Graph contrastive learning with global trajectory flow for next-POI recommendation

SUMMARYThe ability to construct time-trajectories of mantle flow is crucial to move from studies of instantaneous to time-dependent earth models and to exploit geological constraints for mantle convection modelling. However mantle convection is chaotic and subject to the butterfly effect: the trajectories of two identical mantle convection models initialized with slightly different temperature fields diverge exponentially in time until they become uncorrelated. Because one may use seismic inferences about the mantle state as a starting or terminal condition to project mantle flow forward or backward in time, and because the seismic inference is invariably subject to uncertainties, this seemingly would rule out any construction of robust mantle flow trajectories. Here we build upon earlier work which showed that assimilation of the horizontal component of the surface velocity field from a known reference model allows one to overcome the butterfly effect and to construct robust mantle flow trajectories, regardless of the choice of the initial state perturbation. To this end, we use high resolution 3-D spherical mantle convection models in four end-member configurations: an isoviscous purely internally heated model, an isoviscous purely bottom heated model, a model with a radial increase in viscosity along with pure internal heating as well as a model that combines the effects of radial viscosity increase, internal and bottom heating. In order to capture the impact of seismic filtering, we perturb the initial temperature fields of these end-member models through either radial or horizontal smoothing of the temperature field or the application of the tomographic filter of seismic model S20RTS. We assess the quality of the constructed model trajectories via a number of statistical measures as well as comparisons of their dynamic topography histories. The latter is an essential step since mantle flow cannot be directly observed but has to be inferred via its surface manifestations. Importantly, linking mantle flow to surface observations yields patterns representable on a latitude–longitude grid similar to meteorological observables such as precipitation. This invites the application of meteorological quality metrics, such as the power ratio and Taylor diagram, to assess the quality of mantle flow trajectories. We introduce these metrics for the first time in the context of mantle convection and demonstrate their viability based on the compact manner in which they summarize model performance.

As leaders of the Unknown Fields Division at the Architectural Association (AA) School of Architecture in London, Liam Young and Kate Davies take their students on trips to the end of the earth. Cataloguing extreme territories, they investigate the role of design in developing new cultural relationships. Fictional speculation is used as an instrument for understanding emerging environmental and technological futures, in a process of experiencing, chronicling and, ultimately, reimagining the urban and the remote, and charting global flows and trajectories. Here they describe a speculative supply chain that begins below the surface of the Western Desert in outback Australia, and continues to the Arctic ‘Northwest Passage’ and on to London.

The quality of a crowd simulation model is determined by its agents' local and global trajectory efficiency. While an agent-based model can accurately handle the local trajectories, global decisions usually are handled by a global path planner. However, most of the global path planning techniques do not consider other agents and their possible paths and the future global flow in the environment. In this paper, we propose a composite system that takes future agent configurations into account via a modified A* algorithm to create a global path plan and combines the global path plan with a local navigation model. We show that the agents using the proposed model intelligently plan their paths based on the dynamic configuration of the environment. In order to balance the performance vs. trajectory quality trade-off, we propose a hierarchical grid structure and discuss its effects on both trajectory quality and computational performance.