Limit Theorems for Maxima of Heavy-Tailed Terms with Random Dependent Weights

2000 Mathematics Subject Classification: Primary 60F17, 60G52, 60G70 secondary 60E07, 62E20.

Investigation of molecular mechanisms regulating biomineralization of pearl oyster Pinctada maxima

Biomineralization is a process encompassing all mineral containing tissues produced within an organism. The most dynamic example of this process is the formation of the mollusk shell, comprising a variety of crystal phases and microstructures. The organic component incorporated within the shell is said to dictate this remarkable architecture. Subsequently, for the past decade considerable research have been undertaken to identify and characterize the protein components involved in biomineralization. Despite these efforts the general understanding of the process remains ambiguous. This study employs a novel molecular approach to further the elucidation of the shell biomineralization. A microarray platform has been custom generated (PmaxArray 1.0) from the pearl oyster Pinctada maxima. PmaxArray 1.0 consists of 4992 expressed sequence tags (ESTs) originating from the mantle, an organ involved in shell formation. This microarray has been used as the primary tool for three separate investigations in an effort to associate transcriptional gene expression from P. maxima to the process of shell biomineralization. The first investigation analyzes the spatial expression of ESTs throughout the mantle organ. The mantle was dissected into five discrete regions and each analyzed for gene expression with PmaxArray 1.0. Over 2000 ESTs were differentially expressed among the tissue sections, identifying five major expression regions. Three of these regions have been proposed to have shell formation functions belonging to nacre, prismatic calcite and periostracum. The spatial gene expression map was confirmed by in situ hybridization, localizing a subset of ESTs from each expression region to the same mantle area. Comparative sequence analysis of ESTs expressed in the proposed shell formation regions with the BLAST tool, revealed a number of the transcripts were novel while others showed significant sequence similarities to previously characterized shell formation genes. The second investigation correlates temporal EST expression during P. maxima larval ontogeny with transitions in shell mineralization during the same period. A timeline documenting the morphologicat microstructural and mineralogical shell characteristics of P. maxima throughout larval ontogeny has been established. Three different shell types were noted based on the physical characters and termed, prodissoconch I, prodissoconch 11 and dissoconch. PmaxArray 1.0 analyzed ESTs expression of animals throughout the larval development of P. maxima, noting up-regulation of 359 ESTs in association with the shell transitions from prodissoconch 1 to prodissoconch 11 to dissoconch. Comparative sequence analysis of these ESTs indicates a number of the transcripts are novel as well as showing significant sequence similarities between ESTs and known shell matrix associated genes and proteins. These ESTs are discussed in relation to the shell characters associated with their temporal expression. The third investigation uses PmaxArray 1.0 to analyze gene expression in the mantle tissue of P. maxima specimens exposed to sub-lethal concentrations of a shell-deforming toxin, tributyltin (TBT). The shell specific effects of TBT are used in this investigation to interpret differential expression of ESTs with respect to shell formation functions. A lethal and sublethal TBT concentration range was established for P. maxima, noting a concentration of 50 ng L- 1 TBT as sub-lethal over a 21 day period. Mantle tissue from P. maxima animals treated with 50 ng L- 1 TBT was assessed for differential EST expression with untreated control animals. A total of 102 ESTs were identified as differentially expressed in association with TBT exposure, comparative sequence identities included an up-regulation of immunity and detoxification related genes and down-regulation of several shell matrix genes. A number of transcripts encoding novel peptides were additionally identified. The potential actions of these genes are discussed with reference to TBT toxicity and shell biomineralization. This thesis has used a microarray platform to analyze gene expression in spatial, temporal and toxicity investigations, revealing the involvement of numerous gene transcripts in specific shell formation functions. Investigation of thousands of transcripts simultaneously has provided a holistic interpretation of the organic components regulating shell biomineralization.

Focus-score weighted super-resolution for uncooperative iris recognition at a distance and on the move

Uncooperative iris identification systems at a distance and on the move often suffer from poor resolution and poor focus of the captured iris images. The lack of pixel resolution and well-focused images significantly degrades the iris recognition performance. This paper proposes a new approach to incorporate the focus score into a reconstruction-based super-resolution process to generate a high resolution iris image from a low resolution and focus inconsistent video sequence of an eye. A reconstruction-based technique, which can incorporate middle and high frequency components from multiple low resolution frames into one desired super-resolved frame without introducing false high frequency components, is used. A new focus assessment approach is proposed for uncooperative iris at a distance and on the move to improve performance for variations in lighting, size and occlusion. A novel fusion scheme is then proposed to incorporate the proposed focus score into the super-resolution process. The experiments conducted on the The Multiple Biometric Grand Challenge portal database shows that our proposed approach achieves an EER of 2.1%, outperforming the existing state-of-the-art averaging signal-level fusion approach by 19.2% and the robust mean super-resolution approach by 8.7%.

Weighted fairness with MCR guarantee and a general-purpose explicit rate allocation algorithm for ATM ABR service

In this paper, weighted fair rate allocation for ATM available bit rate (ABR) service is discussed with the concern of the minimum cell rate (MCR). Weighted fairness with MCR guarantee has been discussed recently in the literature. In those studies, each ABR virtual connection (VC) is first allocated its MCR, then the remaining available bandwidth is further shared among ABR VCs according to their weights. For the weighted fairness defined in this paper, the bandwidth is first allocated according to each VC's weight; if a VC's weighted share is less than its MCR, it should be allocated its MCR instead of the weighted share. This weighted fairness with MCR guarantee is referred to as extended weighted (EXW) fairness. Certain theoretical issues related to EXW, such as its global solution and bottleneck structure, are first discussed in the paper. A distributed explicit rate allocation algorithm is then proposed to achieve EXW fairness in ATM networks. The algorithm is a general-purpose explicit rate algorithm in the sense that it can realise almost all the fairness principles proposed for ABR so far whilst only minor modifications may be needed.

Connecting users and items with weighted tags for personalized item recommendations

Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.

A hybrid recommender systems based on weighted tags

Social tags in web 2.0 are becoming another important information source to describe the content of items as well as to profile users’ topic preferences. However, as arbitrary words given by users, tags contains a lot of noise such as tag synonym and semantic ambiguity a large number personal tags that only used by one user, which brings challenges to effectively use tags to make item recommendations. To solve these problems, this paper proposes to use a set of related tags along with their weights to represent semantic meaning of each tag for each user individually. A hybrid recommendation generation approaches that based on the weighted tags are proposed. We have conducted experiments using the real world dataset obtained from Amazon.com. The experimental results show that the proposed approaches outperform the other state of the art approaches.

Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.

Spatial analysis of biomineralization associated gene expression from the mantle organ of the pearl oyster Pinctada maxima

Background: Biomineralization is a process encompassing all mineral containing tissues produced within an organism. One of the most dynamic examples of this process is the formation of the mollusk shell, comprising a variety of crystal phases and microstructures. The organic component incorporated within the shell is said to dictate this architecture. However general understanding of how this process is achieved remains ambiguous. The mantle is a conserved organ involved in shell formation throughout molluscs. Specifically the mantle is thought to be responsible for secreting the protein component of the shell. This study employs molecular approaches to determine the spatial expression of genes within the mantle tissue to further the elucidation of the shell biomineralization. Results: A microarray platform was custom generated (PmaxArray 1.0) from the pearl oyster Pinctada maxima. PmaxArray 1.0 consists of 4992 expressed sequence tags (ESTs) originating from mantle tissue. This microarray was used to analyze the spatial expression of ESTs throughout the mantle organ. The mantle was dissected into five discrete regions and analyzed for differential gene expression with PmaxArray 1.0. Over 2000 ESTs were determined to be differentially expressed among the tissue sections, identifying five major expression regions. In situ hybridization validated and further localized the expression for a subset of these ESTs. Comparative sequence similarity analysis of these ESTs revealed a number of the transcripts were novel while others showed significant sequence similarities to previously characterized shell related genes.

Learning multidimensional joint control of a robot using receding horizon locally weighted regression

In this paper we explore the ability of a recent model-based learning technique Receding Horizon Locally Weighted Regression (RH-LWR) useful for learning temporally dependent systems. In particular this paper investigates the application of RH-LWR to learn control of Multiple-input Multiple-output robot systems. RH-LWR is demonstrated through learning joint velocity and position control of a three Degree of Freedom (DoF) rigid body robot.

