Sleeping site selection by the golden-handed tamarin Saguinus midas midas: The role of predation risk, proximity to feeding sites, and territorial defence

Tamarin monkeys, of the genus Saguinus, spend over half their lives at arboreal sleeping sites. The decision as to which site to use is likely to have considerable fitness consequences. These decisions about sleeping sites by three troops of golden-handed tamarin Saguinus midas midas were examined over a 9-mo period at a rainforest site in French Guiana. Data are presented on the physical nature of sleeping sites, their number, position within home ranges, and pattern of use and reuse, aspects of behaviour at retirement and egress, and predation attempts on the study troops. Cumulative plot analysis indicated that a tamarin troop used 30-40 sleeping sites in a 100-day period, approximately half of which were used very infrequently, so that consecutive reuse was never greater than three nights. Sleeping trees were superior in architectural parameters and liana weight to non-sleeping trees. There were no more sleeping sites than expected within the home range boundary region of the tamarins or in areas of overlap with the home ranges of neighbouring troops. Tamarins selected sleeping sites nearest to the last feeding site of the day on 25% of occasions. The study troops engaged in a number of activities that may reduce predation risk; raptor attacks on the study troops over 9 mo were frequent but unsuccessful. Tamarins often visited a sleeping site several hours before arrival, and were more likely to visit a site before use if they had not used it recently. The decision to select a sleeping site therefore involved knowledge of the previous frequency of use of that site.

Feature Selection for Pose Invariant Lip Biometrics

For the first time in this paper the authors present results showing the effect of out of plane speaker head pose variation on a lip biometric based speaker verification system. Using appearance DCT based features, they adopt a Mutual Information analysis technique to highlight the class discriminant DCT components most robust to changes in out of plane pose. Experiments are conducted using the initial phase of a new multi view Audio-Visual database designed for research and development of pose-invariant speech and speaker recognition. They show that verification performance can be improved by substituting higher order horizontal DCT components for vertical, particularly in the case of a train/test pose angle mismatch.

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

Robust face recognition with partial occlusion, illumination variation and limited training data by optimal feature selection

This study investigates face recognition with partial occlusion, illumination variation and their combination, assuming no prior information about the mismatch, and limited training data for each person. The authors extend their previous posterior union model (PUM) to give a new method capable of dealing with all these problems. PUM is an approach for selecting the optimal local image features for recognition to improve robustness to partial occlusion. The extension is in two stages. First, authors extend PUM from a probability-based formulation to a similarity-based formulation, so that it operates with as little as one single training sample to offer robustness to partial occlusion. Second, they extend this new formulation to make it robust to illumination variation, and to combined illumination variation and partial occlusion, by a novel combination of multicondition relighting and optimal feature selection. To evaluate the new methods, a number of databases with various simulated and realistic occlusion/illumination mismatches have been used. The results have demonstrated the improved robustness of the new methods.

Selection for cuticular melanism reveals immune function and life-history trade-offs in Spodoptera littoralis

Several insect species show an increase in cuticular melanism in response to high densities. In some species, there is evidence that this melanism is correlated with an up-regulation of certain immune system components, particularly phenoloxidase (PO) activity, and with the down-regulation of lysozyme activity, suggesting a trade-off between the two traits. As melanism has a genetic component, we selected both melanic and nonmelanic lines of the phase-polyphenic lepidopteran, Spodoptera littoralis, in order to test for a causative genetic link between melanism, PO activity and lysozyme activity, and to establish if there are any life-history costs associated with the melanic response. We found that, in fact, melanic lines had lower PO activity and higher lysozyme activity than nonmelanic lines, confirming a genetic trade-off between the two immune responses, but also indicating a genetic trade-off between melanism and PO activity. In addition, we found that lines with high PO activity had slower development rates suggesting that investment in PO, rather than in melanism, is costly.

Locally regularised two-stage learning algorithm for RBF network centre selection

Nonlinear models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually network dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsomous network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise.

Safe Therapeutic Economic Pharmaceutical Selection (STEPS): development, introduction and use in Northern Ireland

A number of medicine selection methods have been used worldwide for formulary purposes. In Northern Ireland, integrated medicines management is being developed, and related projects have been carried out. This paper deals with the description of the STEPS (Safe Therapeutic Economic Pharmaceutical Selection) programme. The paper outlines the development of STEPS and its application as an element of a cost-effective medicines-management process in Northern Ireland.