This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. Few technologies have been more anticipated heading into the 2020s than autonomous vehicles. Tantalizingly close and yet still perhaps decades from market adoption in some use cases, the technology is as promising as it is misunderstood. You've heard the consumer hype, but what gets less ink are the transformative changes that autonomous vehicles will bring -- in some cases already are bringing -- to the enterprise. Affecting sectors as disparate as shipping and logistics, energy, agriculture, transportation, construction, and infrastructure -- to name just a few -- it's hard to overstate the impact of the diverse and versatile set of technologies lumped into the decidedly broad category of'autonomous vehicles'. This guide will help you sort the hype from the business reality and tell you all you need to know about the autonomous vehicle revolution on the ground, in the air, and even at sea. In 1939, General Motors predicted we'd have an autonomous vehicle highway system up and running by the dawn of the 1960s. As with a lot of autonomous vehicle hype, that prediction was a tad premature, but it demonstrates the long history of autonomous vehicle development.

The Mat\'ern covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the Mat\'ern class is that it is possible to get precise control over the degree of differentiability of the process realizations. However, the Mat\'ern class possesses exponentially decaying tails, and thus may not be suitable for modeling long range dependence. This problem can be remedied using polynomial covariances; however one loses control over the degree of differentiability of the process realizations, in that the realizations using polynomial covariances are either infinitely differentiable or not differentiable at all. We construct a new family of covariance functions using a scale mixture representation of the Mat\'ern class where one obtains the benefits of both Mat\'ern and polynomial covariances. The resultant covariance contains two parameters: one controls the degree of differentiability near the origin and the other controls the tail heaviness, independently of each other. Using a spectral representation, we derive theoretical properties of this new covariance including equivalence measures and asymptotic behavior of the maximum likelihood estimators under infill asymptotics. The improved theoretical properties in predictive performance of this new covariance class are verified via extensive simulations. Application using NASA's Orbiting Carbon Observatory-2 satellite data confirms the advantage of this new covariance class over the Mat\'ern class, especially in extrapolative settings.

A referring expression (RE) is a description that identifies a set of instances unambiguously. Mining REs from data finds applications in natural language generation, algorithmic journalism, and data maintenance. Since there may exist multiple REs for a given set of entities, it is common to focus on the most intuitive ones, i.e., the most concise and informative. In this paper we present REMI, a system that can mine intuitive REs on large RDF knowledge bases. Our experimental evaluation shows that REMI finds REs deemed intuitive by users. Moreover we show that REMI is several orders of magnitude faster than an approach based on inductive logic programming.

Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computationally intensive simulations must be run. We show that deep neural networks can be used on a small set of numerical weather simulations to estimate the spread of a weather forecast, significantly reducing computational cost. To train the system, we both modify the 3D U-Net architecture and explore models that incorporate temporal data. Our models serve as a starting point to improve uncertainty quantification in current real-time weather forecasting systems, which is vital for predicting extreme events.

We present a new feature extraction method for complex and large datasets, based on the concept of transport operators on graphs. The proposed approach generalizes and extends the many existing data representation methodologies built upon diffusion processes, to a new domain where dynamical systems play a key role. The main advantage of this approach comes from the ability to exploit different relationships than those arising in the context of e.g., Graph Laplacians. Fundamental properties of the transport operators are proved. We demonstrate the flexibility of the method by introducing several diverse examples of transformations. We close the paper with a series of computational experiments and applications to the problem of classification of hyperspectral satellite imagery, to illustrate the practical implications of our algorithm and its ability to quantify new aspects of relationships within complicated datasets.

Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an integral operator. Nonlinearity or temporal dependence in the dynamics is often captured by allowing the operator parameters to vary temporally, or by re-fitting a model with a temporally-invariant linear operator at each time point in a sliding window. Both procedures tend to be excellent for prediction purposes over small time horizons, but are generally time-consuming and, crucially, do not provide a global prior model for the temporally-varying dynamics that is realistic. Here, we tackle these two issues by using a deep convolution neural network (CNN) in a hierarchical statistical IDE framework, where the CNN is designed to extract process dynamics from the process' most recent behaviour. Once the CNN is fitted, probabilistic forecasting can be done extremely quickly online using an ensemble Kalman filter with no requirement for repeated parameter estimation. We conduct an experiment where we train the model using 13 years of daily sea-surface temperature data in the North Atlantic Ocean. Forecasts are seen to be accurate and calibrated. A key advantage of our approach is that the CNN provides a global prior model for the dynamics that is realistic, interpretable, and computationally efficient. We show the versatility of the approach by successfully producing 10-minute nowcasts of weather radar reflectivities in Sydney using the same model that was trained on daily sea-surface temperature data in the North Atlantic Ocean.

This paper presents a new clustering algorithm for space-time data based on the concepts of topological data analysis and in particular, persistent homology. Employing persistent homology - a flexible mathematical tool from algebraic topology used to extract topological information from data - in unsupervised learning is an uncommon and a novel approach. A notable aspect of this methodology consists in analyzing data at multiple resolutions which allows to distinguish true features from noise based on the extent of their persistence. We evaluate the performance of our algorithm on synthetic data and compare it to other well-known clustering algorithms such as K-means, hierarchical clustering and DBSCAN. We illustrate its application in the context of a case study of water quality in the Chesapeake Bay.

LOS ALAMOS, N.M., Oct. 22, 2019--A probabilistic artificial intelligence computer model developed at Los Alamos National Laboratory provided the most accurate state, national and regional forecasts of the flu in 2018, beating 23 other teams in the Centers for Disease Control and Prevention's FluSight Challenge. The CDC announced the results last week. "Accurately forecasting diseases is similar to weather forecasting in that you need to feed computer models large amounts of data so they can'learn' trends," said Dave Osthus, a statistician at Los Alamos and developer of the computer model, Dante. "But it's very different because disease spread depends on daily choices humans make in their behavior--such as travel, hand-washing, riding public transportation, interacting with the healthcare system, among other things. Those are very difficult to predict."

The Mayflower Autonomous Ship (MAS) will begin its journey on 6 September 2020 and cross the Atlantic Ocean, from Plymouth to Plymouth. Like its namesake in 1620, MAS will rely to some extent on favourable weather to complete its crossing as it will be powered by state-of-the-art hybrid propulsion system, utilizing wind, solar, state-of-the-art batteries, and a diesel generator. MAS will carry three research pods containing myriad sensors that scientists will utilize to conduct persistent, ground-breaking research in meteorology, oceanography, climatology, biology, marine pollution and conservation, and autonomous navigation. MAS is being coordinated through a partnership lead by ProMare, a non-profit charity established to promote marine research and exploration throughout the world. The research pods will be coordinated by Plymouth University, a world-leading centre of excellence for marine and maritime education, research and innovation.

An autonomous boat under developments could be the first ship to cross the Atlantic that is able to navigate around ships and other hazards by itself. The Mayflower Autonomous Ship (MAS) is an autonomous vessel due to depart from Plymouth in England on the fourth centenary of the original Mayflower voyage, on 6 September 2020, with its destination Plymouth, USA. The project was put together by marine research and exploration company ProMare in an effort to expand the scope of marine research. The boat will carry three research pods equipped with scientific instruments to measure various phenomena such as ocean plastics, mammal behaviour or sea level changes. IBM has now joined the initiative, and it will supply technical support for all navigation operations. The Mayflower Autonomous Ship (MAS) is an unmanned vessel set to depart from Plymouth in England on the fourth centenary of the original Mayflower voyage.