Sentence examples for ranked objects from inspiring English sources

We study the welfare maximizing assignment of several heterogeneous, commonly ranked objects to impatient agents with privately known characteristics who arrive sequentially according to a Poisson or renewal process.

Then, we search the best matching between the local signatures of the query and the top k ranked objects.

The 4th worker is a single instance worker that identifies the top ranked objects according to collision probability (and other physical characteristics), normally around 10 pieces of debris.

Given a query and a list of ranked objects, we define for each ranked object a variable equal to if its class is equal to the class of the query and otherwise.

In this paper, we focus on one such function, maximum, that finds the highest ranked object or tuple in a set.

For example, the 6 th ranked object for query 'acute myeloid leukemia,' CSF3 (Colony Stimulating Factor 3), was not found in either of the two curated sources).

The same approach of average rank can be seen, for example, in [ 48] and it is used to rank objects based on several criteria simultaneously.

In this paper we present an improved iterative algorithm for online ranking object quality and user reputation in terms of the user degree (IRUA), where the user's reputation is measured by his/her rating vector, the corresponding objects' quality vector and the user degree.

The main difference is that we need to aggregate the matching segment(s) for each object, and rank the objects to produce the final result.

Many sources on the Internet and elsewhere rank the objects in query results according to how well these objects match the original query.

Comparability and incomparability relations between objects can be visualized in a so called Hasse diagram, which is instructive in ranking the objects by multi-criteria/non-compensatory approaches.