Abstract. Outlier detection finds many applications, especially in domains that have scope for abnormal behavior. In this paper, we present a new technique for detecting distance-based outliers, aimed at reducing execution time associated with the detection process. Our approach operates in two phases and employs three pruning rules. In the first phase, we partition the data into clusters, and make an early estimate on the lower bound of outlier scores. Based on this lower bound, the second phase then processes relevant clusters using the traditional block nested-loop algorithm. Here two efficient pruning rules are utilized to quickly discard more non-outliers and reduce the search space. Detailed analysis of our approach shows that the additional overhead of the first phase is offset by the reduction in cost of the second phase. We also demonstrate the superiority of our approach over existing distance-based outlier detection methods by extensive empirical studies on real datasets.
Ge0.07GaN films were successfully made on Si (100), SiO2/Si (100) substrates by a radio frequency reactive sputtering technique at various deposition conditions listed as a range of 100–400 °C and 90–150 W with a single ceramic target containing 7 at % dopant Ge. The results showed that different RF sputtering power and heating temperature conditions affected the structural, electrical and optical properties of the sputtered Ge0.07GaN films. The as-deposited Ge0.07GaN films had an structural polycrystalline. The GeGaN films had a distorted structure under different growth conditions. The deposited-150 W Ge0.07GaN film exhibited the lowest photoenergy of 2.96 eV, the highest electron concentration of 5.50 × 1019 cm−3, a carrier conductivity of 35.2 S∙cm−1 and mobility of 4 cm2·V−1∙s−1.
Facial anthropometrics are measurements of human faces and are important figures that are used in many different fields, such as cosmetic surgery, protective gear design, reconstruction, etc. Therefore, the first procedure is to extract facial landmarks, then measurements are carried out by professional devices or based on experience. The aim of this review is to provide an update and review of 3D facial measurements, facial landmarks, and nasal reconstruction literature. The novel methods to detect facial landmarks including non-deep and deep learning are also introduced in this paper. Moreover, the nose is the most attractive part of the face, so nasal reconstruction or rhinoplasty is a matter of concern, and this is a significant challenge. The documents on the use of 3D printing technology as an aid in clinical diagnosis and during rhinoplasty surgery are also surveyed. Although scientific technology development with many algorithms for facial landmarks extraction have been proposed, their application in the medical field is still scarce. Connectivity between studies in different fields is a major challenge today; it opens up opportunities for the development of technology in healthcare. This review consists of the recent literature on 3D measurements, identification of landmarks, particularly in the medical field, and finally, nasal reconstruction technology. It is a helpful reference for researchers in these fields.
Dictionary learning (DL) for sparse coding has been widely applied in the field of computer vision. Many DL approaches have been developed recently to solve pattern classification problems and have achieved promising performance. In this paper, to improve the discriminability of the popular dictionary pair learning (DPL) algorithm, we propose a new method called discriminative dictionary pair learning (DDPL) for image classification. To achieve the goal of signal representation and discrimination, we impose the incoherence constraints on the synthesis dictionary and the low-rank regularization on the analysis dictionary. The DDPL method ensures that the learned dictionary has the powerful discriminative ability and the signals are more separable after coding. We evaluate the proposed method on benchmark image databases in comparison with existing DL methods. The experimental results demonstrate that our method outperforms many recently proposed dictionary learning approaches.
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