Representation Of Characteristics Research Articles

Efficient coding of cognitive variables underlies dopamine response and choice behavior.

Reward expectations based on internal knowledge of the external environment are a core component of adaptive behavior. However, internal knowledge may be inaccurate or incomplete due to errors in sensory measurements. Some features of the environment may also be encoded inaccurately to minimize representational costs associated with their processing. In this study, we investigated how reward expectations are affected by features of internal representations by studying behavior and dopaminergic activity while mice make time-based decisions. We show that several possible representations allow a reinforcement learning agent to model animals' overall performance during the task. However, only a small subset of highly compressed representations simultaneously reproduced the co-variability in animals' choice behavior and dopaminergic activity. Strikingly, these representations predict an unusual distribution of response times that closely match animals' behavior. These results inform how constraints of representational efficiency may be expressed in encoding representations of dynamic cognitive variables used for reward-based computations.

Ассоциативное поле концепта ЗДОРОВЬЕ в языковом сознании студентов в период пандемии

The paper aims at studying the specifics of the association field of the HEALTH concept in the language consciousness of students in the linguo-cognitive aspect. Scientific novelty of the research lies in the application of a free association experiment method to identify the features of this concept representation in the perception of students living in the era of the pandemic by analyzing reactions to the word-stimulus HEALTH and determining the dominants of the association field in their linguistic worldview. As a result of the research, a qualitative and quantitative description of reaction words has been carried out, the association field of the HEALTH concept has been constructed, the specifics of objectification of its nuclear and peripheral zones has been revealed. It has been proved that the HEALTH concept is of great importance in the linguistic consciousness of students and reflects the events of the present time.

Loneliness in adolescents with different social media behavior

IntroductionLoneliness is a critical issue of adolescents that has grown severe during the last decade. Social media use is often regarded as negative factor of loneliness experience in connection with escapism. At the same time social media is an important part of adolescents’ communication sphere. The present study aims to explore positive functions of social media which can help adolescents cope with loneliness.ObjectivesThe present research studies features of loneliness representation in adolescents with different behavior in social media.MethodsMultidimensional Inventory of Loneliness Experience; Cognitive Processing of Social Information in Internet Questionnaire; Method of unfinished sentences about loneliness and social media; Questionnaire about social media were used in the study with 44 adolescents, aged from 13 to 18.ResultsAdolescents have a higher level of loneliness (M = 29.6) than the results in 2013 (M = 17.7) show. Context analysis of unfinished sentences shows that 21% of adolescents have a various representation of loneliness, 71% perceive loneliness as negative. General feel of loneliness tends to show negative correlations with adequate perception of information in social media (k = - 0.317; p = 0.038). Negative attitude to loneliness is negatively correlated with interpretation adequacy of social media content (k=-0.568, p<0,001). Adolescents with low levels of holistic social media perceptions have higher levels of loneliness. Use of social media can reduce feelings of loneliness (p = 0.002).ConclusionsSocial media expand adolescents’ representation of loneliness. Productive use of social media can help adolescent cope with loneliness.DisclosureNo significant relationships.

From representations in predictive processing to degrees of representational features

Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success.

Языковые признаки гендерной нейтральности в английском и русском языках (на примере интернет-публикаций)

The aim of the study is to identify the linguistic signs of gender neutrality in the English and Russian languages. The examined languages reflect socio-political events and global trends, one of which is the desire for tolerance that makes it necessary to apply the policy of gender neutrality. The scientific novelty consists in revealing the features of the linguistic representation of gender neutrality at the present stage of language development by studying the materials of the Internet publications. As a result of the study, the potential ways of solving the problem of linguistic sexism have been characterized; the linguistic means of gender neutrality manifestation in English and Russian have been identified.

Deep Learning (CNN) and Transfer Learning: A Review

Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate different types of learning. A number of experiments showed that deep learning systems fared better than traditional ones when it came to image processing, computer vision, and pattern recognition. Several real-world applications and hierarchical systems have utilised transfer learning and deep learning algorithms for pattern recognition and classification tasks. Real-world machine learning settings, on the other hand, often do not support this assumption since training data can be difficult or expensive to get, and there is a constant need to generate high-performance beginners who can work with data from a variety of sources. The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current solutions and evaluate applications in diverse areas of transfer learning as well as deep learning.

Субстантивная репрезентация темпоральной и аспектуальной длительности (на материале английского и русского языков)

The purpose of the paper is to identify the features of substantive representation of aspectual duration in English and Russian. Scientific novelty of the research lies in the fact that the notions of temporal and aspectual duration are distinguished at the level of semantics and means of expression. As a result, it has been proved that substantives of the temporal semantics of duration are units of time measurement and nouns implying the presence of an interval in the studied languages. Aspectual substantives of duration are an indicator of duration of an action only if they are used together with verbal nouns or non-finite verb forms. In the English language, the meaning of continuation is emphasised, not the meaning of duration, which is actively implemented by the substantive “continuity”. In the Russian language, the substantives «длительность», «продолжительность» (duration) demonstrate significant activity compared to the English language.

Особенности репрезентации биофармацевтической индустрии в дискурсе фармацевтических, консалтинговых и статистических организаций (на материале английского языка)

The aim of the study is to identify the features of biopharmaceutical industry representation in the external discourse of international pharmaceutical organizations. For the first time, the ways of representing the international biopharmaceutical industry, determined by the strategies of reputational discourse in the field of pharmacy, are studied in the context of a cross-disciplinary comparison of the discursive practices of pharmaceutical, consulting and statistical organizations using discourse analysis and frame analysis, which explains scientific novelty of the study. As a result of comparing these discursive practices, the role of reputational discourse in the choice of concept nomination and methods of framing/reframing of the most significant topics is shown.

Network Intrusion Detection Method Combining CNN and BiLSTM in Cloud Computing Environment.

A network intrusion detection method combining CNN and BiLSTM network is proposed. First, the KDD CUP 99 data set is preprocessed by using data extraction algorithm. The data set is transformed into image data set by data cleaning, data extraction, and data mapping; Second, CNN is used to extract the parallel local features of attribute information, and BiLSTM is used to extract the features of long-distance-dependent information, so as to fully consider the influence between the front and back attribute information, and attention mechanism is introduced to improve the classification accuracy. Finally, C5.0 decision tree and CNN BiLSTM deep learning model are combined to skip the design feature selection and directly use deep learning model to learn the representational features of high-dimensional data. Experimental results show that, compared with the methods based on AE-AlexNet and SGM-CNN, the network intrusion detection effect of this method is better, the average accuracy can be improved to 95.50%, the false-positive rate can be reduced to 4.24%, and the false positive rate can be reduced to 6.66%. The proposed method can significantly improve the performance of network intrusion detection system.

Malleability of the cortical hand map following a finger nerve block.

Electrophysiological studies in monkeys show that finger amputation triggers local remapping within the deprived primary somatosensory cortex (S1). Human neuroimaging research, however, shows persistent S1 representation of the missing hand’s fingers, even decades after amputation. Here, we explore whether this apparent contradiction stems from underestimating the distributed peripheral and central representation of fingers in the hand map. Using pharmacological single-finger nerve block and 7-tesla neuroimaging, we first replicated previous accounts (electrophysiological and other) of local S1 remapping. Local blocking also triggered activity changes to nonblocked fingers across the entire hand area. Using methods exploiting interfinger representational overlap, however, we also show that the blocked finger representation remained persistent despite input loss. Computational modeling suggests that both local stability and global reorganization are driven by distributed processing underlying the topographic map, combined with homeostatic mechanisms. Our findings reveal complex interfinger representational features that play a key role in brain (re)organization, beyond (re)mapping.

Inferentialist conceptual engineering

ABSTRACT On a representationalist view, conceptual engineering is the practice of changing the extensions and intensions of the devices we use to speak and think. But if this view holds true, conceptual engineering has a bad rationale. Extensions and intensions are not the sorts of things that are better or worse as such. A representationalist account of conceptual engineering thus falls prey to the objection that the practice has a bad rationale. To account for the assumption that conceptual engineering is worthwhile, we propose to view what is being engineered as inferential devices, as opposed to representational devices. The objective is not to establish that being or having an inferential role is all there is to meaning or conceptual content. Rather, our agenda is to recommend a shift of focus from the representational features of content to the inferential features of content for the purposes of doing and thinking about conceptual engineering. Inferentialism about conceptual engineering makes better sense of the practice than a representationalist approach: In addition to accounting for the rationality of engaging in conceptual engineering, inferentialism provides a sound interpretation of what is at stake in concrete examples of conceptual engineering.

Self-supervised Short-text Modeling through Auxiliary Context Generation

Short text is ambiguous and often relies predominantly on the domain and context at hand in order to attain semantic relevance. Existing classification models perform poorly on short text due to data sparsity and inadequate context. Auxiliary context, which can often provide sufficient background regarding the domain, is typically available in several application scenarios. While some of the existing works aim to leverage real-world knowledge to enhance short-text representations, they fail to place appropriate emphasis on the auxiliary context. Such models do not harness the full potential of the available context in auxiliary sources. To address this challenge, we reformulate short-text classification as a dual channel self-supervised learning problem (that leverages auxiliary context) with a generation network and a corresponding prediction model. We propose a self-supervised framework, Pseudo-Auxiliary Context generation network for Short-text Modeling (PACS) , to comprehensively leverage auxiliary context and it is jointly learned with a prediction network in an end-to-end manner. Our PACS model consists of two sub-networks: a Context Generation Network (CGN) that models the auxiliary context’s distribution and a Prediction Network (PN) to map the short-text features and auxiliary context distribution to the final class label. Our experimental results on diverse datasets demonstrate that PACS outperforms formidable state-of-the-art baselines. We also demonstrate the performance of our model on cold-start scenarios (where contextual information is non-existent) during prediction. Furthermore, we perform interpretability and ablation studies to analyze various representational features captured by our model and the individual contribution of its modules to the overall performance of PACS, respectively.

FEATURES OF THE CONCEPTUALIZATION OF SOCIAL SPACE IN GERMAN LANGUAGE DURING THE COVID-19 PANDEMIC

With the development of the current topic of the COVID-19 pandemic in the German language, as well as in other languages, socio-culturally determined neoplasms have appeared, indicating the transformation of society and changes in the ways of understanding and understanding the world, in particular, the conceptualization of the space of society in the context of the corona crisis that has engulfed the entire globe. Using the concept of soziale Distanz as an example, the author examines the features of the verbal representation of the ways of perceiving spatial relations that have developed in the era of a large-scale pandemic that has changed the way of life of people, including the format and structure of social space. The analysis of the material under study showed that in a pandemic, there is a rethinking of social space, in particular social distance, considered in the framework of cognitive linguistics as a way to ensure safety and a guarantee of health.

Mixed Structure with 3D Multi-Shortcut-Link Networks for Hyperspectral Image Classification

A hyperspectral image classification method based on a mixed structure with a 3D multi-shortcut-link network (MSLN) was proposed for the features of few labeled samples, excess noise, and heterogeneous homogeneity of features in hyperspectral images. First, the spatial–spectral joint features of hyperspectral cube data were extracted through 3D convolution operation; then, the deep network was constructed and the 3D MSLN mixed structure was used to fuse shallow representational features and deep abstract features, while the hybrid activation function was utilized to ensure the integrity of nonlinear data. Finally, the global self-adaptive average pooling and L-softmax classifier were introduced to implement the terrain classification of hyperspectral images. The mixed structure proposed in this study could extract multi-channel features with a vast receptive field and reduce the continuous decay of shallow features while improving the utilization of representational features and enhancing the expressiveness of the deep network. The use of the dropout mechanism and L-softmax classifier endowed the learned features with a better generalization property and intraclass cohesion and interclass separation properties. Through experimental comparative analysis of six groups of datasets, the results showed that this method, compared with the existing deep-learning-based hyperspectral image classification methods, could satisfactorily address the issues of degeneration of the deep network and “the same object with distinct spectra, and distinct objects with the same spectrum.” It could also effectively improve the terrain classification accuracy of hyperspectral images, as evinced by the overall classification accuracies of all classes of terrain objects in the six groups of datasets: 97.698%, 98.851%, 99.54%, 97.961%, 97.698%, and 99.138%.

Лингвокультурная специфика репрезентирования телесности (на примере русского и английского юмористического дискурса)

The article focuses on exploring the specific features of body representation in Russian and English jokes. The purpose of the study was to identify linguocultural features of body representation in humorous discourse through analysing the meanings of the lexical unit body, which verbalizes the concept of body in the Russian and English languages. The lexico-semantic, conceptual and interpretative analyses revealed that this concept is represented in the linguistic worldviews of Russian and English linguocultures through common obligatory conceptual signs, such as materiality/tactility, integrity/ partibility and aliveness/deadness. It was established that the obligatory semantic (conceptual) sign of tactility is represented by verbs of perception and is common for both linguocultures, thus eliminating ambiguity in interpretation of jokes. The conceptual sign of integrity/partibility is expressed in the two linguocultures by nominating and redefining the functions of such body parts as the brain, face and liver in Russian linguoculture, and eyes, brain and liver in English linguoculture. The primary mean of representing the conceptual sign of integrity/partibility include metaphors, phraseological units, dark humour, as well as negative and evaluative judgements, all of which often lead to translation mistakes and communication failures. The semantic sign of aliveness/deadness is represented differently in the studied linguocultures. This can be accounted for by the fact that its linguoculture-specific representation mainly depends on contextual connotations and on the culture-specific ‘expected/unexpected’ effect, which is characteristic of humorous discourse.

Textual materiality and abstraction in mathematics.

In this paper, we wish to explore the role that textual representations play in the creation of new mathematical objects. We do so by analyzing texts by Joseph-Louis Lagrange (1736-1813) and Évariste Galois (1811-1832), which are seen as central to the historical development of the mathematical concept of groups. In our analysis, we consider how the material features of representations relate to the changes in conceptualization that we see in the texts.Against this backdrop, we discuss the idea that new mathematical concepts, in general, are increasingly abstract in the sense of being detached from material configurations. Our analysis supports the opposite view. We suggest that changes in the material aspects of textual representations (i.e., the actual graphic inscriptions) play an active and crucial role in conceptual change.We employ an analytical framework adapted from Bruno Latour's account of intertwined material and representational practices in the empirical sciences. This approach facilitates a foregrounding of the interconnection between the conceptual development of mathematics, and the construction, (re-)configuration, and manipulation of the materiality of representations. Our analysis suggests that, in mathematical practice, distinctions between the material and structural features of representations are not permanent and absolute. This problematizes the appropriateness of the distinction between concrete inscriptions and abstract relations in understanding the development of mathematical concepts.

Beyond body maps: Information content of specific body parts is distributed across the somatosensory homunculus.

SummaryThe homunculus in primary somatosensory cortex (S1) is famous for its body part selectivity, but this dominant feature may eclipse other representational features, e.g., information content, also relevant for S1 organization. Using multivariate fMRI analysis, we ask whether body part information content can be identified in S1 beyond its primary region. Throughout S1, we identify significant representational dissimilarities between body parts but also subparts in distant non-primary regions (e.g., between the hand and the lips in the foot region and between different face parts in the foot region). Two movements performed by one body part (e.g., the hand) could also be dissociated well beyond its primary region (e.g., in the foot and face regions), even within Brodmann area 3b. Our results demonstrate that information content is more distributed across S1 than selectivity maps suggest. This finding reveals underlying information contents in S1 that could be harnessed for rehabilitation and brain-machine interfaces.

Representation of Concepts BIG BROTHER / LITTLE SISTER in the Yakut Heroic Epic

Purpose. The article discusses the concepts of BIG BROTHER / LITTLE SISTER (UBAY / BALYS) in order to identify the features of representation of these concepts in the Yakut heroic epic olonkho and to establish the semantic content of these concepts, to determine their cultural and national specifics. Because, firstly, in a traditional family, as a unit of society, the relations of children are of great importance, as blood relatives they are the successors of the clan. Secondly, despite the global technocratic development, interest in and appeal to traditional values is growing in society, because family ties and family relations form self-organization and self-regulation of a person in society. Thirdly, due to the lack of study of these concepts in the Yakut epic texts.Results. Within the framework of the conceptual analysis, the method of continuous sampling was used to collect examples from epic texts; component analysis to characterize the conceptual constituent of the word; a method of contextual analysis which provides an opportunity to identify different meanings and shades of meanings of the studied concepts in the context of epic texts. In the examples considered, the lexemes ubay and balys and their various detonates represent their dictionary definitions, that is they designate an elder man relative and a younger woman relative.Conclusion. The result of the performed analysis shows that the concepts of BIG BROTHER / LITTLE SISTER are represented in the olonkho texts by various lexemes and phrases that have deep connotative meanings and reflect traditional family relationships between elder brothers and younger sisters among the Yakut people.

Person Re-Identification via Pyramid Multipart Features and Multi-Attention Framework

Video-based person re-identification has become quite attractive due to its importance in many vision surveillance problems. It is a challenging topic due to the inter/intra changes, occlusion, and pose variations involved. In this paper, we propose a pyramid-attentive framework that relies on multi-part features and multiple attention to aggregate features of multi-levels and learns attention-based representations of persons through various aspects. Self-attention is used to strengthen the most discriminative features in the spatial and channel domains and hence capture robust global information. We propose the use of part-relation attention between different multi-granularities of features’ representation to focus on learning appropriate local features. Temporal attention is used to aggregate temporal features. We integrate the most robust features in the global and multi-level views to build an effective convolution neural network (CNN) model. The proposed model outperforms the previous state-of-the art models on three datasets. Notably, using the proposed model enables the achievement of 98.9% (a relative improvement of 2.7% on the GRL) top1 accuracy and 99.3% mAP on the PRID2011, and 92.8% (a relative improvement of 2.4% relative to GRL) top1 accuracy on iLIDS-vid. We also explore the generalization ability of our model on a cross dataset.

Self-supervised representation learning for detection of ACL tear injury in knee MR videos

• Novel CNN model is proposed for efficiently solving jigsaw puzzle as pretext task. • The model is trained to learn explainable visual representational features. • Proposed Divide-and-Teach strategy helps in resource-constrained training. • It is effective to extract explainable and transferable context-invariant features. • First work on self-supervised learning for ACL tear detection in Knee MR. The success of deep learning based models for computer vision applications requires large scale human annotated data which are often expensive to generate. Self-supervised learning, a subset of unsupervised learning , handles this problem by learning meaningful features from unlabeled image or video data. In this paper, we propose a self-supervised learning approach to learn transferable features from MR video clips by enforcing the model to learn anatomical features. The pretext task models are designed to predict the correct ordering of the jumbled image patches that the MR video frames are divided into. To the best of our knowledge, none of the supervised learning models performing injury classification task from MR video provide any explanation for the decisions made by the models and hence makes our work the first of its kind on MR video data. Experiments on the pretext task show that this proposed approach enables the model to learn spatial context invariant features which help for reliable and explainable performance in downstream tasks like classification of Anterior Cruciate Ligament tear injury from knee MR videos. The efficiency of the novel Convolutional Neural Network proposed in this paper is reflected in the experimental results obtained in the downstream task. The proposed model achieves an accuracy of 76.62% and an AUC score of 0.848 on the Sagittal plane , outperforming the contrastive learning algorithms like PIRL and SimCLR using jigsaw puzzle as a transformation. The proposed model also achieved an AUC score of 0.740 on the KneeMRI dataset.