Fourier Lucas-Kanade Algorithm

In this paper we propose a framework for both gradient descent image and object alignment in the Fourier domain. Our method centers upon the classical Lucas & Kanade (LK) algorithm where we represent the source and template/model in the complex 2D Fourier domain rather than in the spatial 2D domain. We refer to our approach as the Fourier LK (FLK) algorithm. The FLK formulation is advantageous when one pre-processes the source image and template/model with a bank of filters (e.g. oriented edges, Gabor, etc.) as: (i) it can handle substantial illumination variations, (ii) the inefficient pre-processing filter bank step can be subsumed within the FLK algorithm as a sparse diagonal weighting matrix, (iii) unlike traditional LK the computational cost is invariant to the number of filters and as a result far more efficient, and (iv) this approach can be extended to the inverse compositional form of the LK algorithm where nearly all steps (including Fourier transform and filter bank pre-processing) can be pre-computed leading to an extremely efficient and robust approach to gradient descent image matching. Further, these computational savings translate to non-rigid object alignment tasks that are considered extensions of the LK algorithm such as those found in Active Appearance Models (AAMs).

A salutogenic approach to healing following child sexual assault

Decades of research has now produced a rich description of the destruction child sexual assault (CSA) can cause in an individual’s life. Post-Traumatic Stress Disorder (PTSD), Dissociative Identity Disorder, Borderline Personality Disorder, depression, anxiety, Panic Disorder, intimacy issues, substance abuse, self-harm, and suicidal ideation and attempts, are some of the negative outcomes that have been attributed to this type of traumatic experience. Psychology's tendency to dwell within a pathological paradigm, along with popular media who espouse a similar rhetoric, would lead to the belief that once exposed to CSA, an individual is forever at the mercy of dealing with a massive array of accompanying negative effects. While the possibility of these outcomes in those who have experienced CSA is not at all denied, it is also timely to consider an alternative paradigm that up until now has received a paucity of attention in the sexual assault literature. That is to say, not only do people have the ability to work through the painful and personal impacts of CSA, but for some people the process of recovery may provide a catalyst for positive life changes that have been termed post-traumatic growth (Tedeschi & Calhoun, 1995). To begin with in this chapter, the negative sequale’ of childhood sexual assault it discussed initially. Inherent to this discussion are questions of measurement and definitions of sexual assault. The chapter highlights ways in which the term CSA has been defined and hence operationalised in research, and the myriad problems, confusions, and inconclusive findings that have plagued the sexual assault literature. Following this is a review of the sparse literature that has conceptualised CSA from a more salutogenic (Antonovsky, 1979) theoretical orientation. It is argued that a salutogenic approach to intervention and to research in this area, provides a more useful way of promoting healing and the gaining of wisdom, but importantly does not negate the very real distress that may accompany growth. This chapter will then present a case study to elucidate the theoretical and empirical literature discussed using the words of a survivor. Finally, the chapter concludes with implications for therapeutic practice, which includes some practical ways in which to promote adaptation to life within the context of having survived this insidious crime.

Efficient solution of two-sided nonlinear space-fractional diffusion equations using fast Poisson preconditioners

We develop a fast Poisson preconditioner for the efficient numerical solution of a class of two-sided nonlinear space fractional diffusion equations in one and two dimensions using the method of lines. Using the shifted Gr¨unwald finite difference formulas to approximate the two-sided(i.e. the left and right Riemann-Liouville) fractional derivatives, the resulting semi-discrete nonlinear systems have dense Jacobian matrices owing to the non-local property of fractional derivatives. We employ a modern initial value problem solver utilising backward differentiation formulas and Jacobian-free Newton-Krylov methods to solve these systems. For efficient performance of the Jacobianfree Newton-Krylov method it is essential to apply an effective preconditioner to accelerate the convergence of the linear iterative solver. The key contribution of our work is to generalise the fast Poisson preconditioner, widely used for integer-order diffusion equations, so that it applies to the two-sided space fractional diffusion equation. A number of numerical experiments are presented to demonstrate the effectiveness of the preconditioner and the overall solution strategy.

A banded preconditioner for the two-sided, nonlinear space-fractional diffusion equation

The method of lines is a standard method for advancing the solution of partial differential equations (PDEs) in time. In one sense, the method applies equally well to space-fractional PDEs as it does to integer-order PDEs. However, there is a significant challenge when solving space-fractional PDEs in this way, owing to the non-local nature of the fractional derivatives. Each equation in the resulting semi-discrete system involves contributions from every spatial node in the domain. This has important consequences for the efficiency of the numerical solver, especially when the system is large. First, the Jacobian matrix of the system is dense, and hence methods that avoid the need to form and factorise this matrix are preferred. Second, since the cost of evaluating the discrete equations is high, it is essential to minimise the number of evaluations required to advance the solution in time. In this paper, we show how an effective preconditioner is essential for improving the efficiency of the method of lines for solving a quite general two-sided, nonlinear space-fractional diffusion equation. A key contribution is to show, how to construct suitable banded approximations to the system Jacobian for preconditioning purposes that permit high orders and large stepsizes to be used in the temporal integration, without requiring dense matrices to be formed. The results of numerical experiments are presented that demonstrate the effectiveness of this approach.

