REREADING MIRCEA ELIADE: SOME MYTHS AND TRUTHS ABOUT THE SACRED, THE HISTORICAL, AND THE WWII

This project grows out of my dissatisfaction with a number of popular critiques against Mircea Eliade’s approach to religious phenomena, in particular the charges along the lines that his academic writings are crypto-theological, ahistorical, and fascist. The set of questions I ask are as follows: Does Eliade assume the existence of a transcendent, autonomous entity in his explanation of religion, as his critics claim? Is “ahistorical” accurate to capture Eliade’s sense of the relationship between religious phenomena and history? Why does Eliade not take advantage of the more “historical” or “scientific” tools of analysis of his time, such as Marxism and the like? Through close examination of Eliade\u27s works, especially the two foundational pieces, The Myth of the Eternal Return, or Cosmos and History and Patterns in Comparative Religion, and the newly published diary, The Portugal Journal, I argue that Eliade writes about the sacred consistently as an element of human experience rather than an autonomous existence outside experience. Secondly, I argue that Eliade does not dismiss the political origins or manipulation of religion, but highlights the dynamic encounter between humans and natural phenomena in its origin. In terms of the general relationship between religion and history, Eliade seems to have a Weberian sense of elective affinity. Thirdly, I argue that Eliade downplays the scientific theories of his time in his interpretation of religious phenomena, because he perceived an intrinsic opposition in them to the spiritual freedom that he desperately struggled for to defend himself from personal and historical disasters of WWII. Eliade\u27s fundamental pragmatic position with regards to religion emerges from these discussions

Planning Towards Equal Spatial Accessibility of NCI Cancer Centers Across Geographic Areas and Demographic Groups in the U.S.

The Cancer Centers designated by the National Cancer Institute (NCI) form the “backbone” of the cancer care system in the United States. Awarded via a peer-review process and being re-evaluated every 3 to 5 years, an NCI Cancer Center receives substantial financial support from NCI grants. When the quality standard is not compromised, we argue that an additional criterion for improving and promoting equal accessibility should be factored into the designation and planning process of NCI Cancer Centers. With the help of regression and dummy variables, this research evaluates geographic disparities in spatial accessibility of the NCI Cancer Centers across geographic area, divisions and urbanicity. It also evaluates demographic disparities across ethnic and poverty groups. Then this research examines two planning objectives to minimize the inequalities in accessibility. One is to minimize the geographic inequality while the other is to minimize the racial disparities. Two types of optimization scenarios are considered in this exploratory research for the objective of minimizing inequality of spatial accessibility. One is to allocate additional resources to existing NCI Cancer Centers, and the other is to designate new centers from the most likely candidates (e.g., existing academic medical centers or AMCs). Quadratic Programming (QP) and Particle Swarm Optimization (PSO) are used to solve different optimization problems. Several scenarios are used to illustrate the impact of optimization on reducing geographic and demographic disparities. Results from the study may inform the public policy decision making process in planning of the NCI Cancer Centers towards equal accessibility

An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm