their portfolios and investing in their own demise--also known as the renewable energy sector. Way back in July, 2017 Oilprice reported that France's Total S.A. was "leading the charge on renewables". At the time, Total's website boasted: "For Total, contributing to the development of renewable energies is as much a strategic choice as an industrial responsibility. We are doing our part to diversify the global energy mix by investing in renewables, with a strategic focus on solar energy and bioenergies."

The writing is on the wall. Every major global governmental agency is warning of the imminent tipping point towards catastrophic climate change, even the world's largest oil company Saudi Aramco is now talking about reaching peak oil within the next 20 years, and the International Energy Agency projects that it will happen in more like 10. Solar and wind are cheaper than ever, and large-scale solar mega-projects are quickly becoming the norm. It makes sense, then, that even the supermajor oil companies are diversifying their portfolios and investing in their own demise--also known as the renewable energy sector. Way back in July, 2017 Oilprice reported that France's Total S.A. was "leading the charge on renewables". At the time, Total's website boasted: "For Total, contributing to the development of renewable energies is as much a strategic choice as an industrial responsibility. We are doing our part to diversify the global energy mix by investing in renewables, with a strategic focus on solar energy and bioenergies."

We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.