Text Line Recognition of Dai Language using Statistical Characteristics of Texture Analysis and Deep Gaussian Process

In view of the different recognition methods of Dai in different language, we proposed a novel method of text line recognition for New Tai Lue and Lanna Dai based on statistical characteristics of texture analysis and Deep Gaussian process, which can classify different Dai text lines. First, the Dai text line database is constructed, and the images are preprocessed by de-noise and size standardization. Gabor multi-scale decomposition is carried out on two Dai text line images, and then the statistical features of image entropy and average row variance feature is extracted. The multi-layers Deep Gaussian process classifier is constructed. Experiments show that the accuracy of text line classification of New Tai Lue and Lanna Dai based on Deep Gaussian process is 99.89%, the values of precision, recall and f1-score are 1, 0.9978 and 0.9989, respectively. The combination of Gabor texture analysis average row variance statistical features and Deep Gaussian process model can effectively classify the text line of New Tai Lue and Lanna Dai. Comparative experiments show that the classification accuracy of the model is superior to traditional methods, such as Gaussian Naive Bayes, Random Forest, Decision Tree, and Gaussian Process.

LEGAL DISCOURSE IN THE ASPECT OF INSTITUTIONAL SPECIFICITY: LINGUISTIC ASPECT

Введение. Рассмотрена специфика юридического дискурса в аспекте его институциональной принадлежности и пространственной концептуализации метафорической терминологии. Цель статьи – выявить взаимосвязь между пространственной (горизонталь/вертикаль) концептуализацией метафорической терминологии русского юридического дискурса и системой классификации законодательства Российской Федерации. Материал и методы. Рассматривается понятие «юридический дискурс», а также его специфика через призму отраслевой классификации законодательной системы России. Концептуальная структура законодательства в виде горизонтальной классификации по отраслям права и вертикальной иерархической системы метафоричности проявляется через терминологию юридического дискурса. Результаты и обсуждение. Концептуализация в области метафорической терминологии напрямую отражает концептуальную структуру области права. Несмотря на разнообразие оснований типологизации юридического дискурса, концептуализация знаниевых структур зависит от отрасли права. Метафорические термины выступают конструктами определенной отрасли юридического дискурса, транслируя особые концепты ментальности относительно объектов в рамках данного института права. Заключение. При анализе специфики институционального юридического дискурса обнаружена обусловленность концептуализации знаний, которые привлекаются для метафорического моделирования юридической терминологии. Номинативные средства трансляции системы понятий базируются на терминологической понятийной системе и обусловлены юридическим контекстом (отраслью). Состав знаниевых структур терминологии юридического дискурса обусловлен эволюцией правового знания в рамках языковой и социкультурной принадлежности. Introduction. The article highlights the specifics of the legal discourse in the aspect of its institutional interdependence and space conceptualization of metaphor terminology. The aim of the article is to identify the interrelation between the SPACE (horizontal/vertical) conceptualization of the metaphorical terminology of the Russian legal discourse and the horizontal line classification by branches of law and the vertical hierarchical system of the Russian legislation system. Material and methods. The legal discourse notion and its specificity is considered through the prism of legislative system branch classification accepted in the Russian Federation. The conceptual structure of the legislation system arranged in the form of the horizontal line classification by branches of law and the vertical hierarchical system of conceptual metaphor representation is realized through the terminology of the legal discourse. Results and discussion. Conceptualization in the field of metaphor terminology directly reflects the conceptual structure of the law branch. Although there is a broad variety of grounds for classification typology, the conceptualization of knowledge structures depends directly on the branch of law. Legal branch metaphor terms are constructs of a particular branch of legal discourse, which transfer special concepts of mentality regarding the objects of a particular branch of legal discourse. Conclusion. The analysis of the specifics of the institutional legal discourse reveals the conditionality and interdependence of the knowledge conceptualization within metaphor modeling in the legal terminology. The nominative means for the concepts system transfer are based on the terminology concept system and are determined mainly by the legal context (i.e. legal branch). The content of the knowledge structures in the legal discourse terminology is determined and influenced by the evolution of the legal knowledge within the framework of language and socio-cultural community.

UV bright red-sequence galaxies: how do UV upturn systems evolve in redshift and stellar mass?

ABSTRACT The so-called ultraviolet (UV) upturn of elliptical galaxies is a phenomenon characterized by the up-rise of their fluxes in bluer wavelengths, typically in the 1200–2500 Å range. This work aims at estimating the rate of occurrence of the UV upturn over the entire red-sequence population of galaxies that show significant UV emission. This assessment is made considering it as function of three parameters: redshift, stellar mass, and – what may seem counter-intuitive at first – emission-line classification. We built a multiwavelength spectrophotometric catalogue from the Galaxy Mass Assembly survey, together with aperture-matched data from Galaxy Evolution Explorer Medium-Depth Imaging Survey (MIS) and Sloan Digital Sky Survey, covering the redshift range between 0.06 and 0.40. From this sample, we analyse the UV emission among UV bright galaxies, by selecting those that occupy the red-sequence locus in the (NUV− r) × (FUV−NUV) chart; then, we stratify the sample by their emission-line classes. To that end, we make use of emission-line diagnostic diagrams, focusing the analysis in retired/passive lineless galaxies. Then, a Bayesian logistic model was built to simultaneously deal with the effects of all galaxy properties (including emission-line classification or lack thereof). The main results show that retired/passive systems host an up-rise in the fraction of UV upturn for redshifts between 0.06 and 0.25, followed by an in-fall up to 0.35. Additionally, we show that the fraction of UV upturn hosts rises with increasing stellar mass.

Cell Line Classification Using Electric Cell-Substrate Impedance Sensing (ECIS)

We present new methods for cell line classification using multivariate time series bioimpedance data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS technology, which monitors the attachment and spreading of mammalian cells in real time through the collection of electrical impedance data, has historically been used to study one cell line at a time. However, we show that if applied to data from multiple cell lines, ECIS can be used to classify unknown or potentially mislabeled cells, factors which have previously been associated with the reproducibility crisis in the biological literature. We assess a range of approaches to this new problem, testing different classification methods and deriving a dictionary of 29 features to characterize ECIS data. Most notably, our analysis enriches the current field by making use of simultaneous multi-frequency ECIS data, where previous studies have focused on only one frequency; using classification methods to distinguish multiple cell lines, rather than simple statistical tests that compare only two cell lines; and assessing a range of features derived from ECIS data based on their classification performance. In classification tests on fifteen mammalian cell lines, we obtain very high out-of-sample predictive accuracy. These preliminary findings provide a baseline for future large-scale studies in this field.