Mnemonic Aids in Linguodidactic Discourse

The article examines modern English memory aids as part of linguodidactic discourse in teaching English, and the patterns of mnemonic devices represented by sentence, acronym, abbreviation and verse. The empirical material of the research incorporates 54 units. The focus is made on an extensive number of mnemonics compiled as a meaningful text and the patterns of the devices in the form of a sentence, and the mnemonic acronyms homonymous to general vocabulary, i. e. homoacronyms. At that, the mnemonic aids which are not similar to other words, are not so often used as mnemonic devices. The mnemonic devices in the paper have been studied on the basis of the English and Russian sources published in the 21st century.Mnemonic devices have become part of research only in recent years. Previously mnemonics were studied within the framework of pedagogical discourse in teaching Geography, Biology, Astronomy, History, and Music. In the present research the mnemonics related to teaching English were distributed by structure. Afterwards, mnemonic sentences and the mnemonics homonymous to the existing lexemes were identified. The analysis of the structures showed the domination of the mnemonics structured as sentences and homoacronyms. 54 units of mnemonics under study were presented in the form of 22 sentences, 3 verses, 17 acronyms and 12 abbreviations. The most common structure proved to be a sentence, while the least common one was verse. The mnemonics considered contained only 13 units which were not sentences or homoacronyms: 12 abbreviations and 1 acronym of primary nomination. The examination of the structures showed the domination of the mnemonics organized as a sentence or a homoacronym. The research confirms the assumptions made earlier on the frequent use of sentence mnemonics, which, as it turned out, exceed the shares of the other mnemonic patterns. In their turn, homoacronyms made up a larger share compared to acronyms and abbreviations with no reference to general English words or verse. Thus, we can consider these structures (sentences and homoacronyms) within English teaching as part of pedagogical discourse. To sum it up, while compiling mnemonic aids, preference should be given to sentences or homoforms based on the vocabulary, while verses and abbreviations might be used economically. The article has also revealed additional features of mnemonics, in particular applying asyndeton in acronyms and abbreviations, the average number of 3 or 4 components in a mnemonic aid. Studying such structures will contribute to examining shortened forms and functioning of mnemonics in linguodidactic discourse.

Application of digital technology in the work of a pathologist: guidelines for learning how to use speech recognition systems

Natural language processing is one of the branches of computational linguistics. It is a branch of computer science that includes algorithmic processing of speech and natural language scripts. The algorithms facilitate the development of human-to-machine translation and automatic speech recognition systems (ASRS). ASRS use is twofold: accurately converting operators speech into a coherent and meaningful text and using natural language for interaction with a computer. Currently, these systems are widely used in medical practice, including anatomic pathology. Successful ASRS implementation pivots on creation of standardized templated descriptions for organic inclusion in the diagnosis dictation, likewise on physician training for using such systems in practice. In the past decade, there have been several attempts to standardize surgical pathology reports and create templates undertaken by physicians worldwide. After studying domestic and foreign literature, we created a list of the essential elements that must be included in the template for macro-and microscopic descriptions comprising the final diagnosis. These templates will help in decision-making and accurate diagnosis as they contain all the imperative elements in order of importance. This approach will significantly reduce the need for re-examination of both fixed macroscopic material and the preparation of additional histological sections. The templates built into ASRS reduce the time spent on documentation and significantly reduce the workload for pathologists. For the successful use of ASRS, we have developed an educational course, Digital Speech Recognition in an Anatomical Pathology Practice, for postgraduate education of both domestic and foreign doctors. A brief description of the course is presented in this article, and the course itself is available on the Internet.

Predicting Dyslexia and Reading Speed in Adolescents from Eye Movements in Reading and Non-Reading Tasks: A Machine Learning Approach

There is evidence that abnormalities in eye movements exist during reading in dyslexic individuals. A few recent studies applied Machine Learning (ML) classifiers to such eye movement data to predict dyslexia. A general problem with these studies is that eye movement data sets are limited to reading saccades and fixations that are confounded by reading difficulty, e.g., it is unclear whether abnormalities are the consequence or the cause of reading difficulty. Recently, Ward and Kapoula used LED targets (with the REMOBI & AIDEAL method) to demonstrate abnormalities of large saccades and vergence eye movements in depth demonstrating intrinsic eye movement problems independent from reading in dyslexia. In another study, binocular eye movements were studied while reading two texts: one using the “Alouette” text, which has no meaning and requires word decoding, the other using a meaningful text. It was found the Alouette text exacerbates eye movement abnormalities in dyslexics. In this paper, we more precisely quantify the quality of such eye movement descriptors for dyslexia detection. We use the descriptors produced in the four different setups as input to multiple classifiers and compare their generalization performances. Our results demonstrate that eye movement data from the Alouette test predicts dyslexia with an accuracy of 81.25%; similarly, we were able to predict dyslexia with an accuracy of 81.25% when using data from saccades to LED targets on the Remobi device and 77.3% when using vergence movements to LED targets. Noticeably, eye movement data from the meaningful text produced the lowest accuracy (70.2%). In a subsequent analysis, ML algorithms were applied to predict reading speed based on eye movement descriptors extracted from the meaningful reading, then from Remobi saccade and vergence tests. Remobi vergence eye movement descriptors can predict reading speed even better than eye movement descriptors from the meaningful reading test.

The Role of Value-Semantic Oppositions in Revealing the Deep Literary and Psychological Characteristics of the Narrator’s Image in “Poor Liza” by N.M. Karamzin

Value-semantic oppositions in the novel by N.M. Karamzin, seen as meaningful text dominants in situations of "existential fullness", are discussed in the article from the point of view of an interdisciplinary approach of literary criticism, text theory, psychology and linguistics. The article describes the process of literary and psychological meaning generation, coupled with the narrator’s inherent properties, as well as implicit literary and psychological textual relationships with inherent properties, it also singles out the contextual ways of these characteristics, reflecting motives, attitudes, generalized worldview judgments (generalizations). The author shows the dialog nature of the narrator’s literary and psychological attitude to the realities denoted by various parts of the opposition in the situation of “existential fullness”. The role of lexical markers corresponding to the depicted integral emotional state of the narrator in the privision of active reader's attention and attracting him to the deep literary and psychological characteristics is revealed.

LigGPT: Molecular Generation using a Transformer-Decoder Model

<p>Application of deep learning techniques for the de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enables the usage of state of the art models in Natural Language Processing, such as the Transformers, for molecular design in general. Inspired by Generative Pre-Training (GPT) model that have been shown to be successful in generating meaningful text, we train a Transformer-Decoder on the next token prediction task using masked self-attention for the generation of druglike molecules in this study. We show that our model, LigGPT, outperforms other previously proposed modern machine learning frameworks for molecular generation in terms of generating valid, unique and novel molecules. Furthermore, we demonstrate that the model can be trained conditionally to optimize multiple properties of the generated molecules. We also show that the model can be used to generate molecules with desired scaffolds as well as desired molecular properties, by passing these structures as conditions, which has potential applications in lead optimization in addition to de novo molecular design. Using saliency maps, we highlight the interpretability of the generative process of the model.</p>

LigGPT: Molecular Generation using a Transformer-Decoder Model

<p>Application of deep learning techniques for the de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enables the usage of state of the art models in Natural Language Processing, such as the Transformers, for molecular design in general. Inspired by Generative Pre-Training (GPT) model that have been shown to be successful in generating meaningful text, we train a Transformer-Decoder on the next token prediction task using masked self-attention for the generation of druglike molecules in this study. We show that our model, LigGPT, outperforms other previously proposed modern machine learning frameworks for molecular generation in terms of generating valid, unique and novel molecules. Furthermore, we demonstrate that the model can be trained conditionally to optimize multiple properties of the generated molecules. We also show that the model can be used to generate molecules with desired scaffolds as well as desired molecular properties, by passing these structures as conditions, which has potential applications in lead optimization in addition to de novo molecular design. Using saliency maps, we highlight the interpretability of the generative process of the model.</p>

CPGAN : An Efficient Architecture Designing for Text-to-Image Generative Adversarial Networks Based on Canonical Polyadic Decomposition

Text-to-image synthesis is an important and challenging application of computer vision. Many interesting and meaningful text-to-image synthesis models have been put forward. However, most of the works pay attention to the quality of synthesis images, but rarely consider the size of these models. Large models contain many parameters and high delay, which makes it difficult to be deployed on mobile applications. To solve this problem, we propose an efficient architecture CPGAN for text-to-image generative adversarial networks (GAN) based on canonical polyadic decomposition (CPD). It is a general method to design the lightweight architecture of text-to-image GAN. To improve the stability of CPGAN, we introduce conditioning augmentation and the idea of autoencoder during the training process. Experimental results prove that our architecture CPGAN can maintain the quality of generated images and reduce at least 20% parameters and flops.

gold lamella for ‘Blessed’ Abalala

This article examines a previously unpublished gold lamella of unknown provenance, datable on palaeographical grounds to the 1st century BCE, give-or-take a half century, either side. The tablet preserves three words written in Greek letters that may contain a GrecoPersian formula of protection in the afterlife for its bearer, Abalala, a name of pre-Islamic extraction. The study compares the formula with those on a number of shorter ‘Orphic’ gold lamellae to show that the tiny piece represents a ‘Totenpaß’ for the beneficent dead, rather than a protective charm (phylactery) with the usual voces magicae, although the distinction between magic words and meaningful text is not always clear in such instances.