The effect of seed morphology on the potential dispersal of aquatic macrophytes by the common carp (Cyprinus carpio)

1. The potential for seed dispersal by fish (ichthyochory) will vary among aquatic plants because of differences in seed size and morphology.

2. To examine how seed morphology influences the probability of dispersal by the common carp (Cyprinus carpio), we studied seed ingestion, retention time and subsequent egestion and germination of seeds of Sparganium emersum and Sagittaria sagittifolia, two aquatic plant species with similar sized but morphologically different seeds.

3. We compared dispersal probabilities between the two plant species, in which the probability of dispersal is assumed to be a function of the probabilities of seed ingestion, egestion and germination, and the dispersal distance is assumed to be a function of seed egestion rate over time.

4. We found that, although the soft seeds of S. sagittifolia had an approximately 1.5 times higher probability of being ingested by the carp than the hard seeds of S. emersum (83.15% ± 1.8% versus 56.16% ± 2.7%, respectively), the latter had an almost twofold higher probability of surviving the passage through the digestive tract (38.58% ± 2.7% versus 20.97% ± 1.5%, respectively). Patterns of seed egestion over time did not differ between the two plant species, despite the difference in seed morphology. Gut passage had a different effect on seed germination between plant species. Compared with non-ingested controls, seeds of S. emersum showed a 12.6% increase in germination and a 2.1 day acceleration in germination rate, whereas seeds of S. sagittifolia displayed a 47.3% decrease and 5.1 day delay, respectively.

5. Our results suggest that seed morphology affects the dispersal probability and postdispersal establishment, but not the dispersal distance, of aquatic plants that are dispersed by fish.

Construction and optimisation of prediction intervals for neural network models

This thesis makes contributions to basic and fundamental research in the field of prediction interval construction using neural network models. It proposes novel methods for objective assessment, rapid construction, and optimisation of neural network-based prediction intervals for uncertainty quantification.

Vocal individuality of in-air Weddell seal (Leptonychotes weddellii) pup "primary" calls

As a result of selective pressures faced during lactation, vocal recognition may play a crucial role in maintaining the phocid mother–pup bond during the period of dependence. To investigate this possibility, we examined whether Weddell seal (Leptonychotes weddellii) pups produce individually distinctive “primary” calls. One temporal, nine fundamental frequency features, and two spectral characteristics were measured. A discriminant function analysis (DFA) of 15 Vestfold Hills pups correctly classified 52% of calls, while the cross-validation procedure classified 29% of calls to the correct pup. A second DFA of 10 known-age McMurdo Sound pups correctly classified 44% of “test” calls. For novel calls, the probabilities of attaining such classification rates by chance are low. The relationship between age and call stereotypy indicated that pups 2 wk and older may be more vocally distinctive. Overall, findings suggest that Weddell seal pup “primary” calls are moderately distinctive and only exhibit sufficient stereotypy to aid maternal recognition by approximately two weeks of age.

Optimizing the quality of bootstrap-based prediction intervals

The bootstrap method is one of the most widely used methods in literature for construction of confidence and prediction intervals. This paper proposes a new method for improving the quality of bootstrap-based prediction intervals. The core of the proposed method is a prediction interval-based cost function, which is used for training neural networks. A simulated annealing method is applied for minimization of the cost function and neural network parameter adjustment. The developed neural networks are then used for estimation of the target variance. Through experiments and simulations it is shown that the proposed method can be used to construct better quality bootstrap-based prediction intervals. The optimized prediction intervals have narrower widths with a greater coverage probability compared to traditional bootstrap-based prediction intervals.

Performance optimization problem in speculative prefetching

Speculative prefetching has been proposed to improve the response time of network access. Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modeling. We analyze the performance of a prefetcher that has uncertain knowledge about future accesses. Our performance metric is the improvement in access time, for which we derive a formula in terms of resource parameters (time available and time required for prefetehing) and speculative parameters (probabilities for next access). We develop a prefetch algorithm to maximize the improvement in access time. The algorithm is based on finding the best solution to a stretch knapsack problem, using theoretically proven apparatus to reduce the search space. An integration between speculative prefetching and caching is also investigated.

