. We describe a novel technique for code selection based on data-flow graphs, which arise naturally in the domain of digital signal processing. Code selection is the optimized mapping of abstract operations to partial machine instructions. The presented method performs an important task within the retargetable microcode generator CBC, which was designed to cope with the requirements arising in the context of custom digital signal processor (DSP) programming. The algorithm exploits a graph representation in which control-flow is modeled by scopes. 1 Introduction In the domain of medium-throughput digital signal processing, micro-programmable processor cores are frequently chosen for system realization. By adding dedicated hardware (accelerator paths), these cores are tailored to the needs of new applications. Optimized processor modules can be reused, which is a major benefit compared to high-level synthesis [28] where a completely new design is developed for each application. ...

We describe a system that examines input sentences with respect to arbitrary topics formulated as natural language expressions. It extracts predicate-argument structures from text intervals and links them into semantically organized proposition trees. By instantiating trees constructed for topic descriptions in trees representing input sentences or parts thereof, we are able to assess degree of “topicality ” for each sentence. The presented strategy was used in the BBN distillation system for the GALE Year 1 evaluation and achieved outstanding results compared to other systems and human participants. Index Terms: machine learning, question answering, topicality. 1.