The effect of firm-level contracts on the structure of wages: evidence from matched employer-employee data

Revised: 2006-11

Variability in the air-sea interaction patterns and timescales within the south-eastern Bay of Biscay, as observed by HF radar data

Two high-frequency (HF) radar stations were installed on the coast of the south-eastern Bay of Biscay in 2009, providing high spatial and temporal resolution and large spatial coverage of currents in the area for the first time. This has made it possible to quantitatively assess the air-sea interaction patterns and timescales for the period 2009-2010. The analysis was conducted using the Barnett-Preisendorfer approach to canonical correlation analysis (CCA) of reanalysis surface winds and HF radar-derived surface currents. The CCA yields two canonical patterns: the first wind-current interaction pattern corresponds to the classical Ekman drift at the sea surface, whilst the second describes an anticyclonic/cyclonic surface circulation. The results obtained demonstrate that local winds play an important role in driving the upper water circulation. The wind-current interaction timescales are mainly related to diurnal breezes and synoptic variability. In particular, the breezes force diurnal currents in waters of the continental shelf and slope of the south-eastern Bay. It is concluded that the breezes may force diurnal currents over considerably wider areas than that covered by the HF radar, considering that the northern and southern continental shelves of the Bay exhibit stronger diurnal than annual wind amplitudes.

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

Adaptive Scalable SVD Unit for Fast Processing of Large LSE Problems

Singular Value Decomposition (SVD) is a key linear algebraic operation in many scientific and engineering applications. In particular, many computational intelligence systems rely on machine learning methods involving high dimensionality datasets that have to be fast processed for real-time adaptability. In this paper we describe a practical FPGA (Field Programmable Gate Array) implementation of a SVD processor for accelerating the solution of large LSE problems. The design approach has been comprehensive, from the algorithmic refinement to the numerical analysis to the customization for an efficient hardware realization. The processing scheme rests on an adaptive vector rotation evaluator for error regularization that enhances convergence speed with no penalty on the solution accuracy. The proposed architecture, which follows a data transfer scheme, is scalable and based on the interconnection of simple rotations units, which allows for a trade-off between occupied area and processing acceleration in the final implementation. This permits the SVD processor to be implemented both on low-cost and highend FPGAs, according to the final application requirements.

Managing and resisting ‘degeneration’ in employee-owned businesses: a comparative study of two large retailers in Spain and the UK

Employee-owned businesses have recently enjoyed a resurgence of interest as possible ‘alternatives’ to the somewhat tarnished image of conventional investor-owned capitalist firms. Within the context of global economic crisis, such alternatives seem newly attractive. This is somewhat ironic because, for more than a century, academic literature on employee-owned businesses has been dominated by the ‘degeneration thesis’. This suggested that these businesses tend towards failure – they either fail commercially, or they relinquish their democratic characters. Bucking this trend and offering a beacon - especially in the UK - has been the commercially successful, co-owned enterprise of the John Lewis Partnership (JLP) whose virtues have seemingly been rewarded with favourable and sustainable outcomes. This paper makes comparisons between JLP and its Spanish equivalent Eroski – the supermarket group which is part of the Mondragon cooperatives. The contribution of this paper is to examine in a comparative way how the managers in JLP and Eroski have constructed and accomplished their alternative scenarios. Using longitudinal data and detailed interviews with senior managers in both enterprises it explores the ways in which two large, employee-owned, enterprises reconcile apparently conflicting principles and objectives. The paper thus puts some new flesh on the ‘regeneration thesis’.

Environmental Concerns in Water Pricing Policy: an Application of Data Envelopment Analysis (DEA)

25 p.