As an assistant curator of North American ethnology and archaeology with the Field Museum of Natural History, Chicago (1926 29), he did field work in southern California that persuaded him of the necessity for knowledge of the past to gain understanding of living cultures; he also concluded that insight into prehistoric cultures should ideally proceed from knowledge of living cultures.
The explosive growth of high dimensional data sets has demanded the development of semiautomated or automated tools as a necessity for knowledge discovery [ 4, 40].
Consequently, there is an additional necessity for improved knowledge of tailings eco-compatibility when reused as construction material.
Predictions of biodiversity impacts depended on precise inputs, underlying both the view of this system as being complex, and the necessity for detailed knowledge.
Although none of these data exclude a role for mirror neurons in action understanding in music or language, they argue against a necessity for motor knowledge to either understand sensory input or to elicit specific activity in the IFG.
Even if complications related to IOP elevation during suction ring application in refractive surgery are rare, they raise the necessity for more knowledge about IOP changes during such procedures [ 2, 1, 8- 10].
The high numbers of drug interactions with important risk incidents, especially in the sick population, alert us to the necessity of knowledge by the intensivist for the use of drugs, due to the iatrogenic potential exacerbating the severity already underway.
That's why there is a necessity not only for knowledge of the complete on-machine measurement system errors, but also for the knowledge about probe's errors in separation from machine tools.
As early as 1855, in his Principles of Psychology, Spencer proposed a form of evolutionary epistemology, arguing for a third way between empiricism's emphasis on the necessity of experience for knowledge, and rationalism's insistence on the importance of a priori knowledge.
These are mainly differentiated by their computational complexity (energy detection being the most computationally efficient, thus simplest to implement), necessity for a priori knowledge of the observed signals (matched filters), and the means of extracting features of the recognized signals (feature detectors).
The difficulties present in using conventional techniques to model such a complex system make the application of artificial neural networks (ANNs) to these problems particularly attractive because of their capability for nonlinear mapping and lack of necessity for detailed mechanistic knowledge.
