Gene profiles can be viewed as points in multidimensional space with the dimensionality given by the number of measurements.
Conceptually, each data item can be viewed as a point in a multidimensional space with the dimensions representing the physical features we record.
But it seems safe to say that it will be viewed as the point in time when participation in the data economy went mainstream.
Since covariance matrix (mathbf {C} in Sym_^{d}), it may be viewed as a point on a Riemannian manifold [16].
In this paper, h is a N t dimensional row vector, which can be viewed as a point in the N t dimensional space.
In one-dimensional warranty policies, remanufactured item failures can be viewed as random points occurring over a one-dimensional horizon.
Hubs can therefore be viewed as switching points for interactions.
In most cases those results would be better viewed as mistakes pointing to a next step that will be a bit less mistaken.
This mirrors the argument that the basic service layers — compute and storage and so forth — cannot be viewed as differentiation points in the future, current market dynamics aside.
The solutions for the problem can be viewed as fixed-points of a coupled equation formed in terms of these proximity operators.
