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.     

A formal model of composing components: the TLA+ approach

In this paper, a method for writing composable TLA⁺ specifications that conform to the formal model called Masaccio is introduced. Specifications are organized in TLA⁺ modules that correspond to Masaccio components by means of a trace-based semantics. Hierarchical TLA⁺ specifications are built from atomic component specifications by parallel and serial composition that can be arbitrary nested. While the rule of parallel composition is a variation of the classical joint-action composition, the authors do not know about a reuse method for the TLA⁺ that systematically employs the presented kind of a serial composition. By combining these two composition rules and assuming only the noninterleaving synchronous mode of an execution, the concurrent, sequential, and timed compositionality is achieved.     

Low-contrast lesion segmentation in advanced MRI experiments by time-domain Ricker-type wavelets and fuzzy 2-means

Automated suspicious region segmentation has become a crucial need for the experts dealing with numerous images containing contrast-based lesions in MRI. Not all solutions, however, are based on mathematical infrastructure or providing adequate flexibility. On the other hand, segmentation of low-contrast lesions is very challenging for researchers; therefore, advanced magnetic resonance imaging (MRI) experiments are not commonly used in researches. Given the need of repeatability and adaptability, we present an automated framework for intelligent segmentation of brain lesions by wavelet imaging and fuzzy 2-means. Besides the general use of the wavelets in image processing, which is edge detection; we employed the second-order Ricker-type wavelets as the core of our novel imaging framework for low-contrast lesion segmentation. We firstly introduced the mathematical basis of several Ricker wavelet functions, which are in symmetrical form satisfying finite-energy and admissibility conditions of mother wavelets. Afterwards, we investigated three types of Ricker wavelets to apply on our clinical dataset containing susceptibility-weighted (SW) and minimum intensity projection SW (mIP-SW) images with barely-visible lesions. Finally, we adjusted the system parameters of the wavelets for optimization and post-segmentation by fuzzy 2-means. According to the preliminary results of the clinical experiments we conducted, our framework provided 93.53% average dice score (DSC) for SWI by Ricker-3 and 92.56% for mIP-SWI by Ricker-2 wavelet, as the main performance criteria of segmentation. Despite the lack of SWI or mIP-SWI experiments in the public datasets, we tested our framework with BraTS 2012 training sets containing real images with visible lesions and achieved an average of 88.13% DSC with 11.66% standard deviation by re-optimized framework for whole lesion segmentation, which is one of the highest among other relevant researches. In detail, 87.52% DSC for LG datasets with 11.32% standard deviation; while 88.34% DSC for HG datasets with 11.77% standard deviation are calculated.

     

Progress in application of high temperature superconductor in tokamak magnets

It has long been known that high temperature superconductors (HTS) could have an important role to play in the future of tokamak fusion research. Here we report on first results of the use of HTS in a tokamak magnet and on the progress in design and construction of the first fully-HTS tokamak.     

Morphology,structure, and chemistry of nanoclusters in a mechanically alloyed nanostructured ferritic steel

Nanostructured ferritic steels have excellent elevated temperature strengths, creep resistances, and radiation tolerances due to the presence of a high density of Ti–Y–O-enriched nanoclusters. The compositions, morphologies, and structures of the smallest of these nanoclusters with maximum dimensions of ~2–4 nm were investigated in alloy 14YWT by high-resolution scanning transmission electron microscopy and atom probe tomography. Nanoclusters are found to be coherent with truncated rhombic dodecahedron morphologies defined by the {100} and {110} planes in the Fe matrix. Particles have compositions rich in Ti, O, Y, and Cr that are inconsistent with known oxide structures. The smallest nanoclusters appear to lack an identifiable crystal structure. Both nano-diffraction and focal series imaging through the sample thickness suggest that they are amorphous.