INTERDISCIPLINARY RESEARCH CENTER OF EXCELLENCE FOR CHILDREN WELFARE: BRAIN AND BODY

Author(s):  
NATHALIE CHARPAK ◽  
STEFANO PARMIGIANI ◽  
FREDERICK S. VOM SAAL
AJS Review ◽  
2017 ◽  
Vol 41 (1) ◽  
pp. 1-7
Author(s):  
Eli Lederhendler

The collective discussion embodied in the following group of essays is the outgrowth of a three-year-long symposium on Jewish and urban studies conducted at the Hebrew University's Scholion Interdisciplinary Research Center in the Humanities and Jewish Studies from 2009 to 2012. The synergy that animated our weekly discussions owed something to the fact that, rather than chiming in on similar notes, we partook of a wide sampling of reading and analysis. We came from different disciplines, with different agendas: scholars of literary criticism, adepts of social theory, historians, cultural analysts, an expert in religious philosophy, and a landscape architect with a critical interest in the culture and politics of spatial construction. The broad sweep of our discussions was greater than will be evident from this selection of papers, since our circle of discussants continually swelled and altered during those three years, reshuffling the range of participants and topics. However, most of those whose work is represented in this sampling were present throughout the entire three-year project.


2005 ◽  
Vol 47 (3) ◽  
Author(s):  
Thomas Barkowsky ◽  
John Bateman ◽  
Christian Freksa ◽  
Wolfram Burgard ◽  
Markus Knauff

SummuryThe Transregional Collaborative Research Center SFB/TR 8 Spatial Cognition was established by the German Science Foundation (DFG) at the Universities of Bremen and Freiburg in January 2003. 13 Research projects pursue interdisciplinary research on intelligent spatial information processing. This article introduces the research field of spatial cognition and reports on aspects from cognitive psychology, cognitive robotics, linguistics, and artificial intelligence.


2021 ◽  
Vol 3 (1) ◽  
pp. 55-67
Author(s):  
Vladimir Rakin

The practice of creating interdisciplinary, multidisciplinary research centers in Russia is caused by the need to implement interdisciplinary projects in accordance with the objectives of the Strategy of scientific and technological development and priority directions of science development in the Russian Federation. However, the development of complex research within a single institution faces a number of problems related to the management of the multidisciplinary center, the system of evaluation of research efficiency in separate divisions of the center, funding of individual research areas, creation of a comfortable psychologicalclimate that promotes the performance and effective completion of interdisciplinary projects, creation of an effective stimulation system, aimed not only at increasing the publication activity of researchers, but also to produce world-class results that require in-depth, focused work. The problems of creating an effective management system focused on ensuring maximum creative freedom of a scientist and reducing bureaucratic pressure on a researcher and creating a comfortable psychological climate in an interdisciplinary research center depend mainly on the head of the organization and his management apparatus. The development of scientometric system of comparative rating of the effectiveness of scientific teams within separated units, the creation of a differentiated system of motivation of scientists, aimed not only at increasing the publication activity, but also to obtain breakthrough scientific results requires the involvement of a serious scientific approach, despite the fact that scientometric research is not among the prestigious and attractive scientific directions, but at the same time it causes a high critical reaction from officials and scientists of all scientific fields.


Author(s):  
Emmanuel Ramasso ◽  
Abhinav Saxena

Six years and more than seventy publications later this paper looks back and analyzes the development of prognostic algorithms using C-MAPSS datasets generated and disseminatedby the prognostic center of excellence at NASA Ames Research Center. Among those datasets are five run-to-failure CMAPSS datasets that have been popular due to various characteristicsapplicable to prognostics. The C-MAPSS datasets pose several challenges that are inherent to general prognostics applications. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than seventy publications have used the C-MAPSS datasets for developing datadriven prognostic algorithms. However, in the absence of performance benchmarking results and due to common misunderstandings in interpreting the relationships between these datasets, it has been difficult for the users to suitably compare their results. In addition to identifying differentiating characteristics in these datasets, this paper also provides performance results for the PHM’08 data challenge wining entries to serve as performance baseline. This paper summarizes various prognostic modeling efforts that used C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.


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