An illustration of variable precision rough set theory : The gender classification of the European barn swallow (Hirundo rustica)

This paper introduces a new technique in the investigation of object classification and illustrates the potential use of this technique for the analysis of a range of biological data, using avian morphometric data as an example. The nascent variable precision rough sets (VPRS) model is introduced and compared with the decision tree method ID3 (through a ‘leave n out’ approach), using the same dataset of morphometric measures of European barn swallows (Hirundo rustica) and assessing the accuracy of gender classification based on these measures. The results demonstrate that the VPRS model, allied with the use of a modern method of discretization of data, is comparable with the more traditional non-parametric ID3 decision tree method. We show that, particularly in small samples, the VPRS model can improve classification and to a lesser extent prediction aspects over ID3. Furthermore, through the ‘leave n out’ approach, some indication can be produced of the relative importance of the different morphometric measures used in this problem. In this case we suggest that VPRS has advantages over ID3, as it intelligently uses more of the morphometric data available for the data classification, whilst placing less emphasis on variables with low reliability. In biological terms, the results suggest that the gender of swallows can be determined with reasonable accuracy from morphometric data and highlight the most important variables in this process. We suggest that both analysis techniques are potentially useful for the analysis of a range of different types of biological datasets, and that VPRS in particular has potential for application to a range of biological circumstances.

Towards a set of priorities for bird conservation and research in Australia : the perceptions of ornithologists

Australian delegates at the Australasian Ornithological Conference (2007) were surveyed by questionnaire to determine their perceived research and conservation priorities for Australian birds (n = 134). Respondents were honours or postgraduate students (37.4%), academics (26.2%), wildlife managers (6.5%), land managers (6.5%), environmental consultants (5.6%), independent wildlife researchers (5.6%) or had ‘other’ occupations not relevant to birds or their management (12.1%). Respondents rated their priorities on a predetermined set of issues, and were invited to add additional priorities. ‘Conservation of threatened species’ was considered the highest priority, followed by ‘Conservation of birds and biodiversity in general’, ‘Monitoring’, ‘Management’ and ‘Working with communities’. ‘Animal welfare/rights’ was regarded as comparatively less important. Eight of 11 conservation strategies were regarded as of high importance, these included habitat protection and rehabilitation, threat abatement, research, advocacy and education. This study documents the view of the ornithological community with respect to priority issues facing birds and could potentially feed into government and other policies aimed at conserving and understanding Australia’s birds.

A rough set approach for selecting clustering attribute

A few of clustering techniques for categorical data exist to group objects having similar characteristics. Some are able to handle uncertainty in the clustering process while others have stability issues. However, the performance of these techniques is an issue due to low accuracy and high computational complexity. This paper proposes a new technique called maximum dependency attributes (MDA) for selecting clustering attribute. The proposed approach is based on rough set theory by taking into account the dependency of attributes of the database. We analyze and compare the performance of MDA technique with the bi-clustering, total roughness (TR) and min–min roughness (MMR) techniques based on four test cases. The results establish the better performance of the proposed approach.

Orientation-specific attentional set effects

The thesis is a theoretical and experimental exploration of visual attention: when and how does knowing a feature of something we are about to see help us to see that thing? Attentional benefits occurred under short inspection times with high distraction intensity, and long inspection times with low distraction intensity.

The efficiency of improved ranked set sampling methods

Ranked set sampling is a desirable sampling technique where the ranking of the sample observations does not require the actual measurement. This thesis develops new ranked set sampling techniques and determines the sampling characteristics required to best estimate the required population parameters, such as mean and variance.

Contracting-agency analysis of the association between firm risk, investment opportunity set, corporate governance and firm performance

Tests the efficiency of corporate controls (board monitoring and incentive contracts) for growth or risk firms. By exploring the implications of controls and studying their interactive effects on firm performance, this study demonstrates how and why different firms use corporate governance controls to align managers' and shareholders' interests.

Vision-based target tracking and surveillance with robust set-valued state estimation

Tracking a target from a video stream (or a sequence of image frames) involves nonlinear measurements in Cartesian coordinates. However, the target dynamics, modeled in Cartesian coordinates, result in a linear system. We present a robust linear filter based on an analytical nonlinear to linear measurement conversion algorithm. Using ideas from robust control theory, a rigorous theoretical analysis is given which guarantees that the state estimation error for the filter is bounded, i.e., a measure against filter divergence is obtained. In fact, an ellipsoidal set-valued estimate is obtained which is guaranteed to contain the true target location with an arbitrarily high probability. The algorithm is particularly suited to visual surveillance and tracking applications involving targets moving on a plane.

A robust set-valued state estimation approach to the problem of vision based SLAM for mobile robots

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using ideas and algorithms from robust control and estimation theory. Using a stereo-vision based sensor, a nonlinear measurement model is derived which leads to nonlinear measurements of the landmark coordinates along with optical flow based measurements of the relative robot-landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear filter is guaranteed stable and the ALAM state estimation error is bounded within an ellipsoidal set. No similar results are available for the commonly employed extended Kalman filter which is known to exhibit divergent and inconsistency characteristics in practice.

