MOTIVATION: Profile-based protein homology detection algorithms are valuable tools in genome annotation and protein classification. By utilizing information present in the sequences of homologous proteins, profile-based methods are often able to detect extremely weak relationships between protein sequences, as evidenced by the large-scale benchmarking experiments such as CASP and LiveBench. RESULTS: We study the relationship between the sensitivity of a profile-profile method and the size of the sequence profile, which is defined as the average number of different residue types observed at the profile's positions. We also demonstrate that improvements in the sensitivity of a profile-profile method can be made by incorporating a profile-dependent scoring scheme, such as position-specific background frequencies. The techniques presented in this article are implemented in an alignment algorithm UNI-FOLD. When tested against other well-established methods for fold recognition, UNI-FOLD shows increased sensitivity and specificity in detecting remote relationships between protein sequences. AVAILABILITY: UNI-FOLD web server can be accessed at http://blackhawk.cs.uni.edu

Panelists will describe their schools' approaches to outcomes assessment for computer science students. Both successful and failed approaches will be discussed. Approaches to assessment start with the identification of program goals and selection of appropriate measures. Measurable results include student performance on standardized and locally developed tests, placement statistics, alumni interviews, employer perceptions, and other skills assessment. Pitfalls will be discussed. Results of outcomes assessment at each institution will be presented, including the management of collected data, interpretation of results, and integration of the results into the curriculum development process.

New approaches and techniques for teaching the undergraduate level course in networks and data communications will be discussed. Many of these ideas were presented at the 1998 Workshop on Networking sponsored by NSF and Michigan State University's Department of Computer Science and Engineering.

A performance evaluation metric called CMP scalability is introduced This metric is useful in analyzing performance of a parallel algorithm as the number of processors grows but the memory size per processor is fixed. The CMP scalability metric is derived for the parallel Matrix Multiplication (MM). Gauss Jordan Elimination (GJE), and Fast Fourier Transform (FFT). These examples show that the CMP scalability metric and the isoefficiency metric really address different aspects of scalability and could lead to very different conclusions. According to the CMP scalability analysis FFT is more scalable than GJE. Interestingly, conclusions drawn from the isoefficiency analysis are quite the opposite. The CMP scalability metric, and so also the isoefficiency metric, is a function indicating the asymptotic behavior as the number of processors becomes large. However, the CMP analysis can also help to analyze the performance of an algorithm on a given architecture with a limited number of processors. We present such an analysis of the three algorithms on a 16K processor MasPar MP-1 machine.