Semantic Categories Research Articles

Characteristics of cerebellar cognitive affective syndrome in patients with acute cerebellar stroke and its impact on outcome

Purpose To evaluate the cerebellar cognitive affective syndrome scale (CCAS-S) in patients with acute cerebellar stroke (ACS) and examine its relationship with the discharge destination. Methods Patients with first-time ACS admitted to our hospital between April 2021 and April 2023 were included. The CCAS-S, Mini-Mental State Examination (MMSE), and Scale for the Assessment and Rating of Ataxia (SARA) were evaluated 1 week after stroke onset, and Functional Independence Measure (FIM)/Barthel Index (BI) at discharge, duration of hospitalization, and discharge destination were evaluated. The Mann–Whitney U test was used to compare CCAS-S and variables. Results Thirteen consecutive patients with ACS and age-matched comparison groups were included. The MMSE was within the normal range in all patients; however, patients with stroke had a lower total CCAS-S score (median 72, IQR 66–80) and a higher number of failed tests (median 4, IQR 3–5) than comparison. Significant deficits were observed in semantic fluency (p = 0.008), category switching (p = 0001), and similarity (p = 009). Definite CCAS were diagnosed 10 patients, respectively. Patients discharged home showed better SARA and FIM/BI but similar CCAS-S compared to those discharged to rehabilitation hospitals. Conclusion In patients with ACS, it is the impairment of motor function, not CCAS, that affects discharge destination.

Using data from cue presentations results in grossly overestimating semantic BCI performance.

Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging. Existing EEG-based semantic decoding studies often rely on neural activity recorded when a cue is present, raising concerns about decoding reliability. To address this, we investigate the effects of cue presentation on EEG-based semantic decoding. In an experiment with a clear separation between cue presentation and mental task periods, we attempt to differentiate between semantic categories of animals and tools in four mental tasks. By using state-of-the-art decoding analyses, we demonstrate significant mean classification accuracies up to 71.3% during cue presentation but not during mental tasks, even with adapted analyses from previous studies. These findings highlight a potential issue when using neural activity recorded during cue presentation periods for semantic decoding. Additionally, our results show that semantic decoding without external cues may be more challenging than current state-of-the-art research suggests. By bringing attention to these issues, we aim to stimulate discussion and drive advancements in the field toward more effective semantic BCI applications.

An online review on customer satisfaction of mid-to-high-end health and wellness hotels in China

The rapid development of information technology has enabled the collection of online review data, which provides a more precise representation of hotel guest satisfaction. It is crucial to investigate the factors that impact hotel guest satisfaction as identified through online review content. This study will examine relevant scholarly literature on online reviews and concentrate on 50 mid-to-high-end health and wellness hotels in China. Python is utilized to crawl the online review database, and a comprehensive analysis is conducted on the semantic keywords and categories present in customer online review statements. Service, traffic, catering, and other variables are identified as determinants of customer satisfaction in mid-to-high-end health and wellness hotels through factor analysis. This article will propose specific measures and recommendations to improve customer satisfaction in health and wellness hotels, attract more customers, and contribute to the sustainable development of the industry.

Activity in Occipito-Temporal Cortex Is Involved in Tool-Use Planning and Contributes to Tool-Related Semantic Neural Representations

Abstract Tool use and language are highly refined human abilities which may show neural commonalities due to their potential reciprocal interaction during evolution. Recent work provided evidence for shared neural resources between tool use and syntax. However, whether activity within the tool-use network also contributes to semantic neural representations of tool nouns remains untested. To this aim, we identified the tool-use planning network with functional magnetic resonance imaging while participants used pliers. The very same participants underwent a semantic priming task including two categories, tool nouns and animal nouns, to highlight the respective underlying networks. With multivariate analyses of the activation neural patterns, we tested whether activity in tool-use brain clusters takes part in the neural representation of tool nouns as compared with animal nouns. The results revealed that word semantic categories were decoded within the left occipito-temporal cortex activated by preparing to use a tool, with similar patterns of brain activity for words within the same category. In addition, in the same area, neural activations for tool nouns were found to be higher than those for animal nouns. These findings suggest that activity in tool-use related brain areas encodes semantic information separately for tool nouns and animal nouns, thus supporting the embodiment of tool-noun processing in the tool-use sensorimotor network.