Candidate working set strategy based SMO algorithm in support vector machine

Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of this algorithm is the selection of the working set, which greatly affects the training speed. The feasible direction strategy for the working set selection can decrease the objective function, however, may augment to the total calculation for selecting the working set in each of the iteration. In this paper, a new candidate working set (CWS) Strategy is presented considering the cost on the working set selection and cache performance. This new strategy can select several greatest violating samples from Cache as the iterative working sets for the next several optimizing steps, which can improve the efficiency of the kernel cache usage and reduce the computational cost related to the working set selection. The results of the theory analysis and experiments demonstrate that the proposed method can reduce the training time, especially on the large-scale datasets.

Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model

When using linguistic approaches to solve decision problems, we need the techniques for computing with words (CW). Together with the 2-tuple fuzzy linguistic representation models (i.e., the Herrera and Mart´ınez model and the Wang and Hao model), some computational techniques for CW are also developed. In this paper, we define the concept of numerical scale and extend the 2-tuple fuzzy linguistic representation models under the numerical scale.We find that the key of computational techniques
based on linguistic 2-tuples is to set suitable numerical scale with
the purpose of making transformations between linguistic 2-tuples
and numerical values. By defining the concept of the transitive
calibration matrix and its consistent index, this paper develops an optimization model to compute the numerical scale of the linguistic term set. The desired properties of the optimization model are also presented. Furthermore, we discuss how to construct the transitive calibration matrix for decision problems using linguistic preference relations and analyze the linkage between the consistent index of the transitive calibration matrix and one of the linguistic preference relations. The results in this paper are pretty helpful to complete the fuzzy 2-tuple representation models for CW.

The resident assessment instrument-minimum data set 2.0 quality indicators : a systematic review

Background
The Resident Assessment Instrument-Minimum Data Set (RAI-MDS) 2.0 is designed to collect the minimum amount of data to guide care planning and monitoring for residents in long-term care settings. These data have been used to compute indicators of care quality. Use of the quality indicators to inform quality improvement initiatives is contingent upon the validity and reliability of the indicators. The purpose of this review was to systematically examine published and grey research reports in order to assess the state of the science regarding the validity and reliability of the RAI-MDS 2.0 Quality Indicators (QIs).

Methods
We systematically reviewed the evidence for the validity and reliability of the RAI-MDS 2.0 QIs. A comprehensive literature search identified relevant original research published, in English, prior to December 2008. Fourteen articles and one report examining the validity and/or reliability of the RAI-MDS 2.0 QIs were included.

Results
The studies fell into two broad categories, those that examined individual quality indicators and those that examined multiple indicators. All studies were conducted in the United States and included from one to a total of 209 facilities. The number of residents included in the studies ranged from 109 to 5758. One study conducted under research conditions examined 38 chronic care QIs, of which strong evidence for the validity of 12 of the QIs was found. In response to these findings, the 12 QIs were recommended for public reporting purposes. However, a number of observational studies (n=13), conducted in "real world" conditions, have tested the validity and/or reliability of individual QIs, with mixed results. Ten QIs have been studied in this manner, including falls, depression, depression without treatment, urinary incontinence, urinary tract infections, weight loss, bedfast, restraint, pressure ulcer, and pain. These studies have revealed the potential for systematic bias in reporting, with under-reporting of some indicators and over-reporting of others.

Conclusion
Evidence for the reliability and validity of the RAI-MDS QIs remains inconclusive. The QIs provide a useful tool for quality monitoring and to inform quality improvement programs and initiatives. However, caution should be exercised when interpreting the QI results and other sources of evidence of the quality of care processes should be considered in conjunction with QI results.

Retrodicting patch use by foraging swans in a heterogeneous environment using a set of functional responses

Effective conservation of important bird areas requires insight in the number of birds an area can support, and how this carrying capacity changes with habitat modifications. When food depletion is the dominant mechanism of competition, it should in principle be possible to calculate the total time foragers can spend per patch from their functional response (intake rate as a function of food density). However, in the field there are likely to be factors modulating the functional response. In this study previously published results of experiments on captive Bewick's swans were used to obtain functional responses of swans digging for tubers of Fennel pondweed on different foraging substrates: sandy and clayey sediment, and in shallow and deep water. In a field study, four 250×250 m sections belonging to different types (sandy–shallow, clayey–shallow, sandy–deep and clayey–deep) were delineated. Here tubers were sampled with sediment corers in three years, both before and after swan exploitation in autumn, and swans were observed and mapped from a hide in two of these years. Giving-up tuber biomass densities varied among sections. Substitution of these giving-up densities in the derived patch-type-specific functional responses yielded the quitting net energy intake rates in the four sections. As expected from the marginal value theorem, the quitting net energy intake rates did not vary among sections. Moreover, the observed foraging pressure (total foraging time per area) per patch type was in quantitative agreement with the integrated functional responses. These results suggest that in spatially heterogeneous environments, patch exploitation by foragers can be predicted from their functional responses after accounting for foraging substrate.