High resolution transmission electron microscopy of the Murchison Carbonaceous Chondrite and the Kenna Ureilite

Fine-grained matrices in carbonaceous chondrites and small, micron-sized inclusions in achondrites can be characterized effectively using high resolution transmission electron micro­scopy (HRTEM).

Preparation of porous nanofibers from electrospun polyacrylonitrile/calcium carbonate composite nanofibers using porogen leaching technique

Production of nanofibrous polyacrylonitrile/calcium carbonate (PAN/CaCO3) nanocomposite web was carried out through solution electrospinning process. Pore generating nanoparticles were leached from the PAN matrices in hydrochloric acid bath with the purpose of producing an ultimate nanoporous structure. The possible interaction between CaCO3 nanoparticles and PAN functional groups was investigated. Atomic absorption method was used to measure the amount of extracted CaCO3 nanoparticles. Morphological observation showed nanofibers of 270–720 nm in diameter containing nanopores of 50–130 nm. Monitoring the governing parameters statistically, it was found that the amount of extraction (ε) of CaCO3was increased when the web surface area (a) was broadened according to a simple scaling law (ε = 3.18 a0.4). The leaching process was maximized in the presence of 5% v/v of acid in the extraction bath and 5 wt % of CaCO3 in the polymer solution. Collateral effects of the extraction time and temperature showed exponential growth within a favorable extremum at 50°C for 72 h. Concentration of dimethylformamide as the solvent had no significant impact on the extraction level.

Preparing nanocomposite fibrous scaffolds of P3HB/nHA for bone tissue engineering

Nanocomposites are recently known to be among the most successful materials in biomedical applications. In this work we sought to fabricate fibrous scaffolds which can mimic the extra cellular matrix of cartilaginous connective tissue not only to a structural extent but with a mechanical and biological analogy. Poly(3-hydroxybutyrate) (P3HB) matrices were reinforced with 5, 10 and 15 %wt hydroxyapatite (HA) nanoparticles and electrospun into nanocomposite fibrous scaffolds. Mechanical properties of each case were compared with that of a P3HB scaffold produced in the same processing condition. Spectroscopic and morphological observations were used for detecting the interaction quality between the constituents. Nanoparticles rested deep within the fibers of 1 μm in diameter. Chemical interactions of hydrogen bonds linked the constituents through the interface. Maximum elastic modulus and mechanical strength was obtained with the presence of 5%wt hydroxyapatite nanoparticles. Above 10%wt, nanoparticles tended to agglomerate and caused the entity to lose its mechanical performance; however, viscoelasticity interfered at this concentration and lead to a delayed failure. In other words, higher elongation at break and a massive work of rupture was observed at 10%wt.

Managing memory and reducing I/O cost for correlation matrix calculation in bioinformatics

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

Health and community services for trafficked women : an exploratory study of policy and practice

The trafficking of women has attracted considerable international and national policy attention, particularly since the UN Protocol to Prevent, Suppress and Punish Trafficking in Persons, Especially Women and Children (2000), of which the Australian Government has been a signatory since 2005. The provision of health and community services for trafficked women is a central feature of this Protocol, but in Australia service provision is made difficult by how trafficked women are understood and treated in policy and legal terms. This study aimed to explore the provision of health and community services for trafficked women in the Greater Sydney region through a series of interviews with government and non-government organisations. The findings reveal that services have been inaccessible as a result of sparse, uncoordinated, and poorly funded provision. The major obstacle to adequate and appropriate service provision has been a national policy approach focusing on 'border protection' and criminalisation rather than on trafficked women and their human rights. We conclude that further policy development needs to focus on the practical implications of how such rights can be translated into the delivery of health and community services that trafficked women can access and be supported by more effectively.

Fabrication and characterisation of graphene oxide-epoxy nanocomposite

Adequate amount of graphene oxide (GO) was firstly prepared by oxidation of graphite and GO/epoxy nanocomposites were subsequently prepared by typical solution mixing technique. X-ray diffraction (XRD) pattern, X-ray photoelectron (XPS), Raman and Fourier transform infrared (FTIR) spectroscopy indicated the successful preparation of GO. Scanning electron microscopy (SEM) and Transmission electron microscopy (TEM) images of the graphite oxide showed that they consist of a large amount of graphene oxide platelets with a curled morphology containing of a thin wrinkled sheet like structure. AFM image of the exfoliated GO signified that the average thickness of GO sheets is ~1.0 nm which is very similar to GO monolayer. Mechanical properties of as prepared GO/epoxy nanocomposites were investigated. Significant improvements in both Young’s modulus and tensile strength were observed for the nanocomposites at very low level of GO loading. The Young’s modulus of the nanocomposites containing 0.5 wt% GO was 1.72 GPa, which was 35 % higher than that of the pure epoxy resin (1.28 GPa). The effective reinforcement of the GO based epoxy nanocomposites can be attributed to the good dispersion and the strong interfacial interactions between the GO sheets and the epoxy resin matrices.

Chitosan-collagen scaffolds with nano/microfibrous architecture for skin tissue engineering

In this study, a hierarchical nano/microfibrous chitosan/collagen scaffold that approximates structural and functional attributes of native extracellular matrix (ECM), has been developed for applicability in skin tissue engineering. Scaffolds were produced by electrospinning of chitosan followed by imbibing of collagen solution, freeze-drying and subsequent cross-linking of two polymers. Scanning electron microscopy showed formation of layered scaffolds with nano/microfibrous architechture. Physico-chemical properties of scaffolds including tensile strength, swelling behavior and biodegradability were found satisfactory for intended application. 3T3 fibroblasts and HaCaT keratinocytes showed good in vitro cellular response on scaffolds thereby indicating the matrices′ cytocompatible nature. Scaffolds tested in an ex vivo human skin equivalent (HSE) wound model, as a preliminary alternative to animal testing, showed keratinocyte migration and wound re-epithelization — a pre-requisite for healing and regeneration. Taken together, the herein proposed chitosan/collagen scaffold, shows good potential for skin tissue engineering.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

An evaluation framework for cross-lingual link discovery

Cross-Lingual Link Discovery (CLLD) is a new problem in Information Retrieval. The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages. This is particularly helpful in knowledge discovery if a multi-lingual knowledge base is sparse in one language or another, or the topical coverage in each language is different; such is the case with Wikipedia. Techniques for identifying new and topically relevant cross-lingual links are a current topic of interest at NTCIR where the CrossLink task has been running since the 2011 NTCIR-9. This paper presents the evaluation framework for benchmarking algorithms for cross-lingual link discovery evaluated in the context of NTCIR-9. This framework includes topics, document collections, assessments, metrics, and a toolkit for pooling, assessment, and evaluation. The assessments are further divided into two separate sets: manual assessments performed by human assessors; and automatic assessments based on links extracted from Wikipedia itself. Using this framework we show that manual assessment is more robust than automatic assessment in the context of cross-lingual link discovery.

Social support promotes psychological well-being following a natural disaster

Receiving emotional support has consistently been demonstrated as an important factor associated with mental health but sparse research has investigated giving support in addition to receiving it or the types of support that predict well-being. In this paper the relationship between giving and receiving instrumental and emotional social support and psychological well-being during and following a natural disaster is investigated. A survey administered between four and six months after fatal floods was conducted with 200 community members consisting of men (n = 68) and women (n = 132) aged between 17 and 87 years. Social support experiences were assessed using the 2-Way Social Support Scale (2-Way SSS; Shakespeare-Finch & Obst, 2011) and eudemonic well-being was measured using the Psychological Well-Being Scale (PWBS; Ryff & Keyes, 1995). Hierarchical multiple regression analyses were used to examine expected relationships and to explore the differential effects of the four factors of the 2-Way SSS. Results indicated that social support shared significant positive associations with domains of psychological well-being, especially with regards to interpersonal relationships. Receiving and giving emotional support were respectively the strongest unique predictors of psychological well-being. However, receiving instrumental support predicted less autonomy. Results highlight the importance of measuring social support as a multidimensional construct and affirm that disaster response policy and practice should focus on emotional as well as instrumental needs in order to promote individual and community psychosocial health following a flooding crisis.

An ontology-based method for sparsity problem in tag recommendation

Tags or personal metadata for annotating web resources have been widely adopted in Web 2.0 sites. However, as tags are freely chosen by users, the vocabularies are diverse, ambiguous and sometimes only meaningful to individuals. Tag recommenders may assist users during tagging process. Its objective is to suggest relevant tags to use as well as to help consolidating vocabulary in the systems. In this paper we discuss our approach for providing personalized tag recommendation by making use of existing domain ontology generated from folksonomy. Specifically we evaluated the approach in sparse situation. The evaluation shows that the proposed ontology-based method has improved the accuracy of tag recommendation in this situation.