Sentence examples for generating objects from inspiring English sources

In the past, the problem of uniform random generation of combinatorial structures, that is the problem of randomly generating objects (of a preliminary fixed input size) of a specified class that have the same or similar properties, has been extensively studied.

Coming back to the random testing problem from software engineering, we observe that generating objects of a given class of input data according to a uniform distribution is sufficient for testing the correctness of particular algorithms.

The process of generating objects in images to represent mixtures of multiple fundamental patterns follows the Bayesian network in Figure 2. The generative process is as follows: for each of M images, a mixture θ i is first sampled (conditioned on the hyper-parameter α).

In order to show that attentional modulation can be applied with benefits to different object detection algorithms, we evaluate the effects of attentional modulation using symmetry (see Sect. " Symmetry-based object detection") for generating object hypotheses.

In processing mode, either symmetry (see Sect. " Symmetry-based object detection") or the appearance-based classifier (see Sect. " The appearance-based classifier") is used for generating object hypotheses but never both at the same time.

Then M ∈ 〈 G ♢ (d ) 〉 and M ∉ 〈 G ♢ (d − 1 ) 〉 if and only if there exists an N ∈ T such that G G d Hom ∗ (M, N ) ≠ 0. An important conceptual point about this perspective is that, by choosing a generating object G, the homotopy category of A has been enhanced to a filtered category.

In principle, two general (systematic) approaches have been developed for the uniform generation of these structures: First, the recursive method originated in [ 24] (to generate various data structures) and later systematized and extended in [ 25] (to decomposable data structures), where general combinatorial decompositions are used to generate objects at random based on counting possibilities.

The distribution of generated objects is an important factor of the simulated image generation process.

These Schematics, as well as any of the randomly generated objects in the game world, including archways, towers and trees, can all be re-skinned en masse.

A naive comprehension principle generates objects that violate the principle of noncontradiction.

Classes are assembled from (composite) records and traits and instantiated to generate objects.