ABSTRACT
We report the growth of self-assembled Bi2Se3 quantum dots (QDs) by molecular beam epitaxy on GaAs substrates using the droplet epitaxy technique. The QD formation occurs after anneal of Bismuth droplets under Selenium flux. Characterization by atomic force microscopy, scanning electron microscopy, X-ray diffraction, high-resolution transmission electron microscopy and X-ray reflectance spectroscopy is presented. Raman spectra confirm the QD quality. The quantum dots are crystalline, with hexagonal shape, and have average dimensions of 12-nm height (12 quintuple layers) and 46-nm width, and a density of 8.5 × 109 cm-2. This droplet growth technique provides a means to produce topological insulator QDs in a reproducible and controllable way, providing convenient access to a promising quantum material with singular spin properties.
ABSTRACT
We propose an improved random forest classifier that performs classification with minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in classification accuracy. Our algorithm converges with a reduced but important set of features. We prove that further addition of trees or further reduction of features does not improve classification performance. The efficacy of the proposed approach is demonstrated through experiments on benchmark datasets. We further use the proposed classifier to detect mitotic nuclei in the histopathological datasets of breast tissues. We also apply our method on the industrial dataset of dual phase steel microstructures to classify different phases. Results of our method on different datasets show significant reduction in average classification error compared to a number of competing methods.
