This letter proposes a type of mutually cooperative relaying (MCR) scheme based on a spatially coordinate-interleaved orthogonal design (SCID), in which two cooperative users are spatially multiplexed without bandwidth expansion. It provides not only diversity gain (with order of two) as in the existing MCR scheme, but also additional coding gain. Our simulation results demonstrate that the proposed SCID scheme is useful for improving the uplink performance as long as one user can find another active user as a close neighbor that is simultaneously communicating with the same destination, e.g., a base station in the cellular network.

In this paper, we propose new external context features for the semantic classification of bio-entities. In the previous approaches, the words located on the left or the right context of bio-entities are frequently used as the external context features. However, in our prior experiments, the external contexts in a flat representation did not improve the performance. In this study, we incorporate predicate-argument features into training the ME-based classifier. Through parsing and argument identification, we recognize biomedical verbs that have argument relations with the constituents including a bio-entity, and then use the predicate-argument structures as the external context features. The extraction of predicate-argument features can be done by performing two identification tasks: the biomedically salient word identification which determines whether a word is a biomedically salient word or not, and the target verb identification which identifies biomedical verbs that have argument relations with the constituents including a bio-entity. Experiments show that the performance of semantic classification in the bio domain can be improved by utilizing such predicate-argument features.