Organisational culture and privatisation:A case study of the Argentinean railway sector

Public units that have been through a deregulatory process operate in a new market, see their legal framework radically altered, have to fight for customers and to generate their own resources. Such changes affect the way a company operates and obviously also affect the culture an organisation has. This study illustrates how the organisational culture of Ferrocarriles Argentinos, an Argentinean state-owned-enterprise, was transformed as a consequence of the company's partitioning and subsequent transfer into private hands. The case provides a good example of how a weak and dysfunctional culture was re-oriented towards an emphasis on customers, cost-reduction and efficiency. It also provides an interesting example for the management of ongoing as well as future privatisation processes. The insights gained through this study could find application within organisations that, though belonging to the private sector, have been heavily regulated and are trying to get rid of the pernicious cultural by-products of this regulation.

Effects of high-energy protons on selected cells Final report, Jun. 1966 - Aug. 1966

Irradiation effects of high energy protons studied on silver-cadmium and nickel-cadmium cells containing battery electrodes and potassium hydroxide electrolyte

Radiation effects on silver and zinc battery electrodes. V Interim report, Apr. - Jul. 1966

Gamma radiation effects determined on silver and zinc battery electrodes and silver-cadmium cell

Radiation effects on silver and zinc battery electrodes, II Interim report, Jul. - Oct. 1965

Radiation effects on silver and zinc electrodes in silver-zinc batter

Radiation effects on silver and zinc battery electrodes, i interim report, apr. - jul. 1965

Radiation effects on silver and zinc battery electrode

Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization

The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative filtering. Although specific instances can often be solved with specialized algorithms, the general affine rank minimization problem is NP-hard. In this paper, we show that if a certain restricted isometry property holds for the linear transformation defining the constraints, the minimum rank solution can be recovered by solving a convex optimization problem, namely the minimization of the nuclear norm over the given affine space. We present several random ensembles of equations where the restricted isometry property holds with overwhelming probability. The techniques used in our analysis have strong parallels in the compressed sensing framework. We discuss how affine rank minimization generalizes this pre-existing concept and outline a dictionary relating concepts from cardinality minimization to those of rank minimization

The effects of radiation on nickel-cadmium battery electrodes, i final report, jun. 1963 - apr. 1965

Effect of radiation on nickel-cadmium battery electrode

Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e.,~linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods