Masked face recognition using deep learning: a review

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR

Editorial Image Retrieval Using Handcrafted and CNN Features

Textual keywords have been used in the early stages for image retrieval systems. Due to the huge increase of image content, an image is efficiently used instead according to the time computation. Deciding powerful feature representations are the important factors for the retrieval performance of a content-based image retrieval (CBIR) system. In this work, we present a combined feature representation based on handcrafted and deep approaches, to categorize editorial images into six classes (athletics, football, indoor, outdoor, portrait, ski). The experimental results show the superior performance of the combined features among different editorial classes

Study of pallial neurogenesis in shark embryos and the evolutionary origin of the subventricular zone

The dorsal part of the developing telencephalon is one of the brain areas that has suffered most drastic changes throughout vertebrate evolution. Its evolutionary increase in complexity was thought to be partly achieved by the appearance of a new neurogenic niche in the embryonic subventricular zone (SVZ). Here, a new kind of amplifying progenitors (basal progenitors) expressing Tbr2, undergo a second round of divisions, which is believed to have contributed to the expansion of the neocortex. Accordingly, the existence of a pallial SVZ has been classically considered exclusive of mammals. However, the lack of studies in ancient vertebrates precludes any clear conclusion about the evolutionary origin of the SVZ and the neurogenic mechanisms that rule pallial development. In this work, we explore pallial neurogenesis in a basal vertebrate, the shark Scyliorhinus canicula, through the study of the expression patterns of several neurogenic markers. We found that apical progenitors and radial migration are present in sharks, and therefore, their presence must be highly conserved throughout evolution. Surprisingly, we detected a subventricular band of ScTbr2-expressing cells, some of which also expressed mitotic markers, indicating that the existence of basal progenitors should be considered an ancestral condition rather than a novelty of mammals or amniotes. Finally, we report that the transcriptional program for the specification of glutamatergic pallial cells (Pax6, Tbr2, NeuroD, Tbr1) is also present in sharks. However, the segregation of these markers into different cell types is not clear yet, which may be linked to the lack of layering in anamniotesThis work was supported by the Spanish Ministerio de Economía y Competitividad-FEDER (BFU2014-5863-1P)S

Literary Translation: Implantation Vs Transference

The figurative language employed by authors, which reflects their styles of writing, is one main reason behind the challenges that most literary translators encounter when dealing with literary works. Usually employed for aesthetic and poetic purposes, figures of speech imply connotative meanings. In literary works, words are used only assigns to settle down the flying spirits of meanings and ideas so that the audience can have a thread that could lead them to intended meanings. I believe that literary translators should face the challenges of translating literary works through two main approaches. First, transferring the work of art as it is without trying to find any equivalent in the target language for any piece of text in the source language. The aim of such type of translation would be familiarizing the audience in the target language with the literature and culture of the source language. Second, translating the SL work of art creatively, i.e. using all possible strategies and procedures to find natural equivalents in the TL for any stylistic features in the SLT. This type of translation should aim at pleasing and entertaining the TL audience

Energy Transfer through a Magnetized Williamson Hybrid Nanofluid Flowing around a Spherical Surface: Numerical Simulation

A computational simulation of Williamson fluid flowing around a spherical shape in the case of natural convection is carried out. The Lorentz force and constant wall temperature are taken into consideration. In addition, upgrader heat transfer catalysts consisting of multi-walled carbon tubes, molybdenum disulfide, graphene oxide, and molybdenum disulfide are employed. The Keller box approach is used to solve the mathematical model governing the flow of hybrid Williamson fluid. To validate our findings, the key parameters in the constructed model are set to zero. Next, the extent of the agreement between our results and published results is observed. Numerical and graphical results that simulate the impressions of key parameters on physical quantities related to energy transmission are obtained, discussed, and analyzed. According to the results of this study, increasing the value of the Weissenberg number causes an increase in both the fluid temperature and drag force, while it also leads to a decrease in both the velocity of the fluid and the rate of energy transmission. Increasing the magnetic field intensity leads to a reduction in the rate of heat transfer, drag force, and fluid velocity while it has an appositive effect on temperature profiles