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

Prevention and Management of Bleeding During Endoscopic Approaches to Skull Base Pathologies

The rate of serious permanent morbidity and mortality with endonasal approaches has declined secondary to increased knowledge of the pertinent anatomy, advanced neuroimaging and navigation techniques, better surgical instruments, and improved exposure and reconstruction strategies.1-3 Although rare, vascular injury remains a potentially serious complication. However, with limited systematically-collected and reported data, the exact incidence rate of vascular injuries is difficult to determine. In terms of arterial injuries, the incidence based on reported series likely ranges from 0.3%-9% (Table 1),4-11 with higher rates most commonly associated with chordomas and chondrosarcomas involving the clivus. Venous injury is comparatively less severe and easier to manage. As a result, there is a comparatively lower impetus to publish epidemiological data and management strategies for these injuries. The consequences of arterial injury include fatal hemorrhage, vessel occlusion or thromboembolism causing infarction, development of a pseudoaneurysm (PA), carotid-cavernous fistula (CCF), subarachnoid hemorrhage (SAH), and vasospasm.6,7,9 Surgical expertise and detailed knowledge of the neurovascular anatomy is critical to the avoidance and management of vascular injuries. Pages: 20-2

The ℓs\ell^s-boundedness of a family of integral operators on UMD Banach function spaces

We prove the $\ell^s$-boundedness of a family of integral operators with an operator-valued kernel on UMD Banach function spaces. This generalizes and simplifies earlier work by Gallarati, Veraar and the author, where the $\ell^s$-boundedness of this family of integral operators was shown on Lebesgue spaces. The proof is based on a characterization of $\ell^s$-boundedness as weighted boundedness by Rubio de Francia.Comment: 13 pages. Generalization of arXiv:1410.665

Managing the Socially Marginalized: Attitudes Towards Welfare, Punishment and Race

Welfare and incarceration policies have converged to form a system of governance over socially marginalized groups, particularly racial minorities. In both of these policy areas, rehabilitative and social support objectives have been replaced with a more punitive and restrictive system. The authors examine the convergence in individual-level attitudes concerning welfare and criminal punishment, using national survey data. The authors\u27 analysis indicates a statistically significant relationship between punitive attitudes toward welfare and punishment. Furthermore, accounting for the respondents\u27 racial attitudes explains the bivariate relationship between welfare and punishment. Thus, racial attitudes seemingly link support for punitive approaches to opposition to welfare expenditures. The authors discuss the implications of this study for welfare and crime control policies by way of the conclusion