Different rooms, different voices: double-breasting, multi-channel representation and the managerial agenda

Double-breasting has been identified as where companies run union voice and non-union voice mechanisms across different plants. While research has focused on the incidence of such arrangements, there is a dearth of evidence into the dynamics of it. This article seeks to complement existing research by examining the contours of double-breasting in a case study organisation. The findings suggest that more research is necessary into the dynamics of double-breasting in terms of how voice in sites affects each other and the extent to which running different regimes affects the managerial agenda.

Non-union employee representation, union avoidance and the managerial agenda

Non-union employee representation is an area which has attracted much interest in the voice literature. Much of the literature has been shaped by a dialogue which considers NERs as a means of union avoidance. More recently however scholars have suggested that for NERs to work in such contexts, they may need to be imbued with a higher set of functionalities to remain viable entities. Using a critical case study of a union recognition drive and managerial response in the form of an NER, this article contributes to a more nuanced interpretation of the literature dialogue than hitherto exists. A core component of the findings directly challenge existing interpretations within the field; namely that NERs are shaped by a paradox of managerial action. It is argued that the NER failed to satisfy for employees because of a structural remit, rather than through any paradox in managerial intent.

Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via ℓ1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.

Regulating for mutual gains? Non-union employee representation and the Information and Consultation Directive

Interest in ‘mutual gains’ has principally been confined to studies of the unionised sector. Yet there is no reason why this conceptual dynamic cannot be extended to the non-unionised realm, specifically in relation to non-union employee representation (NER). Although extant research views NER as unfertile terrain for mutual gains, the paper examines whether NER developed in response to the European Directive on Information and Consultation (I&C) of Employees may offer a potentially more fruitful route. The paper examines this possibility by considering three cases of NER established under the I&C Directive in Ireland, assessing the extent to which mutual gains were achieved.

Simplifying the representation of complex free-energy landscapes using sketch-map

A new scheme, sketch-map, for obtaining a low-dimensional representation of the region of phase space explored during an enhanced dynamics simulation is proposed. We show evidence, from an examination of the distribution of pairwise distances between frames, that some features of the free-energy surface are inherently high-dimensional. This makes dimensionality reduction problematic because the data does not satisfy the assumptions made in conventional manifold learning algorithms We therefore propose that when dimensionality reduction is performed on trajectory data one should think of the resultant embedding as a quickly sketched set of directions rather than a road map. In other words, the embedding tells one about the connectivity between states but does not provide the vectors that correspond to the slow degrees of freedom. This realization informs the development of sketch-map, which endeavors to reproduce the proximity information from the high-dimensionality description in a space of lower dimensionality even when a faithful embedding is not possible.

A latent feature analysis of the neural representation of conceptual knowledge

Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. © 2012 Springer-Verlag.

A sparse representation based fast detection method for surface defect detection of bottle caps

A practical machine-vision-based system is developed for fast detection of defects occurring on the surface of bottle caps. This system can be used to extract the circular region as the region of interests (ROI) from the surface of a bottle cap, and then use the circular region projection histogram (CRPH) as the matching features. We establish two dictionaries for the template and possible defect, respectively. Due to the requirements of high-speed production as well as detecting quality, a fast algorithm based on a sparse representation is proposed to speed up the searching. In the sparse representation, non-zero elements in the sparse factors indicate the defect's size and position. Experimental results in industrial trials show that the proposed method outperforms the orientation code method (OCM) and is able to produce promising results for detecting defects on the surface of bottle caps.