The scope and limits of fine-grained image and category information in the ventral visual pathway.

Humans can easily abstract incoming visual information into discrete semantic categories. Previous research employing functional MRI (fMRI) in humans has identified cortical organizing principles that allow not only for coarse-scale distinctions such as animate versus inanimate objects but also more fine-grained distinctions at the level of individual objects. This suggests that fMRI carries rather fine-grained information about individual objects. However, most previous work investigating fine-grained category representations either additionally included coarse-scale category comparisons of objects, which confounds fine-grained and coarse-scale distinctions, or only used a single exemplar of each object, which confounds visual and semantic information. To address these challenges, here we used multisession human fMRI (female and male) paired with a broad yet homogenous stimulus class of 48 terrestrial mammals, with 2 exemplars per mammal. Multivariate decoding and representational similarity analysis (RSA) revealed high image-specific reliability in low- and high-level visual regions, indicating stable representational patterns at the image level. In contrast, analyses across exemplars of the same animal yielded only small effects in the lateral occipital complex (LOC), indicating rather subtle category effects in this region. Variance partitioning with a deep neural network and shape model showed that across exemplar effects in EVC were largely explained by low-level visual appearance, while representations in LOC appeared to also contain higher category-specific information. These results suggest that representations typically measured with fMRI are dominated by image-specific visual or coarse-grained category information but indicate that commonly employed fMRI protocols may reveal subtle yet reliable distinctions between individual objects.Significance Statement While it has been suggested that functional MRI (fMRI) responses in ventral visual cortex carry fine-grained information about individual objects, much previous research has confounded fine-grained with coarse-scale category information or only used individual visual exemplars, which potentially confounds semantic and visual object information. Here we address these challenges in a multisession fMRI study where participants viewed a highly homogenous stimulus set of 48 land mammals with 2 exemplars per animal. Our results reveal a strong dominance of image-specific effects and additionally indicate subtle yet reliable category-specific effects in lateral occipital complex, underscoring the capacity of commonly employed fMRI protocols to uncover fine-grained visual information.

PHONETIC MEANS OF EXPRESSING NEGATION IN MODERN BULGARIAN

The object of the current study are the phonetic means that express negation in Contemporary Bulgarian and that are part of the formal inventory of the functional and semantic category of polarity (and more precisely – part of the periphery of the negative polarity). Our interest on this topic was promoted by the lack of detailed research. The main goal here is to present a description of these specific characteristics by paying more attention to the role of intonation and stress in the speech realization of the phrases of interest. To achieve this goal, we have employed a variety of technological resources for rhythmic and melodic analysis, with the help of which we have conducted experimental research on our targeted phrases and provided graphic representation of melody and intensity of pronunciation, as well as spectrographic analysis of speech. Our data shows that stress and intonation are indeed differentiating factors in the meaning of the phonetic explicators we observe. Stress and intonation make t

Multiple meaning as a kind of iterative repetition in the Yakut language

The purpose of the article is to determine the semantic structure and features of the functioning of the means of expressing the proper multiple meaning as one of the subtypes of iterative repetition in the Yakut language. The object of the study is the semantics of an accurate or inaccurate quantitative account of situations. The subject of study is the so-called circumstances of multiplicity or counting complex. Based on the material of the Yakut language, the author, following I. B. Dolinina, adheres to the proposed terminology and suggests considering such cases of repeatability as an independent particular meaning of the iterative type of multiplicity. The main attention in identifying the semantic varieties of this meaning is paid to the use of such quantification features as certainty/uncertainty of the amount of repeatability and its limitation/unlimited. Depending on the correlation of these signs and the intensity of their manifestation, a list of representatives for each of them is determined. The main research method is functional–semantic, which uses the approach "from semantics to its formal expression" when describing linguistic material. Textual examples in the Yakut language are given using the method of morphemic glossing. The scientific novelty of the work lies in the systematization of the means of expression and the allocation of varieties of the actual multiple meaning, which had not previously become the subject of special research in Yakut linguistics. During the analysis of the functioning of adverbs with the semantics of multiplicity or counting, the methods of formation of representatives characteristic of each of the three subtypes of the actual multiple meaning (affixal, by reduplication and combination with the countable element "togul") were established. As an additional conclusion, we can cite the revealed dependence of the definition of the value in question on the temporal form of the predicate (statements with circumstances of multiplicity with a predicate in the form of the present-future and imperfect acquire habitual semantics). The theoretical significance of the results of this study is determined by the contribution to the study of the functional and semantic category of the aspectuality of the Yakut language, and may also be of interest for comparative and typological work in this field. Their practical significance is related to the possibility of their application in lexicographic and translation practice.

