Does Cross-Listing in the US Foster Mergers and Acquisitions and Increase Target Shareholder Wealth?

We examine the role of cross-listing in alleviating domestic market constraint and facilitating cross-border mergers and acquisitions. Cross-listing appears to strengthen the bargaining power of target firms, allowing them to extract higher takeover premiums relative to their non-cross-listed peers. Moreover, shareholders of Sarbanes-Oxley-compliant targets seem to benefit from a higher premium. We also find that cross-listed firms are more likely to be acquisition targets. This evidence is consistent with the idea that cross-listing increases firms’ attractiveness and visibility on the market for corporate control. Our results are robust to various specifications and to the self-selection bias arising from the decision to cross-list.Cross-listing, mergers & acquisitions, governance, Sarbanes-Oxley Act

Skin involvement as the presenting sign of a male breast cancer

Male breast cancer is a rare disease with an unknown etiopathogenesis. We report a case of a 50-year-old man with a breast cancer revealed by skin involvement

Jordanian Working Women’s Perception of Life Difficulties

This study is an attempt to identify Jordanian working-women’s perception of their life difficulties. Areas of life’s difficulties include: psychological, social, political and career. Such difficulties were investigated across five variables: age, education, employment, years of experience and civil status. Each variable included subgroups. The sample consisted of 186 subjects living in the Amman district. Subjects responded to a questionnaire about life difficulties. The questionnaire’s internal reliability as well as test-retest reliability ranged from .79 to .92. Statistical analysis of data consisted of ANOVA analysis of variance and the Scheffe test of differences between groups. Results were tested at the .5-level or better. Jordanian women reported significant differences in all four areas of life’s difficulties

Where Have the Litigants Gone?

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reason, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have analyzed 1) several Convolutional Neural Network (CNN) architectures, 2) data augmentation techniques and 3) transfer learning. We have achieved the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.Comment: 22 pages, 10 figure

Performance Analysis of the Multi-pass Transformation for Complex 3D-Stencils on GPUs

Artículo presentado al Congreso Español de Informática 2013Performance Analysis of the Multi-pass Transformation for Complex 3D-Stencils on GPU