Public transport systems are expected to reduce pollution and contribute to sustainable development. However, disruptions in public transport such as delays may negatively affect mobility choices. To quantify delays, aggregated data from vehicle location systems are frequently used. However, delays observed at individual stops are caused inter alia by fluctuations in running times and the knock-on effects of delays occurring in other locations. Hence, in this work, we propose both a method detecting significant delays and a reference architecture, relying on the stream processing engines in which the method is implemented. The method can complement the calculation of delays defined as deviation from schedules. This provides both online rather than batch identification of significant and repetitive delays, and resilience to the limited quality of location data. The method we propose can be used with different change detectors, such as ADWIN, applied to a location data stream shuffled to individual edges of a transport graph. It can detect in an online manner at which edges statistically significant delays are observed and at which edges delays arise and are reduced. Such detections can be used to model mobility choices and quantify the impact of regular rather than random disruptions on feasible trips with multimodal trip modelling engines. The evaluation performed with the public transport data of over 2000 vehicles confirms the merits of the method and reveals that a limited-size subgraph of a transport system graph causes statistically significant delays.
The Beryllium-doped (Be-doped) diamond and Beryllium related (Be-X) co-doped diamond have been carefully investigated by the density functional theory (DFT) to explore the possibility to achieve effective and shallow n-type doping in diamond. Although the ionization energy and formation energy of interstitial/substitutional Be-doped diamond is not ideal, the introduction of Be-related co-doping techniques (Be-N/O/S) greatly improves the electrical properties in diamonds. We found, for the first time, n-type diamond doping can be realized in Be-N, Be-O and Be-S co-doped systems, among which Be-N3 has the best performance. Be-N3 has the advantages of low ionization energy (0.25 eV), low formation energy (−1.59 eV), and direct bandgap. The N-2p states play a crucial role in the conduction band edge of Be-N3 co-doped diamond. Hence, the Be-N3 could be expected to become a promising alternative for N-type shallow doping in diamond.
Traditional global sensitivity analysis (GSA) techniques, such as variance- and density-based approaches, are limited in cases where a comprehensive understanding of temporal dynamics is critical, especially for models with diverse timescales and structural complexity, such as system dynamics and agent-based models (ABMs). To address this, we propose a novel manifold learning-based method for GSA in systems exhibiting complex spatiotemporal processes. Our method employs Grassmannian diffusion maps to reduce the dimensionality of the data and polynomial chaos expansion (PCE) to map stochastic input parameters to diffusion coordinates of the reduced space. We calculate sensitivity indices from PCE coefficients, aggregating multiple outputs and their entire trajectories for a more general estimation of parameter sensitivities. We demonstrate the capabilities of the proposed approach by applying it to the Lotka-Volterra model and an epidemic dynamics ABM and capturing diverse temporal dynamics. We establish that the new methodology meets all “good” properties of a global sensitivity measure, making it a valuable alternative to traditional GSA techniques. We anticipate that it will potentially expand the application of manifold-based approaches and deepen the understanding of complex spatiotemporal processes.
Interior point methods are widely used for different types of mathematical optimization problems. Many implementations of interior point methods in use today rely on direct linear solvers to solve systems of equations in each iteration. The need to solve ever larger optimization problems more efficiently and the rise of hardware accelerators for general purpose computing has led to a large interest in using iterative linear solvers instead, with the major issue being inevitable ill-conditioning of the linear systems arising as the optimization progresses. We investigate the use of Krylov solvers for interior point methods in solving optimization problems from radiation therapy and support vector machines. We implement a prototype interior point method using a so called doubly augmented formulation of the Karush-Kuhn-Tucker linear system of equations, originally proposed by Forsgren and Gill, and evaluate its performance on real optimization problems from radiation therapy and support vector machines. Crucially, our implementation uses a preconditioned conjugate gradient method with Jacobi preconditioning internally. Our measurements of the conditioning of the linear systems indicate that the Jacobi preconditioner improves the conditioning of the systems to a degree that they can be solved iteratively, but there is room for further improvement in that regard. Furthermore, profiling of our prototype code shows that it is suitable for GPU acceleration, which may further improve its performance in practice. Overall, our results indicate that our method can find solutions of acceptable accuracy in reasonable time, even with a simple Jacobi preconditioner.
Processing of deforming shapes represented by sequences of triangle meshes with connectivity varying in time is difficult, because of the lack of temporal correspondence information, which makes it hard to exploit the temporal coherence. Establishing surface correspondence is not an easy task either, especially since some surface patches may have no corresponding counterpart in some frames, due to self-contact. Previously, it has been shown that establishing sparse correspondence via tracking volume elements might be feasible, however, previous methods suffer from severe drawbacks, which lead to tracking artifacts that compromise the applicability of the results. In this paper, we propose a new, temporally global optimisation step, which allows to improve the intermediate results obtained via forward tracking. Together with an improved formulation of volume element affinity and a robust means of identifying and removing tracking irregularities, the procedure yields a substantially better model of temporal volume correspondence.
