Measurements of laminar flame speeds of acetone/methane/air mixtures

The effect of acetone on the laminar flame speed of methane/air mixtures is investigated over a range of stoichiometries at atmospheric pressure and room temperature. The liquid acetone is vaporised and seeded into the methane/air mixture at 5%, 9% and 20% of the total fuel by mole. The experiment is performed using the jet-wall stagnation flame configuration and the particle imaging velocimetry (PIV) technique. Laminar flame speeds are derived by extrapolating the reference flame speed back to zero strain rate. Experimental results are compared to numerically calculated values using a base methane chemical kinetic mechanism (GRI-Mech 3.0) extended with acetone oxidation and pyrolysis reactions from the literature. The experimental results show that acetone addition does not affect the laminar flame speed of methane significantly within the range of concentrations considered, with a stronger effect on the rich range than under fuel-lean conditions, and that the peak laminar flame speed of acetone in air is ~42.5 cm/s at ϕ = 1.2. Simulation results reveal that the most important reactions determining acetone laminar flame speeds are H + O2 → O + OH, OH + CO → H + CO2, HO2 + CH3 → OH + CH3O and H + O2 + H2O → HO2 + H2O. Comparison of the expected disappearance of acetone relative to methane shows that the former is a good fluorescent marker for the latter

Top-N Recommender System via Matrix Completion

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.Comment: AAAI 201

Twin Learning for Similarity and Clustering: A Unified Kernel Approach

Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise and outliers. Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering. In addition, nonlinear similarity often exists in many real world data which, however, has not been effectively considered by most existing methods. To tackle these two challenges, we propose a model to simultaneously learn cluster indicator matrix and similarity information in kernel spaces in a principled way. We show theoretical relationships to kernel k-means, k-means, and spectral clustering methods. Then, to address the practical issue of how to select the most suitable kernel for a particular clustering task, we further extend our model with a multiple kernel learning ability. With this joint model, we can automatically accomplish three subtasks of finding the best cluster indicator matrix, the most accurate similarity relations and the optimal combination of multiple kernels. By leveraging the interactions between these three subtasks in a joint framework, each subtask can be iteratively boosted by using the results of the others towards an overall optimal solution. Extensive experiments are performed to demonstrate the effectiveness of our method.Comment: Published in AAAI 201