As against the utilitarians he showed that the beautiful pleases "without interest" (i.e., utilitarian interest); against the intellectualists, that it pleases "without concepts"; and further, against both, that it has "the form of purposiveness" without "representation of a purpose"; and, against the hedonists, that it is "the object of a universal pleasure".
This study exemplifies the application of detailed, discrete-event, stochastic simulation models to derive deterministic, analytical response surface representations for purposes of plant design and throughput analysis in the process industries.
By contrast, the bootstrapping that tort law refuses is of the essence of contract and underwritten by the fact that contractual promisors, in contrast to those who make representations for purposes of tort law, intend not just to convey information but rather, directly though the self-same intentions, to assume obligations.
We propose a novel type of CSIT representation for the purpose of user selection, coined as ranking-based feedback.
The content validity of the tool as a representation of its intended purpose was also qualitatively assessed.
"Thus if the State provisionally represented the common purposes of individuals, it could be seen figuratively as a single individual being the representation of the common purposes of all men within it.
Also, the linear logic is proposed to characterize the state of changes in the Petri net model accurately by using symbolic time representation for the purpose of acquiring deterministic behavior.
In this paper we present a new approach to road network representation for the purpose of monitoring and controlling traffic.
We define an object-oriented data model called XSDM (XML Schema Data Model) and present a graphical representation of XML Schema for the purpose of schema integration.
$P X)$ might be a good enough representation of belief for some purposes.
Chopra et al. [58] propose a Deep Learning model (based on neural networks) for domain adaptation which strives to learn a useful (for prediction purposes) representation of the unsupervised data by taking into consideration information available from the distribution shift between the training and test data.
