Board structure and survival of new economy IPO firms

Research Question/Issue: This study examines the relevance of currently accepted best practice recommendations regarding board structure on the survival likelihood of new economy initial public offering companies. We argue that industry context determines governance outcomes. Research Findings/Insights: We study 125 Australian new economy firms listed between 1994 and 2002. Each firm is tracked until the end of 2007 for monitoring their survival. We find that board independence is associated with an increase in the likelihood of corporate survival. We also find that the benefits of board independence increase at a decreasing rate. Theoretical/Academic Implications: The standard best practice recommendation of board independence stems from the monitoring role of directors and is based on agency theory. The results from our study suggest that the recommendation regarding board independence does not work well for new economy firms. While the agency theory based model implies a monotonic relation between board independence and performance, our research suggests that the relationship is nonlinear. This variation occurs because of increased monitoring costs faced by outsiders due to higher information asymmetry and complexity of new economy firms. Our empirical results suggest that inside directors play a complementary role to outsiders in mitigating firm failure. Practitioner/Policy Implications: Our research offers insights to policy makers who are interested in setting best practice standards regarding board structure. Our research suggests that firm/industry characteristics play a crucial role in determining the optimal board structure. In firms/industries where outsiders face significantly higher information processing costs, insiders can play a valuable complementary role to outsiders in enhancing the effectiveness of the board. Thus future hard or soft regulations related to board structure should consider industry context.

Nanomídia(s): legitimidade e interdisciplinaridade face ao antropoceno

New media epistemologies are emerging and might be considered illegitimate not because of plain rejection or criticism, but because of their alien origins and inter/ transdisciplinary implications. This article tells the story of a nano, tiny world within the world of media studies: the world of the term ‘nanomedia’ and its hyphenated sister ‘nano-media’. It narrates the different uses of this term as an illustration of the way in which disciplinarity determines the level of legitimacy or illegitimacy of an emerging term. We present another possible use of the term nanomedia in the field of media studies, one that is more closely aligned with its scientific origins. The importance and relevance of this proposition is connected to the present challenges we face in the anthropocene.

Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation

In this study, we have investigated the evidence of fetal heart rate asymmetry and how the fetal heart rate asymmetry changes before and after 35 weeks of gestation. Noninvasive fetal electrocardiogram (fECG) signals from 45 pregnant women at the gestational age from16 to 41 weeks with normal single pregnancies were analysed. A nonlinear parameter called heart rate asymmetry (HRA) index that measures time asymmetry of RR interval time-series signal was used to understand the changes of HRA in early and late fetus groups. Results indicate that fetal HRA measured by Porta's Index (PI) consistently increases after 35 weeks gestation compared to foetus before 32 weeks of gestation. It might be due to significant changes of sympatho-vagal balance towards delivery with more sympathetic surge. On the other hand, Guzik's Index (GI) showed a mixed effect i.e., increases at lower lags and decreases at higher lags. Finally, fHRA could potentially help identify normal and the pathological autonomic nervous system development.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Multi-view subspace clustering for face images

In many real-world computer vision applications, such as multi-camera surveillance, the objects of interest are captured by visual sensors concurrently, resulting in multi-view data. These views usually provide complementary information to each other. One recent and powerful computer vision method for clustering is sparse subspace clustering (SSC); however, it was not designed for multi-view data, which break down its linear separability assumption. To integrate complementary information between views, multi-view clustering algorithms are required to improve the clustering performance. In this paper, we propose a novel multi-view subspace clustering by searching for an unified latent structure as a global affinity matrix in subspace clustering. Due to the integration of affinity matrices for each view, this global affinity matrix can best represent the relationship between clusters. This could help us achieve better performance on face clustering. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other alternatives based on state-of-The-Arts on challenging multi-view face datasets.