Resource-aware speculative prefetching in wireless networks

Mobile users connected to wireless networks expect performance comparable to those on wired networks for interactive multimedia applications. Satisfying Quality of Service (QoS) requirements for such applications in wireless networks is a challenging problem due to limitations of low bandwidth, high error rate and frequent disconnections of wireless channels. In addition, wireless networks suffer from varying bandwidth. In this paper we investigate object prefetching during times of connectedness and bandwidth availability to enhance user perceived connectedness. This paper presents an access model that is suitable for multimedia access in wireless networks. Access modelling for the purpose of predicting future accesses in the context of speculative prefetching has received much attention in the literature. The model recognizes that a web page, instead of just a single file, is typically a compound of several files. When it comes to making prefetch decisions, most previous studies in speculative prefetching resort to simple heuristics, such as prefetching an item with access probabilities larger than a manually tuned threshold. This paper takes a different approach. Specifically, it models the performance of the prefetcher, taking into account access predictions and resource parameters, and develops a prefetch policy based on a theoretical analysis of the model. Since the analysis considers cache as one of the resource parameters, the resulting policy integrates prefetch and cache replacement decisions. The paper investigates the effect of prefetching on network load. In order to make effective use of available resources and maximize access improvement, it is beneficial to prefetch all items with access probabilities exceeding certain threshold.

Performance modelling of speculative prefetching for compound requests in low bandwidth networks

To improve the accuracy of access prediction, a prefetcher for web browsing should recognize the fact that a web page is a compound. By this term we mean that a user request for a single web page may require the retrieval of several multimedia items. Our prediction algorithm builds an access graph that captures the dynamics of web navigation rather than merely attaching probabilities to hypertext structure. When it comes to making prefetch decisions, most previous studies in speculative prefetching resort to simple heuristics, such as prefetching an item with access probabilities larger than a manually tuned threshold. The paper takes a different approach. Specifically, it models the performance of the prefetcher and develops a prefetch policy based on a theoretical analysis of the model. In the analysis, we derive a formula for the expected improvement in access time when prefetch is performed in anticipation for a compound request. We then develop an algorithm that integrates prefetch and cache replacement decisions so as to maximize this improvement. We present experimental results to demonstrate the effectiveness of compound-based prefetching in low bandwidth networks.

A performance model of speculative prefetching in distributed information systems

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modelling. We use improvement in access time as the performance metric, for which we derive a formula in terms of resource parameters (time available and time required for prefetching) and speculative parameters (probabilities for next access). The performance maximization problem is expressed as a stretch knapsack problem. We develop an algorithm to maximize the improvement in access time by solving the stretch knapsack problem, using theoretically proven apparatus to reduce the search space. Integration between speculative prefetching and caching is also investigated, albeit under the assumption of equal item sizes.

Hierarchical monitoring of people's behaviors in complex environments using multiple cameras

We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.

Effect of speculative prefetching on network load in distributed systems

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper on the other hand investigates the performance of speculative prefetching. When prefetching is performed speculatively, there is bound to be an increase in the network load. Furthermore, the prefetched items must compete for space with existing cache occupants. These two factors-increased load and eviction of potentially useful cache entries-are considered in the analysis. We obtain the following conclusion: to maximise the improvement in access time, prefetch exclusively all items with access probabilities exceeding a certain threshold.

Projected socioeconomic disparities in the prevalence of obesity among Australian adults

Objective: To project prevalence of normal weight, overweight and obesity by educational attainment, assuming a continuation of the observed individual weight change in the 5-year follow-up of the national population survey, the Australian Diabetes, Obesity and Lifestyle study (AusDiab; 2000–2005).

Methods: Age-specific transition probabilities between BMI categories, estimated using logistic regression, were entered into education-level-specific, incidence-based, multi-state life tables. Assuming a continuation of the weight change observed in AusDiab, these life tables estimate the prevalence of normal weight, overweight and obesity for Australian adults with low (secondary), medium (diploma) and high (degree) levels of education between 2005 and 2025.

Results: The prevalence of obesity among individuals with secondary level educational attainment is estimated to increase from 23% in 2000 to 44% in 2025. Among individuals with a degree qualification or higher, it will increase from 14% to 30%. If all current educational inequalities in weight change could be eliminated, the projected difference in the prevalence of obesity by 2025 between the highest and lowest educated categories would only be reduced by half (to a 6 percentage point difference from 14 percentage points).

Conclusion: We predict that almost half of Australian adults with low educational status will be obese by 2025. Current trends in obesity have the potential to drive an increase in the absolute difference in obesity prevalence between educational categories in future years.

Implications: Unless obesity prevention and management strategies focus specifically on narrowing social inequalities in obesity, inequalities in health are likely to widen.

