Leaky doors: private captivity as a prominent source of bird introductions in Australia

The international pet trade is a major source of emerging invasive vertebrate species. We used online resources as a novel source of information for accidental bird escapes, and we investigated the factors that influence the frequency and distribution of bird escapes at a continental scale. We collected information on over 5,000 pet birds reported to be missing on animal websites during the last 15 years in Australia. We investigated whether variables linked to pet ownership successfully predicted bird escapes, and we assessed the potential distribution of these escapes. Most of the reported birds were parrots (> 90%), thus, we analysed factors associated with the frequency of parrot escapes. We found that bird escapes in Australia are much more frequent than previously acknowledged. Bird escapes were reported more frequently within, or around, large Australian capital cities. Socio-economic factors, such as the average personal income level of the community, and the level of human modification to the environment were the best predictors of bird escapes. Cheaper parrot species, Australian natives, and parrot species regarded as peaceful or playful were the most frequently reported escapees. Accidental introductions have been overlooked as an important source of animal incursions. Information on bird escapes is available online in many higher income countries and, in Australia, this is particularly apparent for parrot species. We believe that online resources may provide useful tools for passive surveillance for non-native pet species. Online surveillance will be particularly relevant for species that are highly reported, such as parrots, and species that are either valuable or highly commensal.Miquel Vall-llosera, Phillip Casse

Evidence of a chimpanzee-sized ancestor of humans but a gibbon-sized ancestor of apes

Body mass directly affects how an animal relates to its environment and has a wide range of biological implications. However, little is known about the mass of the last common ancestor (LCA) of humans and chimpanzees, hominids (great apes and humans), or hominoids (all apes and humans), which is needed to evaluate numerous paleobiological hypotheses at and prior to the root of our lineage. Here we use phylogenetic comparative methods and data from primates including humans, fossil hominins, and a wide sample of fossil primates including Miocene apes from Africa, Europe, and Asia to test alternative hypotheses of body mass evolution. Our results suggest, contrary to previous suggestions, that the LCA of all hominoids lived in an environment that favored a gibbon-like size, but a series of selective regime shifts, possibly due to resource availability, led to a decrease and then increase in body mass in early hominins from a chimpanzee-sized LCA

Mel-frequency cepstral coefficients for eye movement identification

Human identification is an important task for various activities in society. In this paper, we consider the problem of human identification using eye movement information. This problem, which is usually called the eye movement identification problem, can be solved by training a multiclass classification model to predict a person's identity from his or her eye movements. In this work, we propose using Mel-frequency cepstral coefficients (MFCCs) to encode various features for the classification model. Our experiments show that using MFCCs to represent useful features such as eye position, eye difference, and eye velocity would result in a much better accuracy than using Fourier transform, cepstrum, or raw representations. We also compare various classification models for the task. From our experiments, linear-kernel SVMs achieve the best accuracy with 93.56% and 91.08% accuracy on the small and large datasets respectively. Besides, we conduct experiments to study how the movements of each eye contribute to the final classification accuracy. © 2012 IEEE

Generalization and robustness of batched weighted average algorithm with V-geometrically ergodic Markov data

We analyze the generalization and robustness of the batched weighted average algorithm for V-geometrically ergodic Markov data. This algorithm is a good alternative to the empirical risk minimization algorithm when the latter suffers from overfitting or when optimizing the empirical risk is hard. For the generalization of the algorithm, we prove a PAC-style bound on the training sample size for the expected L1-loss to converge to the optimal loss when training data are V-geometrically ergodic Markov chains. For the robustness, we show that if the training target variable's values contain bounded noise, then the generalization bound of the algorithm deviates at most by the range of the noise. Our results can be applied to the regression problem, the classification problem, and the case where there exists an unknown deterministic target hypothesis. © 2013 Springer-Verlag

Learning from non-iid data: Fast rates for the one-vs-all multiclass plug-in classifiers

We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution. These are two typical scenarios where training data are not iid. The learning rates are obtained under a multiclass version of Tsybakov’s margin assumption, a type of low-noise assumption, and do not depend on the number of classes. Our results are general and include a previous result for binaryclass plug-in classifiers with iid data as a special case. In contrast to previous works for least squares SVMs under the binary-class setting, our results retain the optimal learning rate in the iid case

Bayesian Pool-based Active Learning With Abstention Feedbacks

We study pool-based active learning with abstention feedbacks, where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated abstention rate into the greedy criteria. We prove that both of our algorithms have near-optimality guarantees: they respectively achieve a ${(1-\frac{1}{e})}$ constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios