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The expansion operator for belief sets, K+p = Cn(K∪{p}), was constructed to ensure that the outcome is logically closed.
Our GGA algorithm uses the expansion operator progressively but attempts to realize the optimal scale of revealing the intrinsic relationship among proteins.
However, due to the ambiguity of the expansion operator, we decided to remove these operators from the queries.
The expansion operator assigns new probabilities for all pairs of nodes, while the inflation operator changes the probabilities for all these walks in the graph, boosting the probabilities of intra-cluster walks and demoting inter-cluster walks.
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As a by-product, we obtain related sharp estimates for the so-called re-expansion operator, an important object in some problems of mathematical physics.
The ADN strategy enhances the local search capability around the global-best solution in a dimension by dimension manner, and the search distance is adapted by shrinking and random-expansion operators.
This method arises from an observation that potential evaluation using the multipole to local expansion translation operator can be expressed as a series discrete convolutions of the multipole moments with their associated spherical harmonics functions.
Near kinematic thresholds S encodes nonperturbative information, while far from thresholds S can be computed with an expansion in operators.
This result provides hypotheses under which a rigorous numerical analysis could be conducted for recent generalizations to the methods of Operator Expansions, Field Expansions, and Transformed Field Expansions.
Additionally, it is a provably stable algorithm as opposed to other boundary perturbation approaches such as Bruno and Reitich's "method of field expansions" or Milder's "method of operator expansions".
We focus on the benefits of angle axis parameters by considering a multipole operator expansion of the rotation operator ˆD Φ,ˆn), and a Clebsch Gordan expansion of the rotation matrices DJMM′ Φ,ˆn).
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