Representation Of Characteristics Research Articles

Crossmodal correspondence of elevation/pitch and size/pitch is driven by real-world features.

Crossmodal correspondences are consistent associations between sensory features from different modalities, with some theories suggesting they may either reflect environmental correlations or stem from innate neural structures. This study investigates this question by examining whether retinotopic or representational features of stimuli induce crossmodal congruency effects. Participants completed an auditory pitch discrimination task paired with visual stimuli varying in their sensory (retinotopic) or representational (scene integrated) nature, for both the elevation/pitch and size/pitch correspondences. Results show that only representational visual stimuli produced crossmodal congruency effects on pitch discrimination. These results support an environmental statistics hypothesis, suggesting crossmodal correspondences rely on real-world features rather than on sensory representations.

Enhanced BERT-based Multi-Head Self-Attention Transformer for Transformation of Marathi Text to Marathi Sign Language Gloss

One recent advancement in the field of machine learning is the translation of text into sign language gloss, which is a form of natural language for the deaf community. The work presented is a new method to translate Marathi text to Marathi sign language gloss by combining salient features of Bidirectional Encoder Representation from Transformer (BERT) for tokenization and complementing the tokenized frame with Encoder with Attention mechanism and decoding with the LSTM decoder. The work conducted experiments on the created corpora of Marathi text and Marathi sign language gloss sentence pairs. The experiments that employed three models show that the suggested model performs better than the existing approaches. The results show that the translation of Marathi text to sign gloss achieves improved performances with an accuracy of 91.5% and the BLEU scores BLEU-1: 85, BLEU-2: 75, BLEU-3: 65, and BLEU-4: 57.

Social representations of young people about people with disabilities and disabilities in an inclusive educational environment: the experience of cross-cultural research

Relevance. Interest in the study of social representations arose due to the development of inclusion in all spheres of life. This fact has an impact on the specifics of training Russian and foreign students in this field in a medical university, the development of their inclusive competence. The study of cross-cultural features of social representations of persons with disabilities and the disabled, the identification of universal and specific phenomena in respondents will contribute to understanding social and psychological phenomena at the individual level that determine social attitudes and behavior of individuals in an inclusive modern society, taking into account its sociocultural characteristics.The purpose of the study is to study the cross-cultural characteristics of social perceptions of people with disabilities and people with disabilities among Russian and foreign students.Objectives: to analyze the content characteristics and approaches to the study of social representations; to develop programs of empirical research; to describe the results of the study; to formulate conclusions.Methodology. This study is based on the provisions of the theory of social constructionism (P. Berger, T. Lukman; J.Douglas); E. Durkheim's concepts of collective representations; theories of social representations (S. Moscovici, D. Jodelet, etc.). Research methods: theoretical analysis of literature, research interview, method of verbal associations, testing, content analysis.Results. Both similar and specific phenomena in social representations of people with disabilities and people with disabilities in the samples of Russian and foreign students were revealed. Similar is the presence of tolerant attitudes towards people with disabilities and people with disabilities in both samples. The differences relate to the social level of social perceptions, as well as the characteristics of social distance in relation to respondents to this category of citizens.Conclusions. Along with a tolerant attitude towards people with disabilities and people with disabilities, respondents have insufficient professional awareness of this category of citizens, experience difficulties in interacting with them at the level of joint activities and professional relationships, which requires the development of additional programs and practices in the context of social, educational and volunteer activities to develop inclusive competence of students.

О представительной природе органов публичной власти

The article discusses the issues of the representative nature of public authorities. Emphasis is placed on the essential features that make it possible to assess the nature of representation. The main attention is paid to representative bodies of power as the most accurate reflection of the nature of representation. When formulating the criteria for distinguishing the features of representation, the formal-logical and comparative-legal methods were used. It is concluded that the nature of representation is most fully revealed in formation and functioning of collegial elected bodies of public power.

Category boundaries modulate memory in a place-cell-like manner.

