Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm.

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the "STrutred Analysis of the REtina (STARE) Database" accessed on Oct. 2020 (https://cecas.clemson.edu/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm.

Learning Sample Specific Weights for Late Fusion.

Late fusion is one of the most effective approaches to enhance recognition accuracy through combining prediction scores of multiple classifiers, each of which is trained by a specific feature or model. The existing methods generally use a fixed fusion weight for one classifier over all samples, and ignore the fact that each classifier may perform better or worse for different subsets of samples. In order to address this issue, we propose a novel sample specific late fusion (SSLF) method. Specifically, we cast late fusion into an information propagation process that diffuses the fusion weights of labeled samples to the individual unlabeled samples, and enforce positive samples to have higher fusion scores than negative samples. Upon this process, the optimal fusion weight for each sample is identified, while positive samples are pushed toward the top at the fusion score rank list to achieve better accuracy. In this paper, two SSLF methods are presented. The first method is ranking SSLF (R-SSLF), which is based on graph Laplacian with RankSVM style constraints. We formulate and solve the problem with a fast gradient projection algorithm; the second method is infinite push SSLF (I-SSLF), which combines graph Laplacian with infinite push constraints. I-SSLF is a l∞ norm constrained optimization problem and can be solved by an efficient alternating direction method of multipliers method. Extensive experiments on both large-scale image and video data sets demonstrate the effectiveness of our methods. In addition, in order to make our method scalable to support large data sets, the AnchorGraph model is employed to propagate information on a subset of samples (anchor points) and then reconstruct the entire graph to get the weights of all samples. To the best of our knowledge, this is the first method that supports learning of sample specific fusion weights for late fusion.

Formation of [Ni(III)(κ(1)-S2CH)(P(o-C6H3-3-SiMe3-2-S)3)]- via CS2 insertion into nickel(III) hydride containing [Ni(III)(H)(P(o-C6H3-3-SiMe3-2-S)3)]-.

Insertion of CS2 into the thermally unstable nickel(III) hydride [PPN][Ni(H)(P(o-C6H3-3-SiMe3-2-S)3)] (1), freshly prepared from the reaction of [PPN][Ni(OC6H5)P(C6H3-3-SiMe3-2-S)3] and 4,4,5,5-tetramethyl-1,3,2-dioxaborolane (HBpin; pin = OCMe2CMe2O) in tetrahydrofuran at -80 °C via a metathesis reaction, readily affords [PPN][Ni(III)(κ(1)-S2CH)(P(o-C6H3-3-SiMe3-2-S)3)] (2) featuring a κ(1)-S2CH moiety.