Grant support and exporting activity

This paper investigates whether government support can act to increase exporting activity. We use a uniquely rich data set on Irish manufacturing plants and employ an empirical strategy that combines a nonparametric matching procedure with a difference-in-differences estimator in order to deal with the potential selection problem inherent in the analysis. Our results suggest that if grants are large enough, they can encourage already exporting firms to compete more effectively on the international market. However, there is little evidence that grants encourage nonexporters to start exporting.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.