The Real Time Specification for Java (RTSJ) allows a program to create real-time threads with hard real-time constraints. Real-time threads use region-based memory management to avoid unbounded pauses caused by interference from the garbage collector. The RTSJ uses runtime checks to ensure that deleting a region does not create dangling references and that real-time threads do not access references to objects allocated in the garbage-collected heap. This paper presents a static type system that guarantees that these runtime checks will never fail for well-typed programs. Our type system therefore 1) provides an important safety guarantee for real-time programs and 2) makes it possible to eliminate the runtime checks and their associated overhead.Our system also makes several contributions over previous work on region types. For object-oriented programs, it combines the benefits of region types and ownership types in a unified type system framework. For multithreaded programs, it allows long-lived threads to share objects without using the heap and without memory leaks. For real-time programs, it ensures that real-time threads do not interfere with the garbage collector. Our experience indicates that our type system is sufficiently expressive and requires little programming overhead, and that eliminating the RTSJ runtime checks using a static type system can significantly decrease the execution time of real-time programs.

This paper describes a fast and fully automated dictionary based approach to gene annotation and exon prediction. Two dictionaries are constructed, one from the nonredundant protein OWL database and the other from the dbEST database. These dictionaries are used to obtain O(1) time lookups of tuples in the dictionaries (4 tuples for the OWL database and 11 tuples for the \ndbEST database). These tuples can be used to rapidly find the longest matches at every position in an input sequence to the database sequences. Such matches provide very useful information pertaining to locating common segments between exons, alternative splice sites, and frequency data of long tuples for statistical purposes. These dictionaries also provide the basis for both homology determination, and statistical approaches to exon prediction. For instance, using the OWL protein database on a benchmark test set of 130 genes, and after removing sequences from the database with exact amino acid homology to genes in our test set, we find 88% of coding nucleotides, and 99% of our predictions of coding nucleotides are correct. Also, 81% of coding exons are predicted exactly, while 82% of our predictions of exons agree exactly with the published annotation of their genes.