FORMAT-DIRECTED LIST PROCESSING IN LISP

1966 ◽  
Author(s):  
Daniel G. Bobrow ◽  
Warren Teitelman
Keyword(s):  
List Processing

The expressivity dimension of speech is the basis of the expression dimension. Evidence from behavioural and neuroimaging studies

2020 ◽  
Author(s):  
Isabelle Hesling
Keyword(s):  
Language Acquisition ◽  
Experimental Psychology ◽  
Right Hemisphere ◽  
Word List ◽  
Behavioral Experiments ◽  
Brain Areas ◽  
Language Communication ◽  
Neuroimaging Data ◽  
List Processing ◽  
The Right

The modalities of communication are the sum of the expression dimension (linguistics) and the expressivity dimension (prosody), both being equally important in language communication. The expressivity dimension which comes first in the act of speech, is the basis on which phonemes, syllables, words, grammar and morphosyntax, i.e., the expression dimension of speech is superimposed. We will review evidence (1) revealing the importance of prosody in language acquisition and (2) showing that prosody triggers the involvement of specific brain areas dedicated to sentences and word-list processing. To support the first point, we will not only rely on experimental psychology studies conducted in newborns and young children but also on neuroimaging studies that have helped to validate these behavioral experiments. Then, neuroimaging data on adults will allow for concluding that the expressivity dimension of speech modulates both the right hemisphere prosodic areas and the left hemisphere network in charge of the expression dimension

Using procedures in list processing

1977 ◽  
Author(s):  
V. J. Rayward-Smith
Keyword(s):  
List Processing

Using procedures in list processing

1977 ◽  
Vol 12 (6) ◽  
pp. 179-183 ◽  
Author(s):  
V. J. Rayward-Smith
Keyword(s):  
List Processing

scholarly journals Faster sequence alignment through GPU-accelerated restriction of the seed-and-extend search space

2014 ◽  
Author(s):  
Richard Wilton ◽  
Tamas Budavari ◽  
Ben Langmead ◽  
Sarah J Wheelan ◽  
Steven Salzberg ◽  
...  
Keyword(s):  
Sequence Alignment ◽  
High Throughput ◽  
Search Space ◽  
Computational Effort ◽  
Biological Sequences ◽  
Reduction Techniques ◽  
Software Implementations ◽  
List Processing ◽  
High Processing

Motivation: In computing pairwise alignments of biological sequences, software implementations employ a variety of heuristics that decrease the computational effort involved in computing potential alignments. A key element in achieving high processing throughput is to identify and prioritize potential alignments where high-scoring mappings can be expected. These tasks involve list-processing operations that can be efficiently performed on GPU hardware. Results: We implemented a read aligner called A21 that exploits GPU-based parallel sort and reduction techniques to restrict the number of locations where potential alignments may be found. When compared with other high-throughput aligners, this approach finds more high-scoring mappings without sacrificing speed or accuracy. A21 running on a single GPU is about 10 times faster than comparable CPU-based tools; it is also faster and more sensitive in comparison with other recent GPU-based aligners.

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