In this paper we propose a new method for parallel execution of Prolog programs and present its implementation on a distributed memory parallel computer, Fujitsu AP1000. In our method a number of processes (named Prolog engines) explore different branches of a search tree (named tasks) in parallel, which is the same as OR-parallelism. Unlike OR-parallelism, the mapping between Prolog engines and tasks is statically determined like data parallelism. Each Prolog engine can decide which task is executed by the engine without communicating with the other engines. In many search problems, however, such static task mapping may cause imbalance on the processing time of each engine since the computational costs to explore branches may vary substantially. To cope with this issue, we devise a method to adjust the task imbalance by periodical exchanging how many tasks were processed for each engine. Also for reducing communication overhead in load balancing, we limit the scope of engines that exchange the load information each other. The effectiveness of our method is evaluated by measuring execution times for N Queens and the Traveling Salesman Problem on the AP1000. Using 512 processors, we obtained 355-fold speedup for N Queens and 343-fold speedup on the Traveling Salesman Problem.

This paper describes that a neural network, which consists of neurons with piecewise–linear sigmoid characteristics, is able to approximate any piecewise–linear map with origin symmetry. The neural network can generate "deterministic diffusion" originating from its diffusive trajectory.

In this paper, we propose a method for detecting conserved domains from a set of amino acid sequences that belong to a protein family. This method detects the domains as follows: first, generate fixed-length subsequences from the sequences; second, construct a weighted graph that connects any two of the subsequences (vertices) having higher similarity than a pre-defined threshold; third, search for the maximum-density subgraph for each connected component of the graph; finally, explore conserved domains in the sequences by combining the results of the previous step. From the performance results obtained by applying the method to several protein families that have complex conserved domains, we found that our method was able to detect those domains even though some domains were weakly conserved.

We derive the eigenvalue constraint for a neural network with three degrees of freedom. The result implies that the neural network needs a neuron with variable output function to generate chaos. It is also shown that the neuron with the special characteristics can be constructed by ordinary neurons.

In this paper, we propose a method for querying heterogeneous molecular biology databases. Since molecular biology data are distributed into multiple databases that represent different biological domains, it is highly desirable to integrate data together with the correlations between the domains. However, since the total amount of such databases is very large and the data contained are frequently updated, it is difficult to maintain the integration of the entire contents of the databases. Thus, we propose a method for dynamic integration based on user demand, which is expressed with an OQL-based query language. By restricting search space according to user demand, the cost of integration can be reduced considerably. Multiple databases also exhibit much heterogeneity, such as semantic mismatching between the database schemas. For example, many databases employ their own independent terminology. For this reason, it is usually required that the task for integrating data based on a user demand should be carried out transitively; first search each database for data that satisfy the demand, then repeatedly retrieve other data that match the previously found data across every database. To cope with this issue, we introduce two types of agents; a database agent and a user agent, which reside at each database and at a user, respectively. The integration task is performed by the agents; user agents generate demands for retrieving data based on the previous search results by database agents, and database agents search their databases for data that satisfy the demands received from the user agents. We have developed a prototype system on a network of workstations. The system integrates four databases; GenBank (a DNA nucleotide database), SWISS-PROT, PIR (protein amino-acid sequence databases), and PDB (a protein three-dimensional structure database). Although the sizes of GenBank and PDB are each over one billion bytes, the system achieved good performance in searching such very large heterogeneous databases.