Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus

In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential regions in the system's surroundings. To overcome the limitations of these inflexible models, we present GP-LAPLACE, a technique for locating sources and sinks from trajectories in time-varying fields. Using Gaussian processes, we jointly infer a spatio-temporal vector field, as well as canonical vector calculus operations on that field. Notably, we do this from only agent trajectories without requiring knowledge of the environment, and also obtain a metric for denoting the significance of inferred causal features in the environment by exploiting our probabilistic method. To evaluate our approach, we apply it to both synthetic and real-world GPS data, demonstrating the applicability of our technique in the presence of multiple agents, as well as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures, conference submission, University of Oxford. arXiv admin note: text overlap with arXiv:1709.0235

Assessing a New Clue to How Much Carbon Plants Take Up

Current climate models disagree on how much carbon dioxide land ecosystems take up for photosynthesis. Tracking the stronger carbonyl sulfide signal could help

Physical Connectivity Between the NE Atlantic Seamounts

Within the Portuguese Exclusive Economic Zone, the Great Meteor and Madeira-Tore complexes are highly productive areas, which are likely to be classified as marine protected areas (MPAs) due to their ecological vulnerability. This was the main focus of the BIOMETORE project and, framed on it, the aim of the present study was to investigate the physical connectivity between both seamount complexes. Using the HYbrid Coordinate Ocean Model coupled with the Connectivity Modeling System (CMS) (a Lagrangian tool), a series of experiments was conducted in order to determine the influence of the main oceanographic phenomena governing the area in: (i) the origin of the particles that reach each complex, (ii) their capacity to capture and retain incoming particles, and (iii) the physical connectivity between them as well as the intra-connectivity within each seamount system. Due to the geographical location of both groups of seamounts, the Azores Current (AzC) and its associated eddies were identified as the main transport pathways, its influence being stronger at intermediate waters and decreasing with depth. Notwithstanding, the Great Meteor and the MadeiraTore were mainly affected by the AzC southward and eastward branches, respectively, resulting in a non-significant connectivity between the two groups. Meanwhile, the inter-connectivity between seamounts slightly varied with depth at the Great Meteor complex while increasing at Madeira-Tore. In addition, the Plateau, Irving, and Cruiser (PIC) seamounts from the Great Meteor complex and Gorringe and Coral from the Madeira-Tore complex proved to incorporate the regional connectivity routes. Although containing the three smallest seamounts, Madeira-Tore showed the higher capturing capacity per square kilometer, highlighting the influence of the "sticky water effect." In the Great Meteor complex, the "seamount effect" seems to be the main phenomenon responsible for the greater retention and self-recruitment abilities of these seamounts. The presented results provide valuable information for the design of a MPA to preserve these vulnerable habitat

Transition between advection and inertial wave propagation in rotating turbulence

In turbulent flows subject to strong background rotation, the advective mechanisms of turbulence are superseded by the propagation of inertial waves, as the effects of rotation become dominant. While this mechanism has been identified experimentally (Dickinson & Long, J. Fluid Mech., vol. 126, 1983, pp. 315–333; Davidson, Staplehurst & Dalziel, J. Fluid Mech., vol. 557, 2006, pp. 135–144; Staplehurst, Davidson & Dalziel, J. Fluid Mech., vol. 598, 2008, pp. 81–105; Kolvin et al.Phys. Rev. Lett., vol. 102, 2009, 014503), the conditions of the transition between the two mechanisms are less clear. We tackle this question experimentally by tracking the turbulent front away from a solid wall where jets enter an otherwise quiescent fluid. Without background rotation, this apparatus generates a turbulent front whose displacement recovers the law classically obtained with an oscillating grid (Dickinson & Long, Phys. Fluids, vol. 21 (10), 1978, pp. 1698–1701) and we further establish the scale independence of the associated transport mechanism. When the apparatus is rotating at a constant velocity perpendicular to the wall where fluid is injected, not only does the turbulent front become mainly transported by inertial waves, but advection itself is suppressed because of the local deficit of momentum incurred by the propagation of these waves. Scale-by-scale analysis of the displacement of the turbulent front reveals that the transition between advection and propagation is local both in space and spectrally, and takes place when the Rossby number based on the considered scale is of order unity, or equivalently, when the scale-dependent group velocity of inertial waves matched the local advection velocity

Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.

A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results in an annotation that is significantly more accurate than what would be obtained by frame-by-frame evaluation of the classifier output. The framework has been implemented and applied successfully to the analysis of team sports with a single camera. Key words: Visua

A database of optimal integration times for Lagrangian studies of atmospheric moisture sources and sinks

Lagrangian methods for estimating sources and sinks of water vapour have increased in importance in recent years, with hundreds of publications over the past decade on this topic. Results derived from these approaches are, however, very sensitive to the integration time of the trajectories used in the analysis. The most widely used integration time is that derived from the average residence time of water vapour in the atmosphere, normally considered to be around 10 days. In this article, we propose an approach to estimate the optimal integration time for these Lagrangian methods for estimating sources and sinks, by comparing estimates of precipitation from the Lagrangian approach using different times of integration with results obtained from three state-of-the-art reanalyses, thereby providing a database of optimal integration times per month, for a spatial resolution of 0.25° × 0.25° in latitude and longitude.Ministerio de Ciencia e Innovación | Ref. CGL2015-65141-RXunta de Galicia | Ref. ED431C 2017/64-GR

Definition, Capabilities, and Components of a Terrestrial Carbon Monitoring System

Research efforts for effectively and consistently monitoring terrestrial carbon are increasing in number. As such, there is a need to define carbon monitoring and how it relates to carbon cycle science and carbon management. There is also a need to identify capabilities of a carbon monitoring system and the system components needed to develop the capabilities. Capabilities that enable the effective application of a carbon monitoring system for monitoring and management purposes may include: reconciling carbon stocks and fluxes, developing consistency across spatial and temporal scales, tracking horizontal movement of carbon, attribution of emissions to originating sources, cross-sectoral accounting, uncertainty quantification, redundancy and policy relevance. Focused research is needed to integrate these capabilities for sustained estimates of carbon stocks and fluxes. Additionally, if monitoring is intended to inform management decisions, management priorities should be considered prior to development of a monitoring system