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.

     

Durability of fire retardant treated wood products at humid and exterior conditions review of literature

Fire retardants may considerably improve the fire properties of wood products, but the durability, e.g. in exterior applications, has not been addressed fully. This paper reviews the existing knowledge and experience mainly from the USA with the aim of supporting further development in Europe. The review is concentrated on pressure impregnated fire retardant treated wood products which have the best opportunities for increased durability. Copyright © 2001 by John Wiley & Sons, Ltd.     

The Role of Exosomes in Cancer Progression

Early detection, characterization and monitoring of cancer are possible by using extracellular vesicles (EVs) isolated from non-invasively obtained liquid biopsy samples. They play a role in intercellular communication contributing to cell growth, differentiation and survival, thereby affecting the formation of tumor microenvironments and causing metastases. EVs were discovered more than seventy years ago. They have been tested recently as tools of drug delivery to treat cancer. Here we give a brief review on extracellular vesicles, exosomes, microvesicles and apoptotic bodies. Exosomes play an important role by carrying extracellular nucleic acids (DNA, RNA) in cell-to-cell communication causing tumor and metastasis development. We discuss the role of extracellular vesicles in the pathogenesis of cancer and their practical application in the early diagnosis, follow up, and next-generation treatment of cancer patients.