This paper presents new single-layer, i.e., planar-embeddable, clock tree constructions with exact zero skew under either the linear or the Elmore delay model. Our method, called Planar-DME, consists of two parts. The rst algorithm, called Linear-Planar-DME, guarantees an optimal planar zero-skew clock tree (ZST) under the linear delay model. The second algorithm, called ElmorePlanar-DME, uses ...

The article presents the application of the decision tree classifier to the acute abdominal pain diagnosis. The recognition task model is based on a decision tree. In this model the decision tree structure is given by the experts. For the assumed structure of the decision tree the locally optimal strategy is considered. The problem discussed in the work shows a selection of different classifier...

Binary trees are essential structures in Computer Science. The leaf (leaves) of a binary tree is one of the most significant aspects of it. In this study, we prove that the order of a leaf (leaves) of a binary tree is the same in the main tree traversals; preorder, inorder, and postorder. Then, we prove that given the preorder and postorder traversals of a binary tree, the leaf (leaves) of a bi...

Locally linear embedding is a popular manifold learning algorithm for nonlinear dimensionality reduction. However, the success of LLE depends greatly on an input parameter neighborhood size, and it is still an open problem how to find the optimal value for it. This paper focuses on this parameter, proposes that it should be self-tuning according to local density not a uniform value for all the ...

capacity of garden productions in iran is such that is accounted as a country that produces thirteen garden products in the world but despite excellent condition in iran for producing garden products, natural disasters damage production of fruits in the country therefore farmers incur a loss. pistachio tree has been in danger of destruction and dryness. thus, in order to reduce loss incurred on...

We propose novel model transfer-learning methods that refine a decision forest model M learned within a “source” domain using a training set sampled from a “target” domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce ...