We introduce a concept of flexi-depth iterations while depth migrating data in parallel imaging space, using optimized traveltime table computations.
The final result is further improved by several iterations between depth estimation and shape reconstruction.
The algorithm starts with the initial atom identifier of the center atom and grows a circular substructure around this atom throughout a defined number of iterations (search depth).
The recovered shape will be further improved by several iterations between depth estimation and shape reconstruction, similar to the Expectation Maximization (EM) approach.
For UNIX: Install PHP (if not already installed) from http://php.net/downloads.php; provide arguments -s: number of random reads, -i: number of iterations, -t: depth of taxonomy tree to classify the reads into (default setting is 3), -d: metagenome file name, -p: CensuScope package path.
The number of trees (i.e., the number of boosting iterations) and interaction depth (i.e., the depth of trees) are the two parameters tuned.
aiT also takes into account user annotations such as upper bounds on loop iteration counts, recursion depth, targets of indirect function calls, etc.
The evidence-based approach of this study reveals the applicability of human behavior simulation for the students' iteration and in-depth learning in the search for an optimal match between built environments and human activities in architectural design.
This concept provides flexibility to the algorithm by migrating data in a number of depth iterations, which depends upon the available node memory and the size of data to be migrated during runtime.
Now Algorithm 1 will count the total number of bonds and continue to the next iteration by reducing the depth by 1.
Baker performs this deduction iteratively, where in each iteration, the tool performs a depth-first traversal of the AST and examines all nodes of interest: nodes involved in declarations, invocations, and assignments.
