Bjerregaard Hedegaard (bladereason4)

Multiview Generalized Eigenvalue Proximal Support Vector Machine (MvGEPSVM) is an effective method for multiview data classification proposed recently. However, it ignores discriminations between different views and the agreement of the same view. Moreover, there is no robustness guarantee. In this paper, we propose an improved multiview GEPSVM (IMvGEPSVM) method, which adds a multi-view regularization that can connect different views of the same class and simultaneously considers the maximization of the samples from different classes in heterogeneous views for promoting discriminations. This makes the classification more effective. In addition, L1-norm rather than squared L2-norm is employed to calculate the distances from each of the sample points to the hyperplane so as to reduce the effect of outliers in the proposed model. To solve the resulting objective, an efficient iterative algorithm is presented. Theoretically, we conduct the proof of the algorithm's convergence. Experimental results show the effectiveness of the proposed method. Increasing phishing sites today have posed great threats due to their terribly imperceptible hazard. They expect users to mistake them as legitimate ones so as to steal user information and properties without notice. Solcitinib The conventional way to mitigate such threats is to set up blacklists. However, it cannot detect one-time Uniform Resource Locators (URL) that have not appeared in the list. As an improvement, deep learning methods are applied to increase detection accuracy and reduce the misjudgment ratio. However, some of them only focus on the characters in URLs but ignore the relationships between characters, which results in that the detection accuracy still needs to be improved. Considering the multi-head self-attention (MHSA) can learn the inner structures of URLs, in this paper, we propose CNN-MHSA, a Convolutional Neural Network (CNN) and the MHSA combined approach for highly-precise. To achieve this goal, CNN-MHSA first takes a URL string as the input data and feeds it into a mature CNN model so as to extract its features. In the meanwhile, MHSA is applied to exploit characters' relationships in the URL so as to calculate the corresponding weights for the CNN learned features. Finally, CNN-MHSA can produce highly-precise detection result for a URL object by integrating its features and their weights. The thorough experiments on a dataset collected in real environment demonstrate that our method achieves 99.84% accuracy, which outperforms the classical method CNN-LSTM and at least 6.25% higher than other similar methods on average. INTRODUCTION Inadequate correction of mechanical alignment may lead to failure of Total Ankle Replacements (TAR). The mechanical axis of the lower limb (MAL), the mechanical axis of the tibia (MAT) and the anatomical axis of the tibia (AAT) are three well described coronal plane measurements using plain radiography. The assumption is that the MAL, MAT and AAT are equivalent. The relationship between these axes can vary in the presence of proximal deformity. The purpose of this study was to assess the relationship between MAL, MAT and AAT in a cohort of patients considered for TAR. METHODS 75 consecutive standardised preoperative long leg radiographs of patients with end stage ankle osteoarthritis, between 2016 and 2017 at a specialist tertiary center for elective orthopedic surgery were analysed. Patients were split into 2 groups. The first group had a clinically and radiologically detectable deformity proximal to the ankle (such as previous tibial or femoral fracture, severe arthritis, or previous reconstructive surgery), whereas the second (normal) group did not. The MAL, MAT and AAT were measured and the difference between these values were calculated. RESULTS There were 54 patients in the normal group, and 21 patients in the deformity group. The mean difference between the MAL and AAT was 1.7 ± 1.3° (range, 0.1-5.4°). In the normal group,