Evaluating the role of fire disturbance in structuring small reptile communities in temperate forests

Fire is an integral disturbance shaping forest community dynamics over large scales. However, understanding the relationship between fire induced habitat disturbance and biodiversity remain equivocal. Ecological theories including the intermediate disturbance hypothesis (IDH) and the habitat accommodation model (HAM) offer predictive frameworks that could explain faunal responses to fire disturbances. We used an 80 year post-fire chronosequence to investigate small reptile community responses to fires in temperate forests across 74 sites. First, we evaluated if changes in species richness, abundance and evenness post-fire followed trends of prior predictions, including the IDH. Second, using competing models of fine scale habitat elements we evaluated the specific ways which fire influenced small reptiles. Third, we evaluated support for the HAM by examining compositional changes of reptile community post-fire. Relative abundance was positively correlated to age post-fire while richness and evenness showed no associations. The abundance trend was as expected based on the prior prediction of sustained population increase post-disturbance, but the trend for richness contradicted the prediction of highest diversity at intermediate levels of disturbance (according to IDH). Abundance changes were driven mainly by changes in overstorey, ground layer, and shelter, while richness and evenness did not associate with any vegetation parameter. Community composition was not strongly correlated to age since fire, thus support for the HAM was weak. Overall, in this ecosystem, frequent fire disturbances can be detrimental to small reptiles. Future studies utilizing approaches based on species traits could enhance our understanding of biodiversity patterns post-disturbance.

Multiple task transfer learning with small sample sizes

Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.

Modeling, parameter measurement and sensorless speed estimation of IPM synchronous generator for direct drive variable speed wind turbine application

This paper presents simple methods of determining parameters of interior permanent magnet (IPM) synchronous generator such as magnet flux (λM), d-axis inductance (Ld) and q-axis inductance (Lq) of IPM synchronous generator, which are used to control the wind turbine generator. These methods are simple and do not require any complex theory, signal injection or special equipment. Moreover, a sensorless speed estimator is proposed to estimate the speed of the generator without using speed sensor. The measured parameters are used in this speed estimator. The elimination of speed sensor will enhance the system robustness and reduce the design complexity and system cost for a small-scale wind turbine considered in this paper. The effectiveness of parameter measurement methods and sensorless speed estimator is demonstrated by experimental results. Experimental results show that the proposed speed estimator that uses the measured parameters can estimate the generator speed with a small error.