Profiles of language development in pre-school children : a longitudinal latent class analysis of data from the early language in Victoria study

Background Pre-school language impairment is common and greatly reduces educational performance. Population attempts to identify children who would benefit from appropriately timed intervention might be improved by greater knowledge about the typical profiles of language development. Specifically, this could be used to help with the early identification of children who will be impaired on school entry.

Methods This study applied longitudinal latent class analysis to assessments at 8, 12, 24, 36 and 48 months on 1113 children from a population-based study, in order to identify classes exhibiting distinct communicative developmental profiles.

Results Five substantive classes were identified: Typical, i.e. development in the typical range at each age; Precocious (late), i.e. typical development in infancy followed by high probabilities of precocity from 24 months onwards; Impaired (early), i.e. high probabilities of impairment up to 12 months followed by typical language development thereafter; Impaired (late), i.e. typical development in infancy but impairment from 24 months onwards; Precocious (early), i.e. high probabilities of precocity in early life followed by typical language by 48 months. The entropy statistic (0.84) suggested classes were fairly well defined, although there was a non-trivial degree of uncertainty in classification of children. That half of the Impaired (late) class was expected to have typical language at 4 years and 6% of the numerically large Typical class was expected to be impaired at 4 years illustrates this. Characteristics indicative of social advantage were more commonly found in the classes with improving profiles.

Conclusions Developmental profiles show that some pre-schoolers' language is characterized by periods of accelerated development, slow development and catch-up growth. Given the uncertainty in classifying children into these profiles, use of this knowledge for identifying children who will be impaired on school entry is not straightforward. The findings do, however, indicate greater need for language enrichment programmes among disadvantaged children.

Construction of neural network-based prediction intervals using particle swarm optimization

Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with point forecasts and predictions. This paper adopts and develops the lower upper bound estimation (LUBE) method for construction of PIs using neural network (NN) models. This method is fast and simple and does not require calculation of heavy matrices, as required by traditional methods. Besides, it makes no assumption about the data distribution. A new width-based index is proposed to quantitatively check how much PIs are informative. Using this measure and the coverage probability of PIs, a multi-objective optimization problem is formulated to train NN models in the LUBE method. The optimization problem is then transformed into a training problem through definition of a PI-based cost function. Particle swarm optimization (PSO) with the mutation operator is used to minimize the cost function. Experiments with synthetic and real-world case studies indicate that the proposed PSO-based LUBE method can construct higher quality PIs in a simpler and faster manner.

The demand for sentence discounts: some empirical evidence

Sentence discounts are now routinely used by Australian courts to encourage guilty pleas. In this article, the authors examine three populations of not on bail defendants who went to trial and were convicted in New South Wales in 2004 for the offences of aggravated robbery, burglary and murder respectively, with the objective of estimating the percentage reduction in sentence quantum that would have induced them to plead guilty. Since conviction (acquittal) probabilities following a trial are likely to be uniformly distributed between 0 and 1, the expected mean probability of conviction (acquittal) for a defendant pleading not guilty was 0.5. The average reductions in the prison sentence corresponding to this probability were: 21%, 23% and 27% respectively. The maximum (minimum) values were: 39% (1.3%), 40% (1.9%) and 39% (1.5%). This range of values reflects the wide dispersion of actual prison sentences handed down by the courts. The distribution of actual sentence discounts offered by the judges in exchange for a guilty plea is not available, consequently the authors cannot comment on why these defendants chose a trial.

Detecting female precise natal philopatry in green turtles using assignment methods

It is well established that sea turtles return to natal rookeries to mate and lay their eggs, and that individual females are faithful to particular nesting sites within the rookery. Less certain is whether females are precisely returning to their natal beach. Attempts to demonstrate such precise natal philopatry with genetic data have had mixed success. Here we focused on the green turtles of three nesting sites in the Ascension Island rookery, separated by 5–15 km. Our approach differed from previous work in two key areas. First, we used male microsatellite data (five loci) reconstructed from samples collected from their offspring (N = 17) in addition to data for samples taken directly from females (N = 139). Second, we employed assignment methods in addition to the more traditional F-statistics. No significant genetic structure could be demonstrated with FST. However, when average assignment probabilities of females were examined, those for nesting populations in which they were sampled were indeed significantly higher than their probabilities for other populations (Mann–Whitney U-test: P < 0.001). Further evidence was provided by a significant result for the mAIC test (P < 0.001), supporting greater natal philopatry for females compared with males. The results suggest that female natal site fidelity was not sufficient for significant genetic differentiation among the nesting populations within the rookery, but detectable with assignment tests.