Advertisement Blindness in Mobile Social Media Apps

Advertisement blindness is a general term that refers to people’s tendency to automatically and unconsciously ignore advertisements. The phenomenon was originally identified in banner ads, then later in text and native ads on websites. Today, social media is an effective tool for advertisers, yet research investigating users’ interaction habits with social media ads in mobile applications is unexplored. This study expands the ad blindness concept to mobile social media apps, examining its presence and whether target position has an influence. To test these questions, we used a search task where participants browsed a mock scrollable Facebook news feed and identified posts that fit certain semantic categories. The study found that participants had higher accuracy in content posts than in ad posts, also ad avoidance was prevalent in the last third of the news feed. Overall, these results revealed the first evidence of ad blindness on social media mobile applications.

Relationship Between Gestures and Vocabulary in 14-Month-Old Swedish-Learning Children.

In this study, the relationship between gestures and vocabulary size in 177 Swedish-learning 14-month-old children was examined. Gesture use, receptive, and expressive vocabulary were reported by caregivers with the Swedish version of the MacArthur Bates Communicative Developmental Inventory, words and gestures, SECDI-1. Gesture types examined were referential gestures classified as either deictic gestures, conventional gestures, and object actions. A fine-grained analysis of gestures and lexicon was performed. Results show that percentage of gestures used by children significantly predicts percentage of words in their receptive vocabulary. However, looking at gesture type, only use of object actions significantly predicts percentage of words in the receptive vocabulary whereas use of conventional gestures does not. Deictic gestures showed a ceiling effect and were therefore not further used for analysis. The relationship between gesture use and vocabulary size was not impacted by semantic category (food or clothes). Vocabulary in both semantic categories was statistically predicted by object actions in only one semantic category.

Exploring the evolution of the concept of liberty in the U.S. presidential inaugurals

Abstract As daily news unveils, fundamental human values such as equality and liberty can be expressed through various terms influenced by multiple factors, reaching significant levels of disparity, ambiguity and controversy, particularly in specific socio-political contexts. This article employs advanced corpus analysis tools to examine the lexical and semantic configuration of the “right to liberty” as expressed in the U.S. Declaration of Independence and as North American presidents have transmitted it throughout their presidential inaugural addresses. The corpus of speeches is analyzed diachronically to identify the prevalent terms, connotations, and associated semantic categories and observe their differences and evolution. This research provides a comprehensive understanding of the concept of liberty in this context and highlights its startling malleability in American politics.

Similar, not universal: the cognitive dimensions of conceptual prototypes of basic emotions in English and in Polish.

The current study explores the differences in conceptualisation of the prototypical basic emotion lexicalisations (anger, disgust, fear, joy, sadness, surprise) in English and in Polish. Measures of concreteness, imageability and context availability were collected and analysed across the six semantic categories of basic emotions, across different parts of speech and between the self-determined genders of the study participants. The initial results indicate that within these cognitive dimensions the conceptualisations of basic emotions in English and in Polish are only similar on the more general but not the higher levels of conceptualisation. The folk-psychological division between positive and negative emotions and the grammatical parts of speech reveal similar patterns in basic emotion concepts in both Polish and in English. However, on the higher levels of conceptualisations that include specific basic emotion semantic categories and self-identified gender, marked language-specific differences become apparent. Different negative emotions drive the statistical differences in Polish and in English, and the gender effects on the measures of concreteness, imageability and context availability are opposite from one language to the other. In other words, basic emotions may be broadly mutually intelligible, but not exactly the same when communicated across languages and cultures.

An immersive labeling method for large point clouds

3D point clouds, such as those produced by 3D scanners, often require labeling – the accurate classification of each point into structural or semantic categories – before they can be used in their intended application. However, in the absence of fully automated methods, such labeling must be performed manually, which can prove extremely time and labor intensive. To address this we present a virtual reality tool for accelerating and improving the manual labeling of very large 3D point clouds. The labeling tool provides a variety of 3D interactions for efficient viewing, selection and labeling of points using the controllers of consumer VR-kits. The main contribution of our work is a mixed CPU/GPU-based data structure that supports rendering, selection and labeling with immediate visual feedback at high frame rates necessary for a convenient VR experience. Our mixed CPU/GPU data structure supports fluid interaction with very large point clouds in VR, what is not possible with existing continuous level-of-detail rendering algorithms. We evaluate our method with 25 users on tasks involving point clouds of up to 50 million points and find convincing results that support the case for VR-based point cloud labeling.

Remote sensing scene classification based on contextual attention mechanism of lie group space

ABSTRACT High-resolution remote sensing (HRRS) scene classification is based on the semantic information in the image and its contextual spatial correlation, labelling different semantic categories for different HRRS. Convolutional neural networks (CNN) have been widely studied and applied in remote sensing scene classification in recent years. However, most of the existing CNN models focus on the global and high-level semantic features, ignoring the shallower feature information. In addition, complex background information and variable scales lead to a series of problems with large intra-class differences and high inter-class similarities, which also brings challenges to scene classification. To address the above problems and challenges, a scenario classification model based on the contextual spatial attention and channel attention mechanism of Lie Group manifold space learning is proposed in this study. In this model, we fully explore the multi-scale features of the scene (shallower-level and high-level) and propose a novel contextual spatial attention mechanism and channel attention mechanism. Extensive experimentation was carried out on the Union Remote Sensing Image Data Set (URSIS), which improved by 7.13% compared with the classical model. The experimental results showed that compared with other state-of-the-art remote sensing scene classification models, our proposed method has achieved significant improvement in classification accuracy and performance.

Lightweight MRI Brain Tumor Segmentation Enhanced by Hierarchical Feature Fusion.

Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries. For this issue, a lightweight MRI brain tumor segmentation method, enhanced by hierarchical feature fusion (EHFF), is proposed. This method reduces model parameters while improving segmentation performance by integrating hierarchical features. Initially, a fine-grained feature adjustment network is crafted and guided by global contextual information, leading to the establishment of an adaptive feature learning (AFL) module. This module captures the global features of MRI brain tumor images through macro perception and micro focus, adjusting spatial granularity to enhance feature details and reduce computational complexity. Subsequently, a hierarchical feature weighting (HFW) module is constructed. This module extracts multi-scale refined features through multi-level weighting, enhancing the detailed features of spatial positions and alleviating the lack of attention to local position details in macro perception. Finally, a hierarchical feature retention (HFR) module is designed as a supplementary decoder. This module retains, up-samples, and fuses feature maps from each layer, thereby achieving better detail preservation and reconstruction. Experimental results on the BraTS 2021 dataset demonstrate that the proposed method surpasses existing methods. Dice similarity coefficients (DSC) for the three semantic categories ET, TC, and WT are 88.57%, 91.53%, and 93.09%, respectively.

Trends in Mutual Transpositions of Lexical and Grammatical Noun Categories in Russian Language

This study addresses the gap in understanding the lexico-grammatical-structural types of nouns, particularly how lexico-semantic variants gain new connotations during transposition. Drawing upon Babaytseva’s theory of transitivity phenomena and building on Mel’chuk’s text semantic theory, we identify the schemes and mechanisms of transposition relations concerning the lexical and grammatical categories of modern Russian nouns, by analyzing transposition phenomena and their semantic mechanisms in 660 Russian nouns with indefinite denotation and lexical and grammatical status. The research details all possible immigration transpositions of the lexical and grammatical categories of nouns at the minimal context level and their mechanisms. Findings reveal that approximately 27% of the examined nouns carry connotative meanings, encompassing functional-stylistic, cultural-ethnic, and emotional-evaluative facets. Additionally, the transposition of nouns can occur within or between categories. The principal mechanisms of transposition at the syntagmatic level include metaphors and metonymy. Transpositions at the lexical and semantic categories of nouns coincide with several phenomena: changes in the numerical paradigm, phraseologization, connotation, and the unveiling of the noun’s stylistic potential.