Concepts describe how instances of the same kind are related, enabling the categorization and interpretation of new information.1,2 How concepts are represented is a longstanding question. Category boundaries have been considered defining features of concept representations, which can guide categorical inference,3 with fMRI evidence showing category-boundary signals in the hippocampus.4,5 The underlying neural mechanism remains unclear. The hippocampal-entorhinal system, known for its spatially tuned neurons that form cognitive maps of space,6,7 may support conceptual knowledge formation, with place cells encoding locations in conceptual space.4,8,9,10,11 Physical boundaries anchor spatial representations and boundary shifts affect place and grid fields,12,13,14,15,16 as well as human spatial memory,17,18,19 along manipulated dimensions. These place cell responses are likely driven by boundary vector cells, which respond to boundaries at specific allocentric distances and directions,20,21,22,23 the neural correlates of which have been identified in the subiculum and entorhinal cortex20,24,25. We hypothesize similar patterns of memory adaptations in response to shifting category boundaries. Our findings show that after category boundary shifts, participants' memory for category exemplars distorts along the changed dimension, mirroring place field deformations. We demonstrate that the boundary vector cell model of place cell firing best accounts for these distortions compared with alternative geometric explanations. Our study highlights a role of category boundaries in human cognition and establishes a new complementary link between hippocampal coding properties with respect to boundaries and human concept representation, bridging spatial and conceptual domains.

CTNet: A data-driven time-frequency technique for wind turbines fault diagnosis under time-varying speeds

Nonstationary fault signals collected from wind turbine planetary gearboxes and bearings often exhibit close-spaced instantaneous frequencies (IFs), or even crossed IFs, bringing challenges for existing time-frequency analysis (TFA) methods. To address the issue, a data-driven TFA technique, termed CTNet is developed. The CTNet is a novel model that combines a fully convolutional auto-encoder network with the convolutional block attention module (CBAM). In the CTNet, the encoder layer is first designed to extract coarse features of the time-frequency representation (TFR) calculated by the general linear Chirplet transform (GLCT); second, the decoder layer is combined to restore and conserve details of the key time-frequency features; third, the skip connections are designed to accelerate training by linking extracted and reconstructed features; finally, the CBAM is introduced to adaptively explore channel and spatial relationships of the TFR, focusing more on close-spaced or crossed frequency features, and effectively reconstruct the TFR. The effectiveness of the CTNet is validated by numerical signals with close-spaced or crossed IFs, and real-world signals of wind turbine planetary gearbox and bearings. Comparison analysis with state-of-the-art TFA methods shows that the CTNet has high time-frequency resolution in characterizing nonstationary signals and a much better ability to detect wind turbine faults.

Thematic Content, Not Number Matching, Drives Syntactic Bootstrapping

ABSTRACT Children use correlations between the syntax of a clause and the meaning of its predicate to draw inferences about word meanings. On one proposal, these inferences are underwritten by a structural similarity between syntactic and semantic representations: learners expect that the number of clause arguments exactly matches the number of participant roles in the event concept under which its referent is viewed. We argue against this proposal, and in favor of a theory rooted in syntactic and semantic contents – in mappings from syntactic positions to thematic relations. We (i) provide evidence that infants view certain scenes under a concept with three participant relations (a girl taking a truck from a boy), and (ii) show that toddlers do not expect these representations to align numerically with clauses used to describe those scenes: they readily accept two-argument descriptions (“she pimmed the truck!”). This argues against syntactic bootstrapping theories underwritten by mappings between structural features of syntactic and semantic representations. Instead, our findings support bootstrapping based on grammatical and thematic content. Children’s earliest inferences may rely on the assumption that the syntactic asymmetry between subject and object correlates with a difference in how their referents relate to the event described by the sentence.

Феномен скулшутинга и его толкование в контексте юридико-психологического знания и ассоциативных смыслов будущих следователей

<p style="text-align: justify;">The reality and severity of the consequences of the phenomenon of schoolshooting*, which has distinctive features that make it possible to recognize it among criminal events, are stated. The imperfection of the definitions of schoolshooting is emphasized, and the need for its interdisciplinary analysis and study by future investigators within the framework of the discipline “Legal Psychology” is stated. The purpose of the study is to identify the features of associative representations of students (future investigators) about schoolshooting and to reveal the semantic content of conceptual connections, based on modern legal and psychological provisions. The survey data were obtained using the association method and are subject to mathematical processing. The main results correlating with the hypotheses are to determine the spectrum and structure of associative ideas about schoolshooting; to establish the consistency of associative ideas with scientific information; to identify the understanding, but at the same time insufficient use of psychological terms by cadets; to characterize schoolshooting based on filling associative-conceptual connections with scientifically based content.</p>

Features of representation of the genre tradition of the Bildungsroman in the Russian prose of the late 1920s

Features of representation of the genre tradition of the Bildungsroman in the Russian prose of the late 1920s

MCRNet: Underwater image enhancement using multi-color space residual network

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images, which can impede the ever-growing demand for underwater robot operations. To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. Our method takes advantage of the unique features of color representation in the RGB, HSV, and Lab color spaces. By utilizing the distinct feature representations of images in different color spaces, we can highlight and fuse the most informative features of the three color spaces. Our approach employs a self-attention mechanism in the multi-color space feature fusion module. Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images, particularly in severely degraded scenes. Consequently, our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Collaborative imagination synchronizes representations of the future and fosters social connection in the present

From close friends to people on a first date, imagining a shared future appears fundamental to relationships. Yet, no previous research has conceptualized the act of imagination as a socially constructed process that affects how connected we feel to others. The present studies provide a framework for investigating imagination as a collaborative process in which individuals cocreate shared representations of hypothetical events-what we call collaborative imagination. Across two preregistered studies (N = 244), we provide evidence that collaborative imagination of a shared future fosters social connection in novel dyads-beyond imagining a shared future individually or shared experience in general. Subjective ratings and natural language processing of participants' imagined narratives illuminate the representational features of imagined events shaped by collaborative imagination. Together, the present findings have the potential to shift how we view the structure and function of imagination with implications for better understanding interpersonal relationships and collective cognition.

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation.

Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, is proposed in this paper. This network attempts to model long-range spatial dependencies while maintaining real-time performance, to better distinguish polyps from background noise and thus improve segmentation accuracy. A novel Fusion Attention Encoder (FAEncoder) is designed in the proposed network, which integrates Large Kernel Attention (LKA) and channel attention mechanisms to extract deep representational features of polyps and unearth long-range dependencies. Furthermore, a newly designed Visual Attention Mamba module (VAM) is added to the skip connections, modeling long-range context dependencies in the encoder-extracted features and reducing background noise interference through the attention mechanism. Finally, a Pyramid Split Attention module (PSA) is used in the bottleneck layer to extract richer multi-scale contextual features. The proposed method was thoroughly evaluated on four renowned polyp segmentation datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, and ETIS. Experimental findings demonstrate that the proposed method delivers higher segmentation accuracy in less time, consistently outperforming the most advanced lightweight polyp segmentation networks.

Visualizing dementia and stigma: a scoping review of the literature

Discursive choices are recognized by both academic and dementia communities as being central to the perpetuation (or challenging) of dementia-related stigma. Yet, the focus of the vast majority of research on the discursive dynamics of dementia stigma to date has been on the role of language only, effectively failing to regard the multimodal reality of discourse. The present study aims to address this gap by conducting a scoping review of the smaller, and relatively more recent, body of literature that has examined visual modes of communication. The authors ask the following questions: (1) What theories of stigma have informed or guided studies of visual representations of dementia and people with dementia? (2) What visual features of representations of dementia and people with dementia might contribute to and/or challenge dementia stigma? Using Scopus, PubMed, PsychInfo and Google Scholar, 10 papers published between January 2000 and July 2023 were selected and thematically synthesized. The authors found that most studies had limited or no engagement with specific stigma theories, although the general principle of establishing or challenging distance between an in-group (‘us’) and out-group (‘them’) informed many of the analyses. Visual features with the potential to contribute to stigma tended to impersonalize people with dementia through foregrounding visual markers of dementia (oftentimes emphasizing loss and/or the brain) and establishing symbolic distance between viewers/other represented participants and people with dementia. This distance could be achieved through visual framing techniques (regarding angle, gaze, colour, setting) which, for instance, could subtly position people with dementia as the ‘living dead’. There was much less focus on visual features with the potential to challenge stigma, which together emphasized social connection, transformation and taking the perspective of someone with dementia. Turning to reception, another potential aspect of challenging stigma was reinterpreting supposedly ‘stigmatizing’ images. In this article, these findings are interpreted in relation to the broader stigma literature and implications for future research and advocacy efforts are discussed.

Репрезентация образа локуса в рекламном туристическом нарративе

The aim of the study is to define the features of locus image representation in a tourist advertising narrative. The scientific novelty of the work lies in the fact that the consideration of locus image in a tourist advertising narrative has not been undertaken before. The novelty is also determined by the fact that for the first time loci have been singled out, that is, spaces actualized by geographical factors and natural and cultural landscapes. For the first time it has been indicated that loci represented by texts of a tourist advertising narrative simulate its sense-forming space and also construct a system of meanings. The article considers the different interpretations of the term “locus” by scholars and gives the author’s understanding of this concept. As a result of the research, the main loci in a tourist advertising narrative are singled out, namely: the locus of refuge and salvation, the locus of Paradise and absolute happiness, the locus of dreams, the locus of harmony, the locus of hope, the locus of relaxation and activation, the locus of new life, the locus of freedom, the locus of discovery, the locus of adventure, the locus of a fairy tale and a dream, the locus of mystery, the locus of returning to childhood, the locus of initiation. The text-forming potential of the locus image in a tourist advertising narrative is noted, as well as its dominance in focusing and holding the attention and interest of the text’s recipient. The ability of the locus image to potentially influence the consumer of the service by representing the topic of the text and the subsequent development of its structure is emphasized, which actualizes the topic and accumulates markers of valuable information.

Prediction of Drug-Target Affinity Using Attention Neural Network.

Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.

Image types revisited. A texto-material approach for creating image types

In visual communication research there are already several methodological approaches for creating image types. These approaches work with creating typologies based on the representational features of visuals / image motifs, reducing the complexity of extensive visual material. However, we argue that in everyday visual communication, the meaning of images depends largely on the practices in which they are embedded and not on the motif. This paper’s aim is to propose a methodological design for an image type analysis that considers both the representational and the artifact-related component of images, following a texto-material understanding of images. We illustrate the procedure step by step through an empirical study that explores visuals in couple relationships in Switzerland. The method enables a qualitative analysis of a large number of visuals that combines a) the creation of image types and b) a dynamic approach to in-depth visual analysis. Image types resulting from this procedure are more heterogeneous in motifs compared to previous traditions of image type analysis but allow for considering both the visual and the material dimensions of visuals, e.g., practices in which images are embedded. The approach builds on existing methods and contributes to the advancements of methods within visual communication research.

P D N: A Priori Dictionary Network for Fashion Parsing

The task of fashion parsing aims to assign pixel-level labels to clothing targets; thereby, parsing models are required to have good contextual recognition ability. However, the shapes of clothing components are complex, and the types are difficult to distinguish. Recent solutions focus on improving datasets and supplying abundant priori information, but the utilization of features by more efficient methods is rarely explored. In this paper, we propose a multi-scale fashion parsing model called the Priori Dictionary Network (PDN), which includes a priori attention module and a multi-scale backbone. The priori attention module extracts high dimensional features from our designed clothing average template as a priori information dictionary (priori dictionary, PD), and the PD is utilized to activate the feature maps of a CNN from a multi-scale attention mechanism. The backbone is derived from classical models, and five side paths are designed to leverage the richer features of local and global contextual representations. To measure the performance of our method, we evaluated the model on four public datasets, the CFPD, UTFR-SBD3, ModaNet and LIP, and the experimental results show that our model stands out from other State of the Art in all four datasets. This method can assist with the labeling problem of clothing datasets.

Automatic tooth arrangement with joint features of point and mesh representations via diffusion probabilistic models

Tooth arrangement is a crucial step in orthodontics treatment, in which aligning teeth could improve overall well-being, enhance facial aesthetics, and boost self-confidence. To improve the efficiency of tooth arrangement and minimize errors associated with unreasonable designs by inexperienced practitioners, some deep learning-based tooth arrangement methods have been proposed. Currently, most existing approaches employ MLPs to model the nonlinear relationship between tooth features and transformation matrices to achieve tooth arrangement automatically. However, the limited datasets (which to our knowledge, have not been made public) collected from clinical practice constrain the applicability of existing methods, making them inadequate for addressing diverse malocclusion issues. To address this challenge, we propose a general tooth arrangement neural network based on the diffusion probabilistic model. Conditioned on the features extracted from the dental model, the diffusion probabilistic model can learn the distribution of teeth transformation matrices from malocclusion to normal occlusion by gradually denoising from a random variable, thus more adeptly managing real orthodontic data. To take full advantage of effective features, we exploit both mesh and point cloud representations by designing different encoding networks to extract the tooth (local) and jaw (global) features, respectively. In addition to traditional metrics ADD, PA-ADD, CSA, and MErot, we propose a new evaluation metric based on dental arch curves to judge whether the generated teeth meet the individual normal occlusion. Experimental results demonstrate that our proposed method achieves state-of-the-art tooth alignment results and satisfactory occlusal relationships between dental arches. We will publish the code and dataset.

Is Quality Warranty a Type of Representations and Warranties?

The paper identifies and compares the key features of representations and warranties and quality warranties. Based on the results of the study, the author concludes that quality warranty is a type of representations and warranties. This is evidenced by the key element of their similarity — the reflection of the qualitative characteristics of the subject of the transaction. After all, both representations and warranties and quality warranty are inextricably linked with the contract, in the absence of which their existence does not matter. The differences between the studied categories reflect the specific features of quality warranty as representations and warranties. However, this does not exclude the possibility that certain properties of representations and warranties can be extended to a quality guarantee, and this will not contradict the essence of the latter, which will better ensure the rights and interests of parties to transactions. For example: 1) extend the rule on the autonomous and independent nature of representations to the effect of quality warranty; 2) allow for the provision of quality warranties by a third party in non-consumer legal relations, as exists in the case of the use of representations and warranties. It is also proposed to introduce a legal presumption of the commencement of validity of representations and warranties, which is based on the principle of exercising the right in one’s own interest (Clause 2 of Article 1 of the Civil Code of the Russian Federation).

AMCFCN: attentive multi-view contrastive fusion clustering net

Advances in deep learning have propelled the evolution of multi-view clustering techniques, which strive to obtain a view-common representation from multi-view datasets. However, the contemporary multi-view clustering community confronts two prominent challenges. One is that view-specific representations lack guarantees to reduce noise introduction, and another is that the fusion process compromises view-specific representations, resulting in the inability to capture efficient information from multi-view data. This may negatively affect the accuracy of the clustering results. In this article, we introduce a novel technique named the “contrastive attentive strategy” to address the above problems. Our approach effectively extracts robust view-specific representations from multi-view data with reduced noise while preserving view completeness. This results in the extraction of consistent representations from multi-view data while preserving the features of view-specific representations. We integrate view-specific encoders, a hybrid attentive module, a fusion module, and deep clustering into a unified framework called AMCFCN. Experimental results on four multi-view datasets demonstrate that our method, AMCFCN, outperforms seven competitive multi-view clustering methods. Our source code is available at https://github.com/xiaohuarun/AMCFCN.