Interaction in a Paired Speaking Test : the Rater's Perspective

Paired speaking tests are increasingly used in both low-and high-stakes second language assessment contexts. Until recently, very little was known about the way in which raters interpret and apply descriptors relating to interactional competence to a performance that is co-constructed. This book presents a study which explores the interactional features of a paired speaking test that were sailient to raters and the extent to which raters viewed the performance as separable. The study shows that raters use their own frames of reference to interpret descriptors and that they viewed certain features of the performance as mutual accomplishments. The book takes us 'beyond scores', and in doing so, contributes to the growing body of research on paired speaking tests.

Innovation, interaction, and inclusion : heritage luxury brands in collusion with the consumer

Many luxury heritage brands operate on the misconception that heritage is interchangeable with history rather than representative of the emotional response they originally developed in their customer. This idea of heritage as static history inhibits innovation, prevents dynamic renewal and impedes their ability to redefine, strengthen and position their brand in current and emerging marketplaces. This paper examines a number of heritage luxury brands that have successfully identified the original emotional responses they developed in their customers and, through innovative approaches in design, marketing, branding and distribution evoke these responses in contemporary consumers. Using heritage and innovation hand-in-hand, these brands have continued to grow and develop a vision of heritage that incorporates both historical and contemporary ideas to meet emerging customer needs. While what constitutes a ‘luxury’ item is constantly challenged in this era of accessible luxury products, up scaling and aspirational spending, this paper sees consumers’ emotional needs as the key element in defining the concept of luxury. These emotional qualities consistently remain relevant due to their ability to enhance a positive sense of identity for the brand user. Luxury is about the ‘experience’ not just the product providing the consumer with a sense of enhanced status or identity through invoked feelings of exclusivity, authenticity, quality, uniqueness and culture. This paper will analyse luxury heritage brands that have successfully combined these emotional values with those of their ‘heritage’ to create an aura of authenticity and nostalgia that appeals to contemporary consumers. Like luxury, the line where clothing becomes fashion is blurred in the contemporary fashion industry; however, consumer emotion again plays an important role. For example, clothing becomes ‘fashion’ for consumers when it affects their self perception rather than fulfilling basic functions of shelter and protection. Successful luxury heritage brands can enhance consumers’ sense of self by involving them in the ‘experience’ and ‘personality’ of the brand so they see it as a reflection of their own exclusiveness, authentic uniqueness, belonging and cultural value. Innovation is a valuable tool for heritage luxury brands to successfully generate these desired emotional responses and meet the evolving needs of contemporary consumers. While traditionally fashion has been a monologue from brand to consumer, new technology has given consumers a voice to engage brands in a conversation to express their evolving needs, ideas and feedback. As a result, in this consumer-empowered era of information sharing, this paper defines innovation as the ability of heritage luxury brands to develop new design and branding strategies in response to this consumer feedback while retaining the emotional core values of their heritage. This paper analyses how luxury heritage brands can effectively position themselves in the contemporary marketplace by separating heritage from history to incorporate innovative strategies that will appeal to consumer needs of today and tomorrow.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Homodimerization controls the fibroblast growth factor 9 subfamily's receptor binding and heparan sulfate-dependent diffusion in the extracellular matrix

Uncontrolled fibroblast growth factor (FGF) signaling can lead to human diseases, necessitating multiple layers of self-regulatory control mechanisms to keep its activity in check. Herein, we demonstrate that FGF9 and FGF20 ligands undergo a reversible homodimerization, occluding their key receptor binding sites. To test the role of dimerization in ligand autoinhibition, we introduced structure-based mutations into the dimer interfaces of FGF9 and FGF20. The mutations weakened the ability of the ligands to dimerize, effectively increasing the concentrations of monomeric ligands capable of binding and activating their cognate FGF receptor in vitro and in living cells. Interestingly, the monomeric ligands exhibit reduced heparin binding, resulting in their increased radii of heparan sulfate-dependent diffusion and biologic action, as evidenced by the wider dilation area of ex vivo lung cultures in response to implanted mutant FGF9-loaded beads. Hence, our data demonstrate that homodimerization autoregulates FGF9 and FGF20's receptor binding and concentration gradients in the extracellular matrix. Our study is the first to implicate ligand dimerization as an autoregulatory mechanism for growth factor bioactivity and sets the stage for engineering modified FGF9 subfamily ligands, with desired activity for use in both basic and translational research.