Temporal tracking of mineralization and transcriptional developments of shell formation during the early life history of pearl oyster Pinctada maxima

Molluscan larval ontogeny is a highly conserved process comprising three principal developmental stages. A characteristic unique to each of these stages is shell design, termed prodissoconch I, prodissoconch II and dissoconch. These shells vary in morphology, mineralogy and microstructure. The discrete temporal transitions in shell biomineralization between these larval stages are utilized in this study to investigate transcriptional involvement in several distinct biomineralization events. Scanning electron microscopy and X-ray diffraction analysis of P. maxima larvae and juveniles collected throughout post-embryonic ontogenesis, document the mineralogy and microstructure of each shelled stage as well as establishing a timeline for transitions in biomineralization. P. maxima larval samples most representative of these biomineralization distinctions and transitions were analyzed for differential gene expression on the microarray platform PmaxArray 1.0. A number of transcripts are reported as differentially expressed in correlation to the mineralization events of P. maxima larval ontogeny. Some of those isolated are known shell matrix genes while others are novel; these are discussed in relation to potential shell formation roles. This interdisciplinary investigation has linked the shell developments of P. maxima larval ontogeny with corresponding gene expression profiles, furthering the elucidation of shell biomineralization.

Weighted LDA techniques for I-vector based speaker verification

This paper introduces the Weighted Linear Discriminant Analysis (WLDA) technique, based upon the weighted pairwise Fisher criterion, for the purposes of improving i-vector speaker verification in the presence of high intersession variability. By taking advantage of the speaker discriminative information that is available in the distances between pairs of speakers clustered in the development i-vector space, the WLDA technique is shown to provide an improvement in speaker verification performance over traditional Linear Discriminant Analysis (LDA) approaches. A similar approach is also taken to extend the recently developed Source Normalised LDA (SNLDA) into Weighted SNLDA (WSNLDA) which, similarly, shows an improvement in speaker verification performance in both matched and mismatched enrolment/verification conditions. Based upon the results presented within this paper using the NIST 2008 Speaker Recognition Evaluation dataset, we believe that both WLDA and WSNLDA are viable as replacement techniques to improve the performance of LDA and SNLDA-based i-vector speaker verification.

PLDA based speaker verification with weighted LDA techniques

This paper investigates the use of the dimensionality-reduction techniques weighted linear discriminant analysis (WLDA), and weighted median fisher discriminant analysis (WMFD), before probabilistic linear discriminant analysis (PLDA) modeling for the purpose of improving speaker verification performance in the presence of high inter-session variability. Recently it was shown that WLDA techniques can provide improvement over traditional linear discriminant analysis (LDA) for channel compensation in i-vector based speaker verification systems. We show in this paper that the speaker discriminative information that is available in the distance between pair of speakers clustered in the development i-vector space can also be exploited in heavy-tailed PLDA modeling by using the weighted discriminant approaches prior to PLDA modeling. Based upon the results presented within this paper using the NIST 2008 Speaker Recognition Evaluation dataset, we believe that WLDA and WMFD projections before PLDA modeling can provide an improved approach when compared to uncompensated PLDA modeling for i-vector based speaker verification systems.

Estimating the effects of environmental exposures using a weighted mean of monitoring stations

The health effects of environmental hazards are often examined using time series of the association between a daily response variable (e.g., death) and a daily level of exposure (e.g., temperature). Exposures are usually the average from a network of stations. This gives each station equal importance, and negates the opportunity for some stations to be better measures of exposure. We used a Bayesian hierarchical model that weighted stations using random variables between zero and one. We compared the weighted estimates to the standard model using data on health outcomes (deaths and hospital admissions) and exposures (air pollution and temperature) in Brisbane, Australia. The improvements in model fit were relatively small, and the estimated health effects of pollution were similar using either the standard or weighted estimates. Spatial weighted exposures would be probably more worthwhile when there is either greater spatial detail in the health outcome, or a greater spatial variation in exposure.