Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.     

Circulating Monocyte Subsets and Transcatheter Aortic Valve Replacement

Transcatheter aortic valve replacement (TAVR), as an alternative to open heart surgery, has revolutionized the treatment of severe aortic valve stenosis (AVS), the most common valvular disorder in the elderly. AVS is now considered a form of atherosclerosis and, like the latter, partly of inflammatory origin. Patients with high-grade AVS have a highly disturbed blood flow associated with high levels of shear stress. The immediate reopening of the valve during TAVR leads to a sudden restoration of a normal blood flow hemodynamic. Despite its good prognosis for patients, TAVR remains associated with bleeding or thrombotic postprocedural complications, involving mechanisms that are still poorly understood. Many studies report the close link between blood coagulation and inflammation, termed thromboinflammation, including monocytes as a major actor. The TAVR procedure represents a unique opportunity to study the influence of shear stress on human monocytes, key mediators of inflammation and hemostasis processes. The purpose of this study was to conduct a review of the literature to provide a comprehensive overview of the impact of TAVR on monocyte phenotype and subset repartition and the association of these parameters with the clinical outcomes of patients with severe AVS who underwent TAVR.     

Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers

Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in order to develop a mortality prediction model for hospitalised SARS-CoV-2 positive patients. A retrospective cohort study including data from suspected SARS-CoV-2 positive patients admitted to a large National Health Service (NHS) Foundation Trust hospital in the Yorkshire and Humber regions, United Kingdom, between 1 March 2020 and 30 August 2020. Hospitalised adult patients (aged ≥ 18 years) with at least one confirmed positive RT-PCR test for SARS-CoV-2 and blood tests of kidney biomarkers within 36 h of the RT-PCR test were included. The main outcome measure was 90-day in-hospital mortality in SARS-CoV-2 infected patients. The logistic regression and random forest (RF) models incorporated six predictors including three routine kidney function tests (sodium, urea; creatinine only in RF), along with age, sex, and ethnicity. The mortality prediction performance of the logistic regression model achieved an area under receiver operating characteristic (AUROC) curve of 0.772 in the test dataset (95% CI: 0.694–0.823), while the RF model attained the AUROC of 0.820 in the same test cohort (95% CI: 0.740–0.870). The resulting validated prediction model is the first to focus on kidney biomarkers specifically on in-hospital mortality over a 90-day period.     

Shape Decomposition using Modal Analysis

We introduce a novel algorithm that decomposes a deformable shape into meaningful parts requiring only a single input pose. Using modal analysis, we are able to identify parts of the shape that tend to move rigidly. We define a deformation energy on the shape, enabling modal analysis to find the typical deformations of the shape. We then find a decomposition of the shape such that the typical deformations can be well approximated with deformation fields that are rigid in each part of the decomposition. We optimize for the best decomposition, which captures how the shape deforms. A hierarchical refinement scheme makes it possible to compute more detailed decompositions for some parts of the shape.
Although our algorithm does not require user intervention, it is possible to control the process by directly changing the deformation energy, or interactively refining the decomposition as necessary. Due to the construction of the energy function and the properties of modal analysis, the computed decompositions are robust to changes in pose as well as meshing, noise, and even imperfections such as small holes in the surface.     

Influence of FeO/SiO2 and CaO/SiO2 Ratios in Iron‐Saturated ZnO‐Rich Fayalite Slags on the Corrosion of MgO

The corrosion behavior of MgO in iron‐saturated ZnO‐rich fayalite (ZFS) slags having various FeO/SiO₂ ratio and CaO/SiO₂ ratio was investigated using MgO crucible tests for 12 h at 1200°C. The FeO/SiO₂ and CaO/SiO₂ ratios in the ZFS slags were varied from 1.0 to 2.2, and from 0.04 to 0.32, respectively. In all of the tests, it was observed that MgO dissolves into ZFS slags and that (Zn,Fe,Mg)₂SiO₄ olivine and (Zn,Fe,Mg)O solid solution are formed at the crucible/slag interface. The MgO dissolution decreased with the FeO/SiO₂ ratio up to a value of 1.7 and then slightly increased, whereas it continuously increased with the CaO/SiO₂ ratio. There is no obvious relationship between the amount of olivine and the FeO/SiO₂ ratio or CaO/SiO₂ ratio. In comparison, the formation of (Zn,Fe,Mg)O solid solution is enhanced by increasing the FeO/SiO₂ ratio or CaO/SiO₂ ratio in ZFS slags. The results suggest that MgO corrosion is the lowest for FeO/SiO₂ and CaO/SiO₂ ratios around 1.7 and 0, respectively.     

Multi-level Modeling of Silica–Template Interactions During Initial Stages of Zeolite Synthesis

Zeolite synthesis is driven by structure-directing agents, such as tetrapropyl ammonium ions (TPA⁺) for Silicalite-1 and ZSM-5. However, the guiding role of these organic templates in the complex assembly to highly ordered frameworks remains unclear, limiting the prospects for advanced material synthesis. In this work, both static ab initio and dynamic classical modeling techniques are employed to provide insight into the interactions between TPA⁺ and Silicalite-1 precursors. We find that as soon as the typical straight 10-ring channel of Silicalite-1 or ZSM-5 is formed from smaller oligomers, the TPA⁺ template is partially squeezed out of the resulting cavity. Partial retention of the template in the cavity is, however, indispensable to prevent collapse of the channel and subsequent hydrolysis.

Use of locality sensitive hashing (LSH) algorithm to match Web of Science and Scopus

Scientometrics - A novel hashing algorithm is applied to match two prominent and important bibliographic databases at the paper level. In the literature, such tasks have been studied and conducted...