The Strategic Role of Information Asymmetry on Demand for the Multinational Enterprise

We study how asymmetric information impinge on oligopolistic firms’ decision between direct investment and exports in a game-theoretic model with Bayesian learning. Host firms have superior information about market demand and foreign firms can improve their knowledge if foreign direct investment (FDI) is undertaken. In addition to the well-known tension between the fixed set-up costs of investment, the additional variable costs of exports and oligopoly sizes, the incentive to invest abroad is explained by the strategic learning effect. FDI may be observed even if foreign firms are pessimistic or trade costs are zero. Interestingly, compared with the certainty equivalent, the equilibrium number of investors is larger when foreign firms hold optimistic beliefs or, if these are pessimistic, when the strategic learning effect outweighs the conjecture effect.Asymmetric information; Bayesian learning; FDI; international oligopoly

Collective Goods and the Court: A Theory of Constitutional Commodification

Not everything is or should be for sale. Collective goods such as our democracy and parts of our natural environment would be destroyed if they were transformed entirely into commodities to be bought and sold in commercial markets. This Article examines a discrete and unexplored topic within the larger literature on commodification: the extent to which the U.S. Supreme Court participates in the commodification of collective goods. The Court shifts market boundaries through its constitutional interpretations that glorify commodities and exalt individual rights at the expense of collective goods. Examining two lines of cases holding that “money is speech” and “waste is commerce,” this Article contributes a theoretical understanding of the nature of collective goods and their commodification through constitutional interpretation. It also makes recommendations for how the Court and our larger society should address these kinds of issues in the future

Meat Sanitation Pays.

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A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks

Among the many possible approaches for the parallelization of self-organizing networks, and in particular of growing self-organizing networks, perhaps the most common one is producing an optimized, parallel implementation of the standard sequential algorithms reported in the literature. In this paper we explore an alternative approach, based on a new algorithm variant specifically designed to match the features of the large-scale, fine-grained parallelism of GPUs, in which multiple input signals are processed at once. Comparative tests have been performed, using both parallel and sequential implementations of the new algorithm variant, in particular for a growing self-organizing network that reconstructs surfaces from point clouds. The experimental results show that this approach allows harnessing in a more effective way the intrinsic parallelism that the self-organizing networks algorithms seem intuitively to suggest, obtaining better performances even with networks of smaller size.Comment: 17 page

Capas finas de ZrO2 para mejorar la resistencia química de los vidriados mates de calcio

La resistencia al ataque químico de vidriados cerámicos susceptibles al mismo, concretamente formulados a partir de una frita mate de calcio, se ha mejorado depositando en la superficie una capa delgada de óxido de circonio. Un sol de circonio estabilizado es el precursor de la capa, el cual se deposita mediante aerografía sobre el vidriado cocido y con un tratamiento térmico suave (Tmax ≈ 400 ºC), es transformado en ZrO2 y anclado a la superficie. Los ensayos de resistencia química demuestran que el vidriado protegido no sufre un ataque apreciable visualmente por parte del ácido clorhídrico concentrado, condiciones en las que el mismo vidriado sin el recubrimiento resulta gravemente dañado. El precursor utilizado genera una capa de óxido de circonio que protege el vidriado subyacente, de modo que el ataque químico por parte del ácido clorhídrico queda confinado al entorno de las grietas y discontinuidades de la capa depositadaPeer reviewe

3D reconstruction of medical images from slices automatically landmarked with growing neural models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid

Theoretical and experimental insights on firms' internationalization decisions under uncertainty

We revisit and extend previous theoretical work on internationalization decisions by firms which are imperfectly informed on the state of the demand in the market into which they are planning to export or enter through foreign direct investment (FDI). The latter is a costly strategy mitigating the international firm's demand uncertainty, while the local firm is perfectly informed. We report results from an experimental test of the aforementioned framework which confirm dominant strategy play by local firms under both the good and bad states of the local demand. Also, the prediction that the magnitude of the FDI-specific cost determines whether foreign firms enter via FDI is confirmed in qualitative terms. However, in the case in which FDI is the dominant strategy under risk neutrality, less than full FDI adoption is obtained. We also find an unexpected interaction between the internationalization decision and the market strategy once entry has occurred, indicating the presence of relevant behavioral and strategic factors which are not anticipated by the theoretical model

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis