This work offers an Embodied Construction Grammar (Feldman et al. 2010) representation of caused motion, thereby also providing (a sample of) the computational infrastructure for implementing the information that FrameNet has characterized as Caused_motion1 (Ruppenhofer et al. 2010).
This sampling scheme is intended to reduce the number of overall samples for computational reasons, by decreasing the number of samples taken in more recent years as more sequences were generated, while maintaining available data during the seasonal troughs.
a The Matlab script performing the algorithm from [46] is available at http://www.acoustics.hut.fi/publications/papers/spl-adf/adf.m. b The PD patch and sound samples of the computational model will be shared with the community and made available at http://a3lab.dii.univpm.it/projects/jasp-clavinet.
We also note that an existing VBEM algorithm implementation, TIGAR, does not provide a significant improvement over Gibbs sampling in terms of computational time in our examples, as well as having a very high memory requirement.
Nested sampling has the advantage of computational feasibility.
However, due to the lack of manually labeled training samples and the limitation of computational resources, the number of available concept detectors to date remains in the scale of hundreds, which is much smaller compared to the size of human vocabulary.
An effective and flexible method for the generation of computational samples for mesoscopic modeling of anisotropic networks of carbon nanotube (CNT) bundles with various degrees of CNT alignment is developed and applied for investigation of structural self-organization of nanotubes into vertically aligned CNT forests and fibers.
The chapter develops a comprehensive theory of the complexity of induction, which analyzes the inherent difficulty of various kinds of learning problems in terms of sample complexity and computational complexity.
The performance of several sample chambers was studied by means of computational fluid dynamics.
Consequently, the required number of frequency samples and computational complexity of decomposition reduce dramatically.
