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We present an illustrative real-world case study and some randomly generated instances for computational applications.
In this paper, an overview of the state of the art in building efficient silicon for computational applications is presented, including the techniques actively used by the industry, the upcoming new technologies, and the research initiatives geared toward the future.
The stability of the equilibrium point (background activity) of oscillatory neural networks is an important property for computational applications that explore the switching between background activity and oscillatory states.
One important insight coming out of this work suggests there is a need for lexical representation of one kind or another, at least for computational applications.
A completely information-driven schema based on a petabyte-scale platform for computational applications will allow routine data gathering and access.
These approaches, however, appear to require infinite memory of the past and have so far not been developed for computational applications.
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User interfaces of computational applications for life sciences are usually very rich and detailed with many types of data.
We hope that this report will further alert investigators in our and other scientific fields to available resources for specific computational applications and help to select data sets and tools that are relevant for given research topics.
The proposed solution is application-agnostic and can be used for enterprise and computational applications.
High single instruction multiple data (SIMD) efficiency and low power consumption have made graphic processing units (GPUs) an ideal platform for many complex computational applications.
Nevertheless, methods that correct for research focus biases can potentially improve the use of such PPIN and pathway databases for their various computational applications.
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