Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach

Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space embedded in a higher dimensional space. However, since these manifolds belong to non-Euclidean topological spaces, exploiting their structures is computationally expensive, especially when one considers the clustering analysis of massive amounts of data. To this end, we propose an efficient framework to address the clustering problem on Riemannian manifolds. This framework implements random projections for manifold points via kernel space, which can preserve the geometric structure of the original space, but is computationally efficient. Here, we introduce three methods that follow our framework. We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds. Experimental results demonstrate that our framework maintains the performance of the clustering whilst massively reducing computational complexity by over two orders of magnitude in some cases

Tacrolimus phospholipid based nanomicelles as a potential local delivery system for corneal neovascularization therapy

Introduction: Tacrolimus, an immunosuppressive agent, has been shown to be an effective treatment against corneal neovascularization (CNV). However, the poor solubility of this compound restricts its clinical application. The goal of this study was to incorporate tacrolimus into phospholipid-bile salt mixed micelles. Methods and Results: Tacrolimus loaded phospholipid-bile salt mixed micelles were prepared, employing three different methods of direct dispersion, thin film hydration, and remote film loading, and the effects of various formulation parameters (type of dispersion medium, phospholipid to bile salt molar ratio, lipid-to-drug (L/D) molar ratio, time of probe sonication, and type of bile salt) on the physicochemical characteristics of the mixed micelles were assessed. Remote film loading method indicated higher efficacy for drug entrapment in comparison to the other methods. Encapsulation of tacrolimus within the micelles increased remarkably by the use of sodium taurocholate (NaTC) as bile salt, higher phospholipid percentage, and increasing the total lipid level. Atomic force microscopy (AFM) studies confirmed the size and size distribution of the mixed micelles and their spherical morphology. It was observed that release of tacrolimus from the micelles was in a controlled manner, without an initial burst. Conclusions: By adjusting process and formulation factors, phospholipid-bile salt mixed micelles with high entrapment efficiency of (99.5 %) and controlled release behavior were achieved, which possess great potential to be valuable carriers for ocular delivery of tacrolimus for the treatment of CNV.                                                                                                                                       &nbsp

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP

Multi-shot person re-identification via relational Stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features

An investigation of the effect of the components of the learning organization on knowledge application at Isfahan University of Medical Sciences

Introduction: The need to create learning organizations results from the fact that with the increasing complexity and speed of developments, the uncertainty increased in organization environments. Learning is the most important way to improve long-term performance. The main objective of this study was to determine the effectiveness of learning organization initiatives on application of knowledge in Isfahan University of Medical Sciences. Methodology: This study is descriptive and survey to collect information on the literature the library method and to analyze and test the hypotheses; a questionnaire was used, the validity and reliability (89% of the learning organization and 86% knowledge) of which is verified by a number of teachers and experts in the field. The study population consisted of 623 university faculty members, using a simple random sampling, 155 questionnaires were distributed among them. Data analysis was performed by SPSS software, using the mean, standard deviation, one sample t-test, correlation, and multiple regression. Results: There is a relationship between the components of the learning and application of knowledge in university under study. Among the components of a learning organization, the best predictor of knowledge application, according to the results of multiple regression, is a teamwork with an average of 2.65 and a standard deviation of 0.754 and led with an average of 2.61 and standard deviation of 0.519. Based on the coefficient of determination, teamwork alone explained 20% of the variance of application of knowledge and with the introduction of the leadership variable rate of variance explained increased to 25%. Conclusions: The results support the idea that all the components of a learning organization have a positive impact on the university's application of knowledge, and a significant relationship also exists between them. Further, greater emphasis should be placed on the strengths of the relationship, such as great support of faculty members of missions and goals of university, as the average response from all relations is more than any relationship and also have a huge impact on the application of knowledge. The results showed that the lowest correlation belongs to the shared vision of senior management, faculty, and staff to do what needs to be done