Representations of the ethno-political situation in the North Caucasus in the regional media space

The article presents the results of a content analysis of regional media, that was conducted within the framework of a pilot study of the methodology developed by the authors for analysing the problems and prospects for the formation of an all-Russian identity in the North Caucasus. The purpose of the study was to determine the structure and content of representations of the ethno-political situation in the North Caucasus in the media space of the region. The relevance of ethno-political issues in the North Caucasus is due to the fact that this is a frontier region, which, due to its geographical location, ethnic, religious and cultural characteristics, is an arena of various internal and external challenges. Monitoring the ethno-political situation is an important area of ​​research, both in theoretical and practical terms. One of the technologies of such monitoring is the analysis of representations of the ethno-political situation in the political discourse of the region. The political discourse of regional media is a thematically focused, socially conditioned system of news, analytical, and prognostic materials in regional media that builds a political picture of the world. Representations are the content of information messages on media resources of the constituent entities of the Russian Federation in the North Caucasus. The documentary base of the study consisted of 1,656 information messages posted on leading information portals and in non-ethnic media outlets of the constituent entities of the Russian Federation in the North Caucasus Federal District in April–June 2023. Five semantic categories of analysis were defined: 1) the practices used to strengthen the all-Russian identity; 2) politicisation of ethnicity; 3) the influence of foreign diasporas of the North Caucasian peoples; 4) threats and risks in the sphere of humanitarian security; 5) threats and risks in the sphere of socio-political security. The analysis of representations of the ethno-political situation in the political media discourse of the region allowed us to determine what kind of worldview is created and constructed by regional media: 1) prevails positive content about new practices of forming an all-Russian civic identity in the region, new traditions and rituals associated with the heroes of the North Caucasus Military District prevails; 2) potential risks of destabilisation of the socio-political situation in the North Caucasus are associated with the impact of the information war on the population, primarily young people; the growing threats of increased extremist and terrorist activity; problems of religious security in the region, which are presented in the media space in a very fragmentary manner.

Are all negative words alike? Behavioral differences in processing negative words associated and not associated with physical and social pain.

Studies examining whether stimulus valence affects cognitive processing and motor responses yield mixed results, possibly due to treating negative words as a homogeneous category. Words related to pain may hold distinct status because of their relevance to survival. Thus, they offer a unique opportunity to investigate semantic influences on cognitive processing. This study aims to determine if words related to physical and social pain elicit stronger aversion than general negative words by assessing the Affective Compatibility Effect in implicit and explicit tasks. In Experiment 1, 35 participants performed a lexical decision task on 60 positive words and 60 negative words, of which 20 not related to pain, 20 related to physical pain, and 20 related to social pain. Participants held down the central key of a keyboard and released it to press a key far from the screen (avoidance condition) or close to the screen (approach condition) for words. In Experiment 2, 43 participants performed a valence evaluation task on the same words. They held down the central key and released it to press a key close to the screen for positive words and a key far from the screen for negative words (congruent condition), or the opposite (incongruent condition). In Experiment 1, we found faster RTs for social pain-related words compared to other categories. We also found faster RTs in the approach condition than in the avoidance condition, regardless of whether valence or semantics were considered as independent variables. In Experiment 2, we found faster RTs in the congruent condition than in the incongruent condition when semantics was considered as independent variable. We also found an interaction valence*condition, with faster RTs for negative words in the congruent condition than in the incongruent condition when valence was considered as independent variable. Our findings suggest that, notwithstanding pain-related words do not affect aversive behaviors compared to negative, pain-unrelated words, they are processed faster when conveying social pain. This supports the hypothesis that the cognitive system differentiates and responds congruently not only based on general semantic categories, like pain, but also possibly based on nuances within it.

A novel BH3DNet method for identifying pine wilt disease in Masson pine fusing UAS hyperspectral imagery and LiDAR data

Pine Wilt Disease (PWD) is a forest infectious disease that inflicts substantial economic losses to China’s forestry. Its rapid spread and the significant challenges associated with its control make early detection of infected trees crucial for disaster prevention. Unmanned aerial systems (UASs) hyperspectral imaging (HSI) and light detection and ranging (LiDAR) technologies provide high-resolution spectral diagnostic information coupled with intricate three-dimensional structural data, which has potential for fine grained monitoring of PWD. However, how to fuse HSI and LiDAR data to identify the early infected individual trees is still a challenge. This study presents a novel instance segmentation network, BH3DNet, to identify individual trees at different PWD-infected stages by extracting high-level abstract features based on the fusion of drone HSI and LiDAR data. BH3DNet introduces the PointNet++ model as the base network, and incorporates a shared encoder and twin parallel decoders to align semantic category prediction and instance segmentation of individual trees in an end-to-end approach. By applying an enhanced point cloud dataset that fuses drone HSI and LiDAR point cloud data, this model facilitates the identification of PWD infection stages at the individual tree scale. We evaluated the proposed model in a Masson pine forest stand sparsely mixed with broadleaf trees in a variety of infection states ranging from healthy to severely infected by PWD, and compared the performance of the model using the RGB bands, full HSI bands and screened bands as inputs, respectively. BH3DNet achieves an overall accuracy of 89.65 % with a Kappa × 100 of 87.29 for identifying individual trees using screened HSI bands and LiDAR point cloud, significantly outperforming the Mask R-CNN using only HSI data (overall accuracy: 70.81 %, Kappa × 100: 64.16). Moreover, BH3DNet’s accuracy at the early infection stage reaches 83.75 %. It proves that fusing HSI and point cloud data reflects the information of individual trees distribution and infection status, and the BH3DNet is suitable for high-precision monitoring of PWD.

The effect of inter-letter spacing on the N170 during visual word recognition: An event-related potentials experiment

Previous behavioral studies have shown that inter-letter spacing affects visual word recognition and reading. While condensed spacing may hinder the early stages of letter encoding because of increased crowding effects, the impact of expanded inter-letter spacing is still unclear. To examine the electrophysiological signature of inter-letter spacing on visual word recognition, we presented words in three different inter-letter spacing conditions (default, condensed [−1.5 points] or expanded [+1.5 points]) in an event-related potentials go/no-go semantic categorization task. Our focus was on the N170, an event-related potentials component associated with the early encoding of orthographic information, which also is sensitive to crowding effects. Results revealed that the N170 amplitude reached the largest values for the condensed condition than for the default and expanded spacing conditions, which did not differ. While increased crowding impacted the early encoding of orthographic information, extra letter spacing (compared with default spacing) did not. This outcome is consistent with the Modified Receptive Field hypothesis, in which letter receptors adapt their size to cope with letter crowding. These findings reveal that reducing the space between letters more than the default spacing impairs the ability to process written words, whereas slightly expanding the space between letters does not provide any additional benefit.

Quantifying Western Discourses about Technology

In this article, we explore the social impact of technology in contemporary societies using a dataset of multilingual newspapers in English, French, Spanish, Italian, and German. Our observational time covers twenty years (1999-2018). We filter documents using four technological key terms: nuclear, oil, internet, and automation, that we consider as proxies reflecting Smil 2006 taxonomy of technology. Our methodology is formed by a five-step pipeline: Topic modelling (Pachinko Allocation), word embeddings, Ward’s hierarchical cluster analysis, network analysis, and sentiment analysis. We analyze information stability (which we define as low levels of semantic diversity in our data outcomes over time at the individual key term level) and information homogeneity (which we understand as low levels of semantic diversity in our data outcomes across our selection of key terms). We seek to observe to what extent our selection of key terms permeates into press discourses similarly or differently, and whether those discourses fall into semantic categories that could be considered as essential elements of the fabric of contemporary societies (such as finance, education, or politics). Results show, firstly, a consistent overlap of content across newspapers’ semantic fields that could be considered as pillars of society. Secondly, we notice a progressive simplification of information historically, reflecting less polarizing views across countries and, therefore, demonstrating an increasing agreement on technologically related discourses. We interpret these results as an indicator of the rising intrusion of technology in Western, industrialized countries.