GPS falls from the sky and costs nothing to use, but it may not reach a car roving the canyons of Manhattan or a forklift moving boxes in a warehouse. For uninterrupted autonomous driving, you need some backup. Sure, you can festoon your vehicles with a vast array of overlapping sensors, but even that won't always give you a clear sense of where you are. So, when the GPS satellites can't pinpoint you, why not resort to land-based beacons? That's the solution proposed by 5D Robotics, a Carlsbad, Calif.

We focus on the problem of estimating and quantifying uncertainties on the excursion set of a function under a limited evaluation budget. We adopt a Bayesian approach where the objective function is assumed to be a realization of a Gaussian random field. In this setting, the posterior distribution on the objective function gives rise to a posterior distribution on excursion sets. Several approaches exist to summarize the distribution of such sets based on random closed set theory. While the recently proposed Vorob'ev approach exploits analytical formulae, further notions of variability require Monte Carlo estimators relying on Gaussian random field conditional simulations. In the present work we propose a method to choose Monte Carlo simulation points and obtain quasi-realizations of the conditional field at fine designs through affine predictors. The points are chosen optimally in the sense that they minimize the posterior expected distance in measure between the excursion set and its reconstruction. The proposed method reduces the computational costs due to Monte Carlo simulations and enables the computation of quasi-realizations on fine designs in large dimensions. We apply this reconstruction approach to obtain realizations of an excursion set on a fine grid which allow us to give a new measure of uncertainty based on the distance transform of the excursion set. Finally we present a safety engineering test case where the simulation method is employed to compute a Monte Carlo estimate of a contour line.

Computer manufacturer Atos has named its first customer for Bull sequana, a supercomputer design it hopes will reach exaflop levels of performance by 2020. Atos is building the computer for the French Alternative Energies and Atomic Energy Commission (CEA), it said Tuesday. By targeting 2020 for delivery of an exaflop supercomputer, Atos is entering a race in which China and Japan may already have a head-start. An exaflop is a billion billion floating-point operations per second (flops). That's way more than today's fastest machine can manage: China's Tianhe-2 has a maximum performance of 33.9 petaflops (millions of billions of flops), according to the November 2015 edition of the Top500 supercomputer rankings.

With oil and gas prices hovering at decade lows, companies are turning to artificial intelligence to cut costs and boost productivity. The technology, which gives companies the ability to predict future problems, is estimated to save the industry trillions of dollars and lead to a new wave of highly sophisticated jobs. GE Oil and Gas is at the forefront of the shift, using artificial intelligence software to help producers become more efficient. The company's regional director, Mary Hackett, said the recent downturn in prices was driving interest in the technology. "We now need to rather than add to production, we need to make production more efficient and it's that, that will change this industry," she said.

With oil and gas prices hovering at decade lows, companies are turning to artificial intelligence to cut costs and boost productivity. The technology, which gives companies the ability to predict future problems, is estimated to save the industry trillions of dollars and lead to a new wave of highly sophisticated jobs. GE Oil and Gas is at the forefront of the shift, using artificial intelligence software to help producers become more efficient. The company's regional director, Mary Hackett, said the recent downturn in prices was driving interest in the technology. "We now need to rather than add to production, we need to make production more efficient and it's that that will change this industry," she said.

This paper explores the problem of managing movements of aircraft along the surface of busy airports. Airport surface management is a complex logistics problem involving the coordination of humans and machines. The work described here arose from the idea that autonomous towing vehicles for taxiing aircraft could offer a solution to the 'capacity problem' for busy airports, the problem of getting more efficient use of existing surface area to meet increasing demand. Supporting autonomous surface operations requires continuous planning, scheduling and monitoring of operations, as well as systems for optimizing complex human-machine interaction. We identify a set of computational subproblems of the surface management problem that would benefit from recent advances in multi-agent planning and scheduling and probabilistic predictive modeling, and discuss preliminary work at integrating these components into a prototype of a surface management system.

Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However structure of social networks and node attributes are often interpreted separately in the research of community detection. When these two sources are interpreted simultaneously, one common as- sumption shared by previous studies is that nodes attributes are correlated with communities. In this paper, we present a model that is capable of combining topology information and nodes attributes information with- out assuming correlation. This new model can recover communities with higher accuracy even when node attributes and communities are uncorre- lated. We derive the detectability threshold for this model and use Belief Propagation (BP) to make inference. This algorithm is optimal in the sense that it can recover community all the way down to the threshold. This new model is also with the potential to handle edge content and dynamic settings.

Planning with hybrid domains modelled in PDDL+ has been gaining research interest in the Automated Planning community in recent years. Hybrid domain models capture a more accurate representation of real world problems that involve continuous processes than is possible using discrete systems. However, solving problems represented as PDDL+ domains is very challenging due to the construction of complex system dynamics, including non-linear processes and events. In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-linear system dynamics governing the continuous evolution of states. DiNo is based on the discretise-and-validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic, which is introduced in this paper. Although several planners have been developed to work with subsets of PDDL+ features, or restricted forms of processes, DiNo is currently the only heuristic planner capable of handling non-linear system dynamics combined with the full PDDL+ feature set.

Wireless sensor networks play a major role in smart grids and smart buildings. They are not just used for sensing, but they are also used as actuating. In terms of sensing they are used to measure temperature, humidity, light, to detect motion, etc. Sensors are often operated on a battery and hence we often face a trade-off between obtaining frequent sensor readings versus maximizing their battery life. There have been several approaches to maximizing their battery life from hardware level to software level such as reducing components energy consumption, limiting node operation capabilities, using power-aware routing protocols, and adding solar energy support. In this paper, we introduce a novel approach: we model the sensor readings in a wireless network using a dynamic Gaussian Bayesian network (dGBn) whose structure is automatically learned from data. dGBn allows us to integrate information across sensors and infer missing readings more accurately. Through active inference for dGBns, we are able to actively choose which sensors should be pulled for a reading and which ones can stay in a power-saving mode at each time step, maximizing prediction accuracy while staying within the budgetary constraints on battery consumption.

Utilities use Demand Response (DR) to balance supply and demand in the electric grid by involving customers in efforts to reduce electricity consumption during peak periods. To implement and adapt DR under dynamically changing conditions of the grid, reliable prediction of reduced consumption is critical. However, despite the wealth of research on electricity consumption prediction and DR being long in practice, the problem of reduced consumption prediction remains largely un-addressed. In this paper, we identify unique computational challenges associated with the prediction of reduced consumption and contrast this to that of normal consumption and DR baseline prediction. We propose a novel ensemble model that leverages different sequences of daily electricity consumption on DR event days as well as contextual attributes for reduced consumption prediction. We demonstrate the success of our model on a large, real-world, high resolution dataset from a university microgrid comprising of over 950 DR events across a diverse set of 32 buildings. Our model achieves an average error of 13.5%, an 8.8% improvement over the baseline. Our work is particularly relevant for buildings where electricity consumption is not tied to strict schedules. Our results and insights should prove useful to the researchers and practitioners working in the sustainable energy domain.