Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study Scientific Reports

Based on these divergences, it is safe to conclude that CT do show a syntactic-semantic characteristic significantly distinct from ES. The current study uses several syntactic-semantic features as indices to represent the syntactic-semantic features of each corpus from the perspective of syntactic and semantic subsumptions. For syntactic subsumption, all semantic roles are described with features across three dimensions, viz. Average number of semantic roles per verb (ANPV), average number of semantic roles per sentence (ANPS), and average role length (AL).

In particular, there are some attempts to estimate media bias using automatic tools (Groseclose and Milyo, 2005), and they commonly rely on text similarity and sentiment computation (Gentzkow and Shapiro, 2010; Gentzkow et al. 2006; Lott Jr and Hassett, 2014). In summary, social science research on media bias has yielded extensive and effective methodologies. These methodologies interpret media bias from diverse perspectives, marking significant progress in the realm of media studies. However, these methods usually rely on manual annotation ChatGPT and analysis of the texts, which requires significant manual effort and expertise (Park et al. 2009), thus might be inefficient and subjective. For example, in a quantitative analysis, researchers might devise a codebook with detailed definitions and rules for annotating texts, and then ask coders to read and annotate the corresponding texts (Hamborg et al. 2019). Moreover, the standardization process for text annotation is subjective, as different coders may interpret the same text differently, thus leading to varied annotations.

Tendency of process shifts

It is most often used when comparing statistical models fitted to a dataset to identify the model that best fits the population from which the data were sampled. A comparative analysis helped us to identify the best synthetic tabular data generation method in this context for further data augmentation perspective. Furthermore, it helped to determine if the samples are coming from the same distribution or not. Creating synthetic data is becoming increasingly important due to privacy concerns and data availability.

We also recalculated the correlation between the priming effect with the inconsistent and consistent words using related and unrelated primes given the importance of this result to understanding how semantic processing occurs in the task. Unlike the significance of the ANOVA, that result was highly significant, but it was searched for post-hoc, so it is useful to examine the extent to which it is stable. One of the main distinguishing features of current models of reading aloud is the way word form is stored. In some models, including the Connectionist Dual-Process model (CDP1,2,3,4,5,6; see Fig. 1) and the Dual-Route Cascaded model7, words are stored in a lexicon (“mental dictionary”). There are two of these, one for spoken words (the phonological lexicon) and one for written words (the orthographic lexicon), and they can access but are separate from semantics.

Materials

How subcortical structures are posited in the Granger network of information flow during word processing is another question for future research. Time-frequency plots of the PDC estimates can be found in supplementary material section C. For significant values of PDC, we estimated Cohen’s d effect sizes as shown in Fig.

Secondly, as the dataset used in this study is public, the analysis did not include an examination of patients’ clinical manifestations alongside their microstate manifestations. In our future research, we intend to collaborate with hospitals and other organizations to undertake a more in-depth exploration of the relationship between clinical performance and microstates in SCZ. Based on the experimental results, we found that the highest recognition accuracy of 97.2% is achieved when the EEG segment length is 20 s using KNN classifier. Compared with the published results, the proposed two-template microstate modeling analysis of schizophrenia diagnostic indicators in the identification of SCZ achieved the best results.

Such information includes the title, abstract, journal title and ISBN, publication year, number of pages, author keywords, and so on. To determine the authorship patterns, author information including the identifiers assigned by Scopus, names and affiliations, and their respective countriesFootnote 4 were separately collected. As such, despite copious studies that performed bibliometric analyses on ‘language and linguistics’ research, relatively scant research has been carried out on the research in Asian countries and regional characteristics. By filling in this critical gap in the academic literature, the current study contributes to the existing body of research by studying the ‘language and linguistics’ research of 13 Asian countries, as well as various bibliometric characteristics.

GPT-3.5-turbo, Gemini-1.0-Pro-001 and GPT-4-turbo were also unable to make binary discriminations between sensible and nonsense phrases, with these models consistently judging nonsensical phrases as making sense. Claude-3-Opus made a substantial improvement in binary discrimination of combinatorial phrases but was still significantly worse than human performance. The TWT can be used to understand and assess the limitations of current LLMs, and potentially improve them.

For instance, to assess the development of topic modeling research, Li and Lei (2021) analyzed approximately 1200 articles (2000–2017) regarding productivity, research impact, authorship pattern, geographic reach, and publication venues. Our study reconstructs semantics and observes directionality of semantic change by means of a phylogenetic comparative model (Jäger, 2019; Carling et al., 2021). The data is openly available in the database DiACL (Carling, 2017) and has previously been published in the volume Mouton Atlas of Languages and Cultures (Carling, 2019). The data set includes lexemes that have been coded for cognacy as well as lexical polysemy. For the phylogenetic comparative model applied in this paper, we use lexemes that are coded by etymology, removing loans, which reduces the number of lexemes to 13,060. The data in the original data set from the Semitic family was not coded by etymology and is therefore not used in this paper, reducing the number of families to six.

Meaning pattern of “medical names”

Words occurring more than 300 times were deleted (high frequency words with low information content; there were 18 unique words which represented about 20% of all words in our corpus). These infrequent words accounted for 6234 unique words, about 80% of all unique words for only 20% of all words in the corpus. By removing highly frequent and highly infrequent words, we could assess the core similarities and differences across articles in the corpus. After cleaning, there were about 17% of unique words remaining, representing about 60% of total words pre-cleaning. Due to the diversity, dynamics and fuzziness of customer requirement semantics, it is inevitable to classify them systematically in order to understand and further analyze them.

This results in a distinct syntactic-semantic characteristic of translations that may deviate from both source and target languages, hence an eclecticism. In the above example, the verb in the source text is “been”, but the predicate is changed to the verb “下滑(decline)” in the translation, which comes from the word “slide” in the source text. Transformation in predicates of this kind, known as denominalization, is essentially one of the major factors contributing to the difference in semantic depths of verbs. Through denominalization in the translation process, the notion of “decline” is reintroduced to the predicate verb, which eliminates the incongruency between the lexico-grammatical and semantic layers, resulting in more explicit information.

We only discuss results with effect sizes larger than 0.8, as these are most likely to be replicated during the bootstrap resampling and Granger analyses with varying network sizes (shown in Fig. 4). Additionally, when testing for differences between our two conditions, we constrained our analysis only to the strongest connection (marked by accentuated connections with thick lines in Fig. 4) and corrected for multiple comparisons (number of connections) using Bonferroni’s correction. An exploratory analysis of these connections is reported in supplementary material section D. In general, however, little is mentioned about interactions between brain regions during word comprehension. This lack of knowledge can lead to a misinterpretation of the obtained results, also in view of the lingering ambivalence. In the last decade, the scientific community has been increasingly investigating brain connectivity for a better understanding of cognitive processes39,40.

The third meaning pattern is about the sense of “implementation”, which is typically realized by such verbs as zhiding ‘enact’, guanche ‘implement’, kaizhan ‘carry on’, qianshu ‘sign’, lvxing ‘perform’, and xingshi ‘perform’. They are clustered in that their covarying collexemes in the NP slot are generally biaozhun ‘standard’, zhengce ‘regulation’, and quanli ‘rights’. Consider examples in (5), in which zhengce ‘policy’ is a covarying collexeme of both guanche ‘implement’ in (5a) and zhiding ‘enact’ in (5b) which is rewritten from example (2). The second meaning pattern that could uncover the intrinsic nature of the VP in the construction pertains to the sense of “augmentation”. Members of this cluster chiefly incorporate kuoda ‘expand’, zengjia ‘augment’, zengqiang ‘enhance’, tigao ‘improve’, jiakuai ‘speed up’, jiada ‘increase’, and zengda ‘magnify’.

As the results showed, a stronger connection was observed during the time window of 650–750 ms from the right to left anterior temporal lobe in the alpha band and from the right orbitofrontal to the left anterior temporal lobe in the beta band. Additionally, our exploratory analysis for all time windows showed a consistently stronger network, predominantly in the beta band, during concrete word reading (see supplementary material section D). If the context availability theory would be extended in such a way as to account for connectivity, semantics analysis we hypothesize that abstract and concrete words would be processed in the same connectivity patterns but with differently weighted connections. Abstract and concrete words are processed in bihemispheric, partially overlapping networks with the right hemisphere functioning as a sender and the left hemisphere as a receiver. The generally higher connectivity strength for concrete words can serve as a plausible explanation for the concreteness effect observed in behavioral studies (faster retrieval of concrete words).

In the current study, we used a multivariate, time-varying adaptation of Granger causality on source localized EEG data in order to investigate the spatial, spectral and temporal dynamics of the information flow during single word reading. Such a model is computationally complex as it requires many data ChatGPT App points to be trained. The complexity of a multivariate model is known to be relative to the number of variables to the power of two multiplied by the order of the model (O(m2p)). Therefore, even a small number of variables can dramatically increase the need for more trials to ensure a good fit.

For the exploration of T-universals, CT in Yiyan Corpus are compared with CO in the Lancaster Corpus of Mandarin Chinese (LCMC) (McEnery & Xiao, 2004). LCMC is a million-word balanced corpus of written non-translated original Mandarin Chinese texts, which was also created according to the standard of the Brown Corpus. Hence, it is comparable to the Chinese part of Yiyan Corpus in text quantity and genre. Overall, the research object of the current study is 500 pairs of parallel English-Chinese texts and 500 pairs of comparable CT and CO. All the raw materials have been manually cleaned to meet the needs of annotation and data analysis.

Change in overall representational similarity structure

Accordingly, results pertaining to the relation between a (the concept meaning) and the other meanings b,c,d,e,f are merely estimations. Overview of distribution of transition types, defined by their semantic relation, including no change, distant-metonomy, near-metonomy, metaphor, specialization, generalization, meronomy, and holonomy. You can foun additiona information about ai customer service and artificial intelligence and NLP. Semantic change rate (x) and borrowability (y) of concepts in the data set, based on the borrowability scores of our own data (DiACL) and the World Loanword Database (WOLD).

• The current research constructed the flow network models of POM and SFM, separately.
• When their participants were divided based on the extent to which they displayed an imageability (semantic reliance) effect, the division also divided them on reading speed.
• Defects caused by insufficient product conceptual design are difficult to be remedied in the manufacturing and maintenance stages.
• Participants were instructed to take as long as they needed to arrange the words such that more similar words were closer together and more dissimilar words were further apart.

The shortened role length is the first and most obvious effect, especially for A1 and A2. In the English sentence, the longest semantic role contains 27 words while the longest role in Chinese sentences contains only 9 words. 1, extremely long roles can be attributed to multiple substructures nested within the semantic role, such as A1 in Structure 1 (Fig. 1) in the English sentence, which contains three sub-structures. According to the cognitive load theory (Sweller, 2011), this multi-layered nested structure forces the readers to store the information of all the upper layers in memory while processing information from the bottom layer, which contributes significantly to their cognitive load. In contrast, this multi-layered nested structure is deconstructed and decomposed in translated texts through the divide translation, and the number of sub-structures contained in each semantic role is controlled no greater than 1.

In terms of the relations between symptoms of social support, POM, and SFM among college students, analysis of the current study did not support Hypothesis 2. Even though symptoms of social support have positive associations with both POM and SFM, the edge values of symptom-level associations between social support and meaning in life differed across two flow network models. Specifically, in the flow network of POM, “SIA” (Self-acceptance) has the strongest direct and positive association with POM. The connection between “SbS” (Subjective Support) and POM is the second strongest of all connections.

Learning to Walk in the Wild from Terrain Semantics

The microstate sequence MD, OPS, TCR, and TP features were extracted and statistically analyzed. As can be seen from Figure 7, the differences of MS-seq features mainly focus on microstate C and microstate D no matter which microstate template was used. MD, OPS, and TCR of microstate C increased significantly, while MD, OPS, and TCR of microstate D decreased significantly.

Thus, to gain a better understanding about how international and regional publications changed by country, Fig. 4 depicts the yearly changes of the 13 countries’ productivity, separately in international and regional journals. 4 verified the journal types each country had been concentrating on over the years, and it also corroborated the above explanations. While Japanese research had consistently grown in both international and regional journal publications over the years, most of the productivity increase in China since the 2010s can be ascribed to publishing in international journals. Moreover, the changing patterns observed for Hong Kong, Israeli, Singaporean, and Taiwanese research productivity levels among international journals were almost perfectly synchronized with the changes in the countries’ overall productivity. This was also the case for Indonesian, Iranian, Malaysian, and Saudi Arabian productivity changes in regional journals.

Data collection

In both cases, within each anatomically well-separated ROI, we selected one representative time course by taking that with the highest power among all time courses in the ROI. This method has been shown better to capture the dynamics and phase of the signal which would otherwise become lost when averaging an already smooth distribution of sources78,81. To confirm that this chosen time course is a good representation of the ROI, we manually inspected our ROIs to ensure that they were small enough to have similar activation time courses throughout the region.

Creating a complete ontology for observing physical activity sensors, integrating it with the SSN ontology, and deploying SPARQL queries to query the integrated ontology is a complex task that requires careful design and extensive development. In our designed and developed ontology model, we have integrated the concepts with SSN ontology and for the same, we align the classes and properties in our ontology with SSN’s classes and properties. We use “Observation” class to represent our “PhysicalActivityObservation” and properties like “observedBySensor” with SSN’s properties for sensor observations.

As a result, they seem to have a deeper average semantic depth and a higher level of explicitness than verbs in ES. The results of Mann-Whitney U tests indicate statistically significant results, implying that verbs in CT show a quite pronounced characteristic of explicitation in terms of semantic subsumption. In Table 2, the five indices and the results of the Mann-Whitney U tests indicate that there is a notable divergence between CT and ES, with significant differences for most indices. In the current study, the information content is obtained from the Brown information content database (ic-brown.dat) integrated into NLTK. Like Wu-Palmer Similarity, Lin Similarity also has a value range of [0, 1], where 0 indicates dissimilar and 1 indicates completely similar.

However, this research proved that transitivity could offer a perspective on such a topic for experiential meaning, as the primary mode of metafunctional meaning is inherent in all languages, regardless of the differences in text genre. However, there may be room for shifts between experiential and other metafunctions, leaving space for other systematic functional translation studies to explore. Moreover, as Li (2005, p. 98) suggested long before, the linguistic patterns of translation shifts will only serve for a starting point for investigating the reasons behind such shifts. To penetrate deeply into this area, the ultimate goal of translation shift studies will be the exploration of translation norms motivating shift tendencies, thus the translators’ and even patrons’ overall perception to literary translation in political texts during a certain period. Therefore, more empirical studies are expected for further advancement in this research field. In news articles, media outlets convey their attitudes towards a subject through the contexts surrounding it.

Unlike previous approaches that focus on environment geometry, such as terrain shape and obstacle locations, we focus on environment semantics, such as terrain type (grass, mud, etc.) and contact properties, which provide a complementary set of information useful for off-road environments. For this process, after tokenization and cleaning, each remaining token, \(\tau _i\), in each tweet was scored based upon its cosine similarity to the seed term irma. If a term was not present in the vocabulary, due to minimum word count or other restricting criteria, the term was given a zero, which evaluates to a neutral context relation due to cosine similarity. The mean of all cosine similarity values for tokens \(\tau\) within the tweet, including zeroes, was calculated, and this value was designated as the score for the tweet. In this model, the center word is the single input; the context words are the output. Numerical values must therefore be established based upon a uniformly consistent translation encapsulating context and meaning between words.

The semantic analysis uses two distinct techniques to obtain information from text or corpus of data. The first technique refers to text classification, while the second relates to text extractor. Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language. Semiotics refers to what the word means and also the meaning it evokes or communicates. For example, ‘tea’ refers to a hot beverage, while it also evokes refreshment, alertness, and many other associations. Though the used real MOX2-5 dataset is small, we have shown a direction to use the best data synthetization method to use on real datasets for generating synthetic data in a large scale.

• In computer science, research on social media is extensive (Lazaridou et al. 2020; Liu et al. 2021b; Tahmasbi et al. 2021), but few methods are specifically designed to study media bias (Hamborg et al. 2019).
• In terms of semantic subsumption, the results of both Wu-Palmer Similarity and Lin Similarity in Table 2 indicate that verbs in CT are