A constraint to improve the reliability of stereo matching using the rank transform

The rank transform is a non-parametric technique which has been recently proposed for the stereo matching problem. The motivation behind its application to the matching problem is its invariance to certain types of image distortion and noise, as well as its amenability to real-time implementation. This paper derives an analytic expression for the process of matching using the rank transform, and then goes on to derive one constraint which must be satisfied for a correct match. This has been dubbed the rank order constraint or simply the rank constraint. Experimental work has shown that this constraint is capable of resolving ambiguous matches, thereby improving matching reliability. This constraint was incorporated into a new algorithm for matching using the rank transform. This modified algorithm resulted in an increased proportion of correct matches, for all test imagery used.

Estimating the probability of an invalid match for matching using the rank transform

A fundamental problem faced by stereo matching algorithms is the matching or correspondence problem. A wide range of algorithms have been proposed for the correspondence problem. For all matching algorithms, it would be useful to be able to compute a measure of the probability of correctness, or reliability of a match. This paper focuses in particular on one class for matching algorithms, which are based on the rank transform. The interest in these algorithms for stereo matching stems from their invariance to radiometric distortion, and their amenability to fast hardware implementation. This work differs from previous work in that it derives, from first principles, an expression for the probability of a correct match. This method was based on an enumeration of all possible symbols for matching. The theoretical results for disparity error prediction, obtained using this method, were found to agree well with experimental results. However, disadvantages of the technique developed in this chapter are that it is not easily applicable to real images, and also that it is too computationally expensive for practical window sizes. Nevertheless, the exercise provides an interesting and novel analysis of match reliability.

Rank and file : assessing research quality in Australia

In this paper, the author describes recent developments in the assessment of research activity and publication in Australia. Of particular interest to readers will be the move to rank academic journals. Educational Philosophy and Theory (EPAT) received the highest possible ranking, however, the process is far from complete. Some implications for the field, for this journal and particularly, for the educational foundations are discussed.

Rank minimization across appearance and shape for AAM ensemble fitting

Active Appearance Models (AAMs) employ a paradigm of inverting a synthesis model of how an object can vary in terms of shape and appearance. As a result, the ability of AAMs to register an unseen object image is intrinsically linked to two factors. First, how well the synthesis model can reconstruct the object image. Second, the degrees of freedom in the model. Fewer degrees of freedom yield a higher likelihood of good fitting performance. In this paper we look at how these seemingly contrasting factors can complement one another for the problem of AAM fitting of an ensemble of images stemming from a constrained set (e.g. an ensemble of face images of the same person).

RANKL expression in periodontal disease : where does RANKL come from?

Periodontitis is an inflammatory disease characterized by periodontal pocket formation and alveolar bone resorption. Periodontal bone resorption is induced by osteoclasts and receptor activator of nuclear factor-κB ligand (RANKL) which is an essential and central regulator of osteoclast development and osteoclast function. Therefore, RANKL plays a critical role in periodontal bone resorption. In this review, we have summarized the sources of RANKL in periodontal disease and explored which factors may regulate RANKL expression in this disease.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Multidirectional effects of Sr, Mg and Si-containing bioceramic coatings with high bonding strength on inflammation, osteoclastogenesis and osteogenesis

Ideal coating materials for implants should be able to induce excellent osseointegration, which requires several important parameters, such as good bonding strength, limited inflammatory reaction, balanced osteoclastogenesis and osteogenesis, to gain well-functioning coated implants with long-term life span after implantation. Bioactive elements, like Sr, Mg and Si, have been found to play important roles in regulating the biological responses. It is of great interest to combine bioactive elements for developing bioactive coatings on Ti-6Al-4V orthopedic implants to elicit multidirectional effects on the osseointegration. In this study, Sr, Mg and Si-containing bioactive Sr2MgSi2O7 (SMS) ceramic coatings on Ti-6Al-4V were successfully prepared by plasma-spray coating method. The prepared SMS coatings have significantly higher bonding strength (~37MPa) than conventional pure hydroxyapatite (HA) coatings (mostly in the range of 15-25 MPa). It was also found that the prepared SMS coatings switch the macrophage phenotype into M2 extreme, inhibiting the inflammatory reaction via the inhibition of Wnt5A/Ca2+ and Toll-like receptor (TLR) pathways of macrophages. In addition, the osteoclastic activities were also inhibited by SMS coatings. The expression of osteoclastogenesis related genes (RANKL and MCSF) in bone marrow derived mesenchymal cells (BMSCs) with the involvement of macrophages was decreased, while OPG expression was enhanced on SMS coatings compared to HA coatings, indicating that SMS coatings also downregulated the osteoclastogenesis. However, the osteogenic differentiation of BMSCs with the involvement of macrophages was comparable between SMS and HA coatings. Therefore, the prepared SMS coatings showed multidirectional effects, such as improving bonding strength, reducing inflammatory reaction and downregulating osteoclastic activities, but maintaining a comparable osteogenesis, as compared with HA coatings. The combination of bioactive elements of Sr, Mg and Si into bioceramic coatings can be a promising method to develop bioactive implants with multifunctional properties for orthopaedic application.

Using the quantum probability ranking principle to rank interdependent documents

A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between documents through “quantum interference”. This paper explores whether this new ranking principle leads to improved performance for subtopic retrieval, where novelty and diversity is required. In a thorough empirical investigation, models based on the PRP, as well as other recently proposed ranking strategies for subtopic retrieval (i.e. Maximal Marginal Relevance (MMR) and Portfolio Theory(PT)), are compared against the QPRP. On the given task, it is shown that the QPRP outperforms these other ranking strategies. And unlike MMR and PT, one of the main advantages of the QPRP is that no parameter estimation/tuning is required; making the QPRP both simple and effective. This research demonstrates that the application of quantum theory to problems within information retrieval can lead to significant improvements.

Is intent-aware expected reciprocal rank sufficient to evaluate diversity?

In this paper we define two models of users that require diversity in search results; these models are theoretically grounded in the notion of intrinsic and extrinsic diversity. We then examine Intent-Aware Expected Reciprocal Rank (ERR-IA), one of the official measures used to assess diversity in TREC 2011-12, with respect to the proposed user models. By analyzing ranking preferences as expressed by the user models and those estimated by ERR-IA, we investigate whether ERR-IA assesses document rankings according to the requirements of the diversity retrieval task expressed by the two models. Empirical results demonstrate that ERR-IA neglects query-intents coverage by attributing excessive importance to redundant relevant documents. ERR-IA behavior is contrary to the user models that require measures to first assess diversity through the coverage of intents, and then assess the redundancy of relevant intents. Furthermore, diversity should be considered separately from document relevance and the documents positions in the ranking.

Anti-Inflammatory and antiosteoclastogenic activities of parthenolide on human periodontal ligament cellsIn vitro

Periodontitis is an inflammatory disease that causes osteolysis and tooth loss. It is known that the nuclear factor kappa B (NF-κB) signalling pathway plays a key role in the progression of inflammation and osteoclastogenesis in periodontitis. Parthenolide (PTL), a sesquiterpene lactone extracted from the shoots of Tanacetum parthenium, has been shown to possess anti-inflammatory properties in various diseases. In the study reported herein, we investigated the effects of PTL on the inflammatory and osteoclastogenic response of human periodontal ligament-derived cells (hPDLCs) and revealed the signalling pathways in this process. Our results showed that PTL decreased NF-κB activation, I-κB degradation, and ERK activation in hPDLCs. PTL significantly reduced the expression of inflammatory (IL-1β, IL-6, and TNF-α) and osteoclastogenic (RANKL, OPG, and M-CSF) genes in LPS-stimulated hPDLCs. In addition, PTL attenuated hPDLC-induced osteoclastogenic differentiation of macrophages (RAW264.7 cells), as well as reducing gene expression of osteoclast-related markers in RAW264.7 cells in an hPDLC-macrophage coculture model. Taken together, these results demonstrate the anti-inflammatory and antiosteoclastogenic activities of PTL in hPDLCs in vitro. These data offer fundamental evidence supporting the potential use of PTL in periodontitis treatment.

Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems

Discounted Cumulative Gain (DCG) is a well-known ranking evaluation measure for models built with multiple relevance graded data. By handling tagging data used in recommendation systems as an ordinal relevance set of {negative,null,positive}, we propose to build a DCG based recommendation model. We present an efficient and novel learning-to-rank method by optimizing DCG for a recommendation model using the tagging data interpretation scheme. Evaluating the proposed method on real-world datasets, we demonstrate that the method is scalable and outperforms the benchmarking methods by generating a quality top-N item recommendation list.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Osteoclast formation from circulating precursors in osteoporosis

Objective: An imbalance between bone formation and bone resorption is thought to underlie the pathogenesis of reduced bone mass in osteoporosis. Bone resorption is carried out by osteoclasts, which are formed from marrow-derived cells that circulate in the monocyte fraction. Ihe aim of this study was to determine the role of osteoclast formation in the pathogenesis of bone loss in osteoporosis. Methods: The proportion of circulating osteoclast precursors and their relative sensitivity to the osteoclastogenic effects of M-CSF, 1,25(OH)2D3 and RANKL were assessed in primary osteoporosis patients and normal controls. Results: Although there was no difference in the number of circulating osteoclast precursors in osteoporosis patients and normal controls, osteoclasts formed from osteoporosis patients exhibited substantially increased resorptive activity relative to normal controls. Although no increased sensitivity to the osteoclastogenic effects of 1,25(OH)2D3 or M-CSF was noted, increased bone resorption was found in osteoporosis peripheral blood mononuclear cell (PBMC) cultures to which these factors were added. Conclusion: Our findings suggest that osteoclast functional activity rather than formation is increased in primary involutional osteoporosis and that dexamethasone acts to increase osteoclast formation.

Smoothed rank-based procedure for censored data

A smoothed rank-based procedure is developed for the accelerated failure time model to overcome computational issues. The proposed estimator is based on an EM-type procedure coupled with the induced smoothing. "The proposed iterative approach converges provided the initial value is based on a consistent estimator, and the limiting covariance matrix can be obtained from a sandwich-type formula. The consistency and asymptotic normality of the proposed estimator are also established. Extensive simulations show that the new estimator is not only computationally less demanding but also more reliable than the other existing estimators.

Rank regression for analyzing ordinal qualitative data for treatment comparison

Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with qualitative data naturally. They require neither normality assumption nor data transformation. Apart from robustness against outliers and high efficiency, the rank regression can also incorporate covariate effects in the same way as the ordinary regression. By reanalyzing a data set from a wheat Fusarium crown rot study, we illustrated the use of the rank regression methodology and demonstrated that the rank regression models appear to be more appropriate and sensible for analyzing nonnormal data and data with outliers.