We demonstrate the fabrication of reproducible long-range ferromagnetism (FM) in highly crystalline Gd_xZn_1−xO thin films by controlling the defects. Films are grown on lattice-matched substrates by pulsed laser deposition at low oxygen pressures (≤25 mTorr) and low Gd concentrations (x ≤ 0.009). These films feature strong FM (10 μ_B per Gd atom) at room temperature. While films deposited at higher oxygen pressure do not exhibit FM, FM is recovered by post-annealing these films under vacuum. These findings reveal the contribution of oxygen deficiency defects to the long-range FM. We demonstrate the possible FM mechanisms, which are confirmed by density functional theory study, and show that Gd dopants are essential for establishing FM that is induced by intrinsic defects in these films.

This paper is devoted to the study of bi-criteria optimization problems for decision trees. We consider different cost functions such as depth, average depth, and number of nodes. We design algorithms that allow us to construct the set of Pareto optimal points (POPs) for a given decision table and the corresponding bi-criteria optimization problem. These algorithms are suitable for investigation of medium-sized decision tables. We discuss three examples of applications of the created tools: the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees, and comparison of different greedy algorithms for decision tree construction as single- and bi-criteria optimization algorithms

This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this system’s work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction.

In this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses of values of all attributes. Such decision trees are similar to those studied in exact learning, where membership and equivalence queries are allowed. We present greedy algorithm based on entropy for the construction of the above decision trees and discuss the results of computer experiments on various data sets and randomly generated Boolean functions.