Combustor technology for future aircraft

The continuing improvement of aircraft gas turbine engine operating efficiencies involves increases in overall engine pressure ratio increases that will result in combustor inlet pressure and temperature increases, greater combustion temperature rises, and higher combustor exit temperatures. These conditions entail the development of fuel injectors generating uniform circumferential and radial temperature patterns, as well as combustor liner configurations and materials capable of withstanding increased thermal radiation even as the amount of cooling air is reduced. Low NO(x)-emitting combustor concepts are required which will employ staged combustion. The development status of component technologies answering these requirements are presently evaluated

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process

Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performance of these methods has been assessed primarily in the context of Bayesian topic models, particularly latent Dirichlet allocation (LDA). Deriving several new algorithms, and using synthetic, image and genomic datasets, we investigate whether the understanding gleaned from LDA applies in the setting of sparse latent factor models, specifically beta process factor analysis (BPFA). We demonstrate that the big picture is consistent: using Gibbs sampling within SVI to maintain certain posterior dependencies is extremely effective. However, we find that different posterior dependencies are important in BPFA relative to LDA. Particularly, approximations able to model intra-local variable dependence perform best.Comment: ICML, 12 pages. Volume 37: Proceedings of The 32nd International Conference on Machine Learning, 201

Observable Persistent Effects of Habitat Management Efforts in the Ozark Highlands After 10 Years

I investigated the lasting impacts of a management plan designed to improve oak regeneration and benefit wildlife in the Ozark Highlands in Madison, Co., AR. To assess the efficacy of the management plan, I used variables relevant to the success and establishment of oak trees. Controlled burns and selective logging were used to thin the canopy, increase ground level productivity, and increase the abundance of small mammals. I used measurements of overstory and understory densities, light availability, and the density of mice in the genus Peromyscus across time to look at the lasting impacts of management. Different treatment plots were used to investigate the impact of each management action separately (Burn or Cut) and in combination (Burn and Cut) relative to unaltered control plots. Measurements were compared between pre-treatment, post-treatment, and 10-years post-treatment time points. I found that a 10-year lapse in management resulted in a complete return to pre-treatment values in overstory density. I also saw a decline below pre-treatment values in understory density and Peromyscus density. Analysis of light availability at the forest floor revealed a persistent effect of treatment. I conclude that while initial treatment was effective, 10 years between management events is too infrequent to achieve the desired long-term changes within my study system. More frequent management may be more effective in meeting the management goals for this Ozark system

A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles of phylogenies

Divergence date estimates are central to understand evolutionary processes and depend, in the case of molecular phylogenies, on tests of molecular clocks. Here we propose two non-parametric tests of strict and relaxed molecular clocks built upon a framework that uses the empirical cumulative distribution (ECD) of branch lengths obtained from an ensemble of Bayesian trees and well known non-parametric (one-sample and two-sample) Kolmogorov-Smirnov (KS) goodness-of-fit test. In the strict clock case, the method consists in using the one-sample Kolmogorov-Smirnov (KS) test to directly test if the phylogeny is clock-like, in other words, if it follows a Poisson law. The ECD is computed from the discretized branch lengths and the parameter $\lambda$ of the expected Poisson distribution is calculated as the average branch length over the ensemble of trees. To compensate for the auto-correlation in the ensemble of trees and pseudo-replication we take advantage of thinning and effective sample size, two features provided by Bayesian inference MCMC samplers. Finally, it is observed that tree topologies with very long or very short branches lead to Poisson mixtures and in this case we propose the use of the two-sample KS test with samples from two continuous branch length distributions, one obtained from an ensemble of clock-constrained trees and the other from an ensemble of unconstrained trees. Moreover, in this second form the test can also be applied to test for relaxed clock models. The use of a statistically equivalent ensemble of phylogenies to obtain the branch lengths ECD, instead of one consensus tree, yields considerable reduction of the effects of small sample size and provides again of power.Comment: 14 pages, 9 figures, 8 tables. Minor revision, additin of a new example and new title. Software: https://github.com/FernandoMarcon/PKS_Test.gi

Semi-parametric analysis of multi-rater data

Datasets that are subjectively labeled by a number of experts are becoming more common in tasks such as biological text annotation where class definitions are necessarily somewhat subjective. Standard classification and regression models are not suited to multiple labels and typically a pre-processing step (normally assigning the majority class) is performed. We propose Bayesian models for classification and ordinal regression that naturally incorporate multiple expert opinions in defining predictive distributions. The models make use of Gaussian process priors, resulting in great flexibility and particular suitability to text based problems where the number of covariates can be far greater than the number of data instances. We show that using all labels rather than just the majority improves performance on a recent biological dataset

Human behavioural analysis with self-organizing map for ambient assisted living

This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints