The contemporary revival of interest in the Sacred as a category of philosophico-religious reflection here finds a radical reversal of the traditional direction, taking the Sacred as the starting point of the itinerary toward the Divine. The wide variety of essays contained in this volume attempt to ground philosophy of the Sacred and the Divine in phenomenological evidence. Though employing different methodologies, the contributors register by and large the contribution of A-T. Tymieniecka's phenomenology of life in providing a significant (...) 20th century vision for the accomplishment of this task. Its pursuit finds here expression in philosophical, historical, literary and political explorations leading to construing phenomenology of the Sacred as a prerequisite to the investigation of the Divine. The contributors to this extraordinary collection are: C. Bédard, A. Ales Bello, Gerard Bucher, D. Chidester, D. Conchi, M. Kronegger, S. Laycock, Ph. Liverziani, J.N. Mohanty, E. Moutsopoulos, A.M. Olson, Y. Park, G. Penzo, B. Ross, C. Osowiec Ruoff, Th. Ryba, J. Smith, A-T. Tymieniecka and E. Wyschogrod. (shrink)

Extreme Learning Machine is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for unsupervised domain adaptation. It introduces Cross-Domain Mean Approximation into the hidden layer of (...) ELM to reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross-Domain Mean Approximation as the kernelization extension of DELM-CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM-CDMA and DKELM-CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches. (shrink)