Validation of the Italian version of the Yale-Brown Obsessive Compulsive Scale-Second Edition (Y-BOCS-II) in a clinical sample

OBJECTIVE: The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) is the most widely accepted measure of obsessive-compulsive disorder (OCD) symptom severity. Recently, the scale has been revised into a second edition (Y-BOCS-II) in order to improve its measurement properties. The present study aimed to evaluate the psychometric properties of the Italian version of the Y-BOCS-II Severity Scale (SS) in a large clinical sample. METHOD: The original version of the Y-BOCS-II was translated into Italian, which involved forward and back-translation procedures. The Italian Y-BOCS-II-SS was administered to one hundred twenty-five treatment-seeking adults with OCD, together with the original Y-BOCS-SS and a battery of self-report measures assessing OCD symptom severity and depressive and anxious symptomology. The factor structure, internal consistency, temporal stability, and construct validity were investigated on the whole sample, while inter-rater and test-retest reliability were assessed on a subsample of participants. RESULTS: Factor analyses revealed a two-factor structure different from those of the original scale, comprising (1) symptom severity; and (2) interference from symptoms. Internal consistency, test-retest reliability over a 2-week period and inter-rater reliability were satisfactory. The Y-BOCS-II-SS also showed excellent construct validity (and better than the Y-BOCS-SS), with good convergent and discriminant validity when assessed against other OCD symptom measures and measures of depression, anxiety and worry. CONCLUSIONS: These findings suggest that the Italian version of the Y-BOCS-II-SS retains the adequate psychometric properties of the original and that it can be confidently used as an assessment tool of OCD symptoms in both clinical and research settings.

Differential convergence of life-cycle inventories toward upstream production layers

We present an input-output analysis of the life-cycle labor, land, and greenhouse gas (GHG) requirements of alternative options for three case studies: investing money in a new vehicle versus in repairs of an existing vehicle (labor), passenger transport modes for a trip between Sydney and Melbourne (land use), and renewable electricity generation (GHG emissions). These case studies were chosen to demonstrate the possibility of rank crossovers in life-cycle inventory (LCI) results as system boundaries are expanded and upstream production inputs are taken into account. They demonstrate that differential convergence can cause crossovers in the ranking of inventories for alternative functional units occurring at second-and higher-order upstream production layers.

More towards the centre : searching for field position for student equity in Australian higher education

The field of Australian higher education has changed, is changing and is about to change, as it is repositioned in relation to other ‘fields of power’. It is a sector now well defined by its institutional groupings – the Go8, the IRUs, the ATN and the rest – and by their relative claims to selectivity and exclusivity, with every suggestion of their differentiation growing. Even within these groupings there are distinctions and variations. Moreover, Australian universities now compete within an international higher education marketplace, ranked by THES and Shanghai Jiao Tiong league tables. ‘Catchment areas’ and knowledge production have become global. And the potential of a ‘joined‐up’ tertiary education system, of VET and universities, will rework relations within Australian higher education, as will lifting the volume caps on university student numbers. In sum, Australian universities (and agents within them) are positioned differently in the field, although not in the stable relations imagined by Pierre Bourdieu in the France of the 1960s. And being so variously and variably placed, institutions and agents have different stances available to them, including the positions they can take on student equity. In this paper I begin from the premise that our current shared stance on this has been out‐positioned. Nation‐bound proportional representation loses its equity meaning when the Australian elite send their children to Harvard, Yale, Oxford and Cambridge. The same could also be said, and has been, about equity representations by region, institution, discipline and degree. What then, also, for a new national research centre with a focus on student equity and higher education, for its research agenda and positioning in the field? What stance can it take on student equity that will resonate on a national and even international scale? And, given a global field of higher education, what definitions of equity and propositions for policy and practice can it offer? What will work in the pursuit of equity?

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

Ridge regression for two dimensional locality preserving projection

Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.

Application of the DCT energy histogram for face recognition

In this paper, we investigate the face recognition problem via energy histogram of the DCT coefficients. Several issues related to the recognition performance are discussed, In particular the issue of histogram bin sizes and feature sets. In addition, we propose a technique for selecting the classification threshold incrementally. Experimentation was conducted on the Yale face database and results indicated that the threshold obtained via the proposed technique provides a balanced recognition in term of precision and recall. Furthermore, it demonstrated that the energy histogram algorithm outperformed the well-known Eigenface algorithm.

Random Subspace Two-Dimensional PCA for face recognition

The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combines the 2DPCA method with the random subspace (RS) technique. The RS-2DPCA inherits the advantages of both the 2DPCA and RS technique, thus it can avoid the overfitting problem and achieve high recognition accuracy. Experimental results in three benchmark face data sets -the ORL database, the Yale face database and the extended Yale face database B - confirm our hypothesis that the RS-2DPCA is superior to the 2DPCA itself.

Face recognition via the overlapping energy histogram

In this paper we investigate the face recognition problem via the overlapping energy histogram of the DCT coefficients. Particularly, we investigate some important issues relating to the recognition performance, such as the issue of selecting threshold and the number of bins. These selection methods utilise information obtained from the training dataset. Experimentation is conducted on the Yale face database and results indicate that the proposed parameter selection methods perform well in selecting the threshold and number of bins. Furthermore, we show that the proposed overlapping energy histogram approach outperforms the Eigenfaces, 2DPCA and energy histogram significantly.

Unfolding a face : from singular to manifold

Face recognition from a single image remains an important task in many practical applications and a significant research challenge. Some of the challenges are inherent to the problem, for example due to changing lighting conditions. Others, no less significant, are of a practical nature – face recognition algorithms cannot be assumed to operate on perfect data, but rather often on data that has already been subject to pre-processing errors (e.g. localization and registration errors). This paper introduces a novel method for face recognition that is both trained and queried using only a single image per subject. The key concept, motivated by abundant prior work on face appearance manifolds, is that of face part manifolds – it is shown that the appearance seen through a sliding window overlaid over an image of a face, traces a trajectory over a 2D manifold embedded in the image space. We present a theoretical argument for the use of this representation and demonstrate how it can be effectively exploited in the single image based recognition. It is shown that while inheriting the advantages of local feature methods, it also implicitly captures the geometric relationship between discriminative facial features and is naturally robust to face localization errors. Our theoretical arguments are verified in an experimental evaluation on the Yale Face Database.

Who was Sarah Bernhardt? Negotiating fact and fiction

Review essay of : Robert Gottlieb's Sarah: The Life of Sarah Bernhardt. New Haven and London: Yale University Press, 2010. pp. 233.