The Determinants of Hedging by Derivatives in Hong Kong and Chinese Firms and the Value Effects

This dissertation studies the determinants and the value effects of corporate hedging with derivatives for 230 Hong Kong and Chinese non-financial firms listed in Hong Kong Stock Exchange from 2008 to 2013. With the data from annual reports, the evidence is found that there are positive relations between deciding to hedge with derivatives and foreign currency exposures, expected cost of financial distress, and scale of economies. The liquidity measures are negatively linked with the usage of derivatives. The empirical results of the financial distress costs and liquidity factors for Chinese firms are relatively weaker than those of Hong Kong firms, which may be explained by the state and the government as the major shareholder providing financial supports and the debt guarantee. Finally, the growths of firm values from hedging activities are 1.70% for Hong Kong firms and 0.37% for Chinese firms from the perspective of tax benefits

ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation

Liver tumor segmentation is essential for computer-aided diagnosis, surgical planning, and prognosis evaluation. However, obtaining and maintaining a large-scale dataset with dense annotations is challenging. Semi-Supervised Learning (SSL) is a common technique to address these challenges. Recently, Segment Anything Model (SAM) has shown promising performance in some medical image segmentation tasks, but it performs poorly for liver tumor segmentation. In this paper, we propose a novel semi-supervised framework, named ASLseg, which can effectively adapt the SAM to the SSL setting and combine both domain-specific and general knowledge of liver tumors. Specifically, the segmentation model trained with a specific SSL paradigm provides the generated pseudo-labels as prompts to the fine-tuned SAM. An adaptation network is then used to refine the SAM-predictions and generate higher-quality pseudo-labels. Finally, the reliable pseudo-labels are selected to expand the labeled set for iterative training. Extensive experiments on the LiTS dataset demonstrate overwhelming performance of our ASLseg

Concern or Control?: Gender Stereotyping and Hospitality Leaders

Although most managers in the global hospitality industry are still male, an increasing number of women are taking on leadership roles. But how exactly do employees perceive masculine and feminine leadership styles? New research led by UCF Rosen College of Hospitality Management\u27s Associate Professor Bendegul Okumus and the research team she works with looks at gender stereotypes and finds that the most successful managers, in the eyes of their staff, have a management style that combines both masculine and feminine leadership traits

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin