Deakin University Library website 2.0 : iterative change for maximum gain

The purpose of library websites is evolving. Deakin University Library originally undertook to redevelop its website to provide improved access to information, resources and services and to better meet clients in their space. The first phase redeveloped the library homepage and top level link pages. During this time, social networking applications were becoming part of higher education. There were new choices: the Library website and search tools could undergo significant metamorphosis; adopt Web2.0 functionality and move from being the public face of the online library to the public space of its online community, with students and staff as active partners in its development.

The relationship among malingering, psychopathy, and the MMPI-2 validity scales in maximum security forensic psychiatric inpatients

The records of 392 men hospitalized in a maximum security forensic psychiatric hospital were reviewed. Demographic information was collected as well as data from the men's performance on the Psychopathy Checklist-Revised (PCL-R) and Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Prevalence rates for malingering were low across the sample. However, results of chi-square analysis revealed that those who scored high on the PCL-R received a diagnosis of malingering significantly more frequently than those who scored low on the PCL-R. Clinical applications and theoretical implications of the results are discussed.

Maximum likelihood estimation for rician distributed data in analytical q-ball imaging

Analytical q-ball imaging is widely used for reconstruction of orientation distribution function (ODF) using diffusion weighted MRI data. Estimating the spherical harmonic coefficients is a critical step in this method. Least squares (LS) is widely used for this purpose assuming the noise to be additive Gaussian. However, Rician noise is considered as a more appropriate model to describe noise in MR signal. Therefore, the current estimation techniques are valid only for high SNRs with Gaussian distribution approximating the Rician distribution. The aim of this study is to present an estimation approach considering the actual distribution of the data to provide reliable results particularly for the case of low SNR values. Maximum likelihood (ML) is investigated as a more effective estimation method. However, no closed form estimator is presented as the estimator becomes nonlinear for the noise assumption of the Rician distribution. Consequently, the results of LS estimator is used as an initial guess and the more refined answer is achieved using iterative numerical methods. According to the results, the ODFs reconstructed from low SNR data are in close agreement with ODFs reconstructed from high SNRs when Rician distribution is considered. Also, the error between the estimated and actual fiber orientations was compared using ML and LS estimator. In low SNRs, ML estimator achieves less error compared to the LS estimator.

The exact distribution of the maximum, minimum and the range of multinomial/dirichlet and multivariate hypergeometric frequencies

The exact distribution of the maximum and minimum frequencies of Multinomial/Dirichlet and Multivariate Hypergeometric distributions of n balls in m urns is compactly represented as a product of stochastic matrices. This representation does not require equal urn probabilities, is invariant to urn order, and permits rapid calculation of exact probabilities. The exact distribution of the range is also obtained. These algorithms satisfy a long-standing need for routines to compute exact Multinomial/Dirichlet and Multivariate Hypergeometric maximum, minimum, and range probabilities in statistical computation libraries and software packages.

Comparison of different maximum safe levels in fortified foods and supplements using a probabilistic risk assessment approach

Different European institutions have developed mathematical models to propose maximum safe levels either for fortified foods or for dietary supplements. The objective of the present study was to compare and check the safety of these different maximum safe levels (MSL) by using a probabilistic risk assessment approach. The potential maximum nutritional intakes were estimated by taking into account all sources of intakes (base diet, fortified foods and dietary supplements) and compared with the tolerable upper intake levels for vitamins and minerals. This approach simulated the consequences of both food fortification and supplementation in terms of food safety. Different scenarios were tested. They are the result of the combination of several MSL obtained using the previous models. The study was based on the second French Individual and National Study on Food Consumption performed in 2006–7, matched with the French food nutritional composition database. The analyses were based on a sample of 1918 adults aged 18–79 years. Some MSL in fortified foods and dietary supplements obtained independently were protective enough, although some others could lead to nutritional intakes above the tolerable upper intake levels. The simulation showed that it is crucial to consider the inter-individual variability of fortified food intakes when setting MSL for foods and supplements. The risk assessment approach developed here by integrating the MSL for fortified foods and dietary supplements is useful for ensuring consumer protection. It may be subsequently used to test any other MSL for vitamins and minerals proposed in the future.

Maximum likelihood approach for tracking multiple mobile agents with a moving doppler radar system

Tracking mobile agents with a Doppler radar system mounted on a moving vehicle is considered in this paper. Dopplers modulated from mobile agents on the single frequency continuous wave signals are analyzed in order to estimate the positions and velocities of multiple mobile agents. The measurement noise is assumed to be Gaussian and the maximum likelihood estimation is utilized to enhance the localization accuracy.

Maximum-likelihood based estimation for Q-ball imaging using diffusion-weighted MRI data

Q-ball imaging has been presented to reconstruct diffusion orientation distribution function using diffusion weighted MRI. In this thesiis, we present a novel and robust approach to satisfy the smoothness constraint required in Q-ball imaging. Moreover, we developed an improved estimator based on the actual distribution of the MR data.

On the maximum throughput of two-hop wireless network coding

Network coding has shown the promise of significant throughput improvement. In this paper, we study the throughput of two-hop wireless network coding and explore how the maximum throughput can be achieved under a random medium access scheme. Unlike previous studies, we consider a more practical network where the structure of overhearing status between the intended receivers and the transmitters is arbitrary. We make a formal analysis on the network throughput using network coding upon the concept of network coding cliques (NCCs). The analysis shows that the maximum normalized throughput, subject to fairness requirement, is n/n+m, where n is the number of transmitters and m is the number of NCCs in a 2-hop wireless network. We have also found that this maximum throughput can be achieved under a random medium access scheme when the medium access priority of the relay node is equal to the number of NCCs in the network. Our theoretical findings have been validated by simulation as well.

Maximum contrast analysis for nonnegative blind source separation

In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.

Free-ranging eastern chipmunks (Tamias striatus) infected with bot fly (Cuterebra emasculator) larvae have higher resting but lower maximum metabolism

Given the ubiquity and evolutionary importance of parasites, their effect on the energy budget of mammals remains surprisingly unclear. The eastern chipmunk (Tamias striatus (L., 1758)) is a burrowing rodent that is commonly infected by cuterebrid bot fly (Cuterebra emasculator Fitch, 1856) larvae. We measured resting metabolic rate (RMR) and cold-induced [Vo.sub.2]-max (under heliox atmosphere) in 20 free-ranging individuals, of which 4 individuals were infected by one or two larva. We found that RMR was significantly higher in chipmunks infected by bot fly larvae (mean [+ or -] SE = 0.88 [+ or -] 0.05 W) than in uninfected individuals (0.74 [+ or -] 0.02 W). In contrast, V[O.sub.2]-max was significantly lower in chipmunks infected by bot fly larvae (4.96 [+ or -] 0.70 W) than in uninfected individuals (6.37 [+ or -] 0.16 W). Consequently, the aerobic scope (ratio of [Vo.sub.2]-max to RMR) was negatively correlated with the number of bot fly larvae (infected individuals = 5.74 [+ or -] 1.03 W; noninfected individuals = 8.67 [+ or -] 0.26 W). Finally, after accounting for the effects of body mass and bot fly parasitism on RMR and [Vo.sub.2]-max, there was no correlation between the two variables among individuals within our population. In addition to providing the first estimate of [Vo.sub.2]-max in T. striatus, these results offer additional evidence that bot fly parasitism has significant impacts on the metabolic ecology of this host species.

AdaM : adaptive-maximum imputation for neighborhood-based collaborative filtering

In the context of collaborative filtering, the well known data sparsity issue makes two like-minded users have little similarity, and consequently renders the k nearest neighbour rule inapplicable. In this paper, we address the data sparsity problem in the neighbourhood-based CF methods by proposing an Adaptive-Maximum imputation method (AdaM). The basic idea is to identify an imputation area that can maximize the imputation benefit for recommendation purposes, while minimizing the imputation error brought in. To achieve the maximum imputation benefit, the imputation area is determined from both the user and the item perspectives; to minimize the imputation error, there is at least one real rating preserved for each item in the identified imputation area. A theoretical analysis is provided to prove that the proposed imputation method outperforms the conventional neighbourhood-based CF methods through more accurate neighbour identification. Experiment results on benchmark datasets show that the proposed method significantly outperforms the other related state-of-the-art imputation-based methods in terms of accuracy.