An Evaluation of Deep Learning-Based Object Identification

Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study

Transport coefficients of hot and dense hadron gas in a magnetic field: a relaxation time approach

We estimate various transport coefficients of hot and dense hadronic matter in the presence of magnetic field. The estimation is done through solutions of the relativistic Boltzmann transport equation in the relaxation time approximation.We have investigated the temperature and the baryon chemical potential dependence of these transport coefficients. Explicit calculations are done for the hadronic matter in the ambit of hadron resonance gas model. We estimate thermal conductivity, electrical conductivity and the shear viscosity of hadronic matter in the presence of a uniform magnetic field. Magnetic field, in general, makes the transport coefficients anisotropic. It is also observed that all the transport coefficients perpendicular to the magnetic field are smaller compared to their isotropic counterpart.Comment: 22 pages, 11 figures. arXiv admin note: text overlap with arXiv:1903.0393

Bioinformatics tools for development of fast and cost effective simple sequence repeat (SSR), and single nucleotide polymorphisms (SNP) markers from expressed sequence tags (ESTs)

The development of current molecular biology techniques has led to the generation of huge amount of gene sequence information under the expressed sequence tag (EST) sequencing projects on a large number of plant species. This has opened a new era in crop molecular breeding with identification and/or development of a new class of useful DNA markers called genic molecular markers (GMMs). These markers represent the functional component of the genome in contrast to all other random DNA markers (RMMs). Many recent studies have demonstrated that GMMs may be superior to RMMs for use in the marker assisted selection, comparative mapping and exploration of functional genetic diversity in the germplasms adapted to different environment. Therefore, identification of DNA sequences which can be used as markers remains fundamental to the development of GMMs. Amongst others; bioinformatics approaches are very useful for development of molecular markers, making their development much faster and cheaper. Already, a number of computer programs have been implemented that aim at identifying molecular markers from sequence data. A revision of current bioinformatics tools for development of genic molecular markers is, therefore, crucial in this phase. This mini-review mainly provides an overview of different bioinformatics tools available and its use in marker development with particular reference to SNP and SSR markers.Keywords: Genic molecular marker, simple sequence repeat (SSR), and single nucleotide polymorphisms (SNP) markers from expressed sequence tags (ESTs).African Journal of Biotechnology Vol. 12(30), pp. 4713-472

Understanding policy intent and misconfigurations from implementations: consistency and convergence

Abstract. We study the problem of inferring policy intent to identify misconfigurations in access control implementations. This is in contrast to traditional role-mining techniques, which focus on creating better abstractions for access control management. We show how raw metadata can be summarized effectively, by grouping together users with similar permissions over shared resources. Using these summary statements, we apply statistical techniques to detect outliers, which we classify as security and accessibility misconfigurations. Specifically, we show how our techniques for mining policy intent are robust, and have strong consistency and convergence guarantees