Optimal randomized RANSAC

A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-specified probability. The solution is found in time that is (i) close to the shortest possible and (ii) superior to any deterministic verification strategy. A provably fastest model verification strategy is designed for the (theoretical) situation when the contamination of data by outliers is known. In this case, the algorithm is the fastest possible (on average) of all randomized \\RANSAC algorithms guaranteeing a confidence in the solution. The derivation of the optimality property is based on Wald's theory of sequential decision making, in particular a modified sequential probability ratio test (SPRT). Next, the R-RANSAC with SPRT algorithm is introduced. The algorithm removes the requirement for a priori knowledge of the fraction of outliers and estimates the quantity online. We show experimentally that on standard test data the method has performance close to the theoretically optimal and is 2 to 10 times faster than standard RANSAC and is up to 4 times faster than previously published methods.     

Accelerating scientific computations with mixed precision algorithms

On modern architectures, the performance of 32-bit operations is often at least twice as fast as the performance of 64-bit operations. By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many dense and sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution. The approach presented here can apply not only to conventional processors but also to other technologies such as Field Programmable Gate Arrays (FPGA), Graphical Processing Units (GPU), and the STI Cell BE processor. Results on modern processor architectures and the STI Cell BE are presented.

Program summary

Program title: ITER-REFCatalogue identifier: AECO_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AECO_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 7211No. of bytes in distributed program, including test data, etc.: 41 862Distribution format: tar.gzProgramming language: FORTRAN 77Computer: desktop, serverOperating system: Unix/LinuxRAM: 512 MbytesClassification: 4.8External routines: BLAS (optional)Nature of problem: On modern architectures, the performance of 32-bit operations is often at least twice as fast as the performance of 64-bit operations. By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many dense and sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution.Solution method: Mixed precision algorithms stem from the observation that, in many cases, a single precision solution of a problem can be refined to the point where double precision accuracy is achieved. A common approach to the solution of linear systems, either dense or sparse, is to perform the LU factorization of the coefficient matrix using Gaussian elimination. First, the coefficient matrix A is factored into the product of a lower triangular matrix L and an upper triangular matrix U. Partial row pivoting is in general used to improve numerical stability resulting in a factorization PA=LU, where P is a permutation matrix. The solution for the system is achieved by first solving Ly=Pb (forward substitution) and then solving Ux=y (backward substitution). Due to round-off errors, the computed solution, x, carries a numerical error magnified by the condition number of the coefficient matrix A. In order to improve the computed solution, an iterative process can be applied, which produces a correction to the computed solution at each iteration, which then yields the method that is commonly known as the iterative refinement algorithm. Provided that the system is not too ill-conditioned, the algorithm produces a solution correct to the working precision.Running time: seconds/minutes     

Study of structure and properties of ZnO–Bi<Subscript>2</Subscript>O<Subscript>3</Subscript>–P<Subscript>2</Subscript>O<Subscript>5</Subscript> glasses

Glasses of the ternary system ZnO–Bi₂O₃–P₂O₅ were prepared and studied in two compositional series 50ZnO–xBi₂O₃–(50 − x)P₂O₅ and (50 − y)ZnO–yBi₂O₃–50P₂O₅. Two distinct glass-forming regions were found in the 50ZnO–xBi₂O₃–(50 − x)P₂O₅ glass series with x = 0–10 and 20–35 mol.% Bi₂O₃. All prepared Bi₂O₃-containing glasses reveal a high chemical durability. Small additions of Bi₂O₃ (∼5 mol.%) improve thermal stability of glasses. All glasses crystallize on heating within the temperature range of 505–583 °C. Structural studies by Raman and ³¹P MAS NMR spectroscopies showed the rapid depolymerisation of phosphate chains within the first region with x = 0–15 and the presence of isolated Q⁰ phosphate units within the second region with x = 20–35. Raman studies showed that bismuth is incorporated in the glass structure in BiO₆ units and their vibrational bands were observed within the spectral region of 350–700 cm⁻¹. The evolution of properties and the spectroscopic data are both in accordance with a network former effect of Bi₂O₃.     

Synthesis,In Vitro,and Computational Studies of PTP1B Phosphatase Inhibitors Based on Oxovanadium(IV) and Dioxovanadium(V) Complexes

One of the main goals of recent bioinorganic chemistry studies has been to design and synthesize novel substances to treat human diseases. The promising compounds are metal-based and metal ion binding components such as vanadium-based compounds. The potential anticancer action of vanadium-based compounds is one of area of investigation in this field. In this study, we present five oxovanadium(IV) and dioxovanadium(V) complexes as potential PTP1B inhibitors with anticancer activity against the MCF-7 breast cancer cell line, the triple negative MDA-MB-231 breast cancer cell line, and the human keratinocyte HaCaT cell line. We observed that all tested compounds were effective inhibitors of PTP1B, which correlates with anticancer activity. [VO(dipic)(dmbipy)]·2 H₂O (Compound 4) and [VOO(dipic)](2-phepyH)·H₂O (Compound 5) possessed the greatest inhibitory effect, with IC₅₀ 185.4 ± 9.8 and 167.2 ± 8.0 nM, respectively. To obtain a better understanding of the relationship between the structure of the examined compounds and their activity, we performed a computer simulation of their binding inside the active site of PTP1B. We observed a stronger binding of complexes containing dipicolinic acid with PTP1B. Based on our simulations, we suggested that the studied complexes exert their activity by stabilizing the WPD-loop in an open position and limiting access to the P-loop.     

Hematites Precipitated in Alkaline Precursors: Comparison of Structural and Textural Properties for Methane Oxidation

Hematite (α-Fe₂O₃) catalysts prepared using the precipitation methods was found to be highly effective, and therefore, it was studied with methane (CH₄), showing an excellent stable performance below 500 °C. This study investigates hematite nanoparticles (NPs) obtained by precipitation in water from the precursor of ferric chloride hexahydrate using precipitating agents NaOH or NH₄OH at maintained pH 11 and calcined up to 500 °C for the catalytic oxidation of low concentrations of CH₄ (5% by volume in air) at 500 °C to compare their structural state in a CH₄ reducing environment. The conversion (%) of CH₄ values decreasing with time was discussed according to the course of different transformation of goethite and hydrohematites NPs precursors to magnetite and the structural state of the calcined hydrohematites. The phase composition, the size and morphology of nanocrystallites, thermal transformation of precipitates and the specific surface area of the NPs were characterized in detail by X-ray powder diffraction, transmission electron microscopy, infrared spectroscopy, thermal TG/DTA analysis and nitrogen physisorption measurements. The results support the finding that after goethite dehydration, transformation to hydrohematite due to structurally incorporated water and vacancies is different from hydrohematite α-Fe₂O₃. The surface area SBET of Fe₂O₃_NH-70 precipitate composed of protohematite was larger by about 53 m²/g in comparison with Fe₂O₃_Na-70 precipitate composed of goethite. The oxidation of methane was positively influenced by the hydrohematites of the smaller particle size and the largest lattice volume containing structurally incorporated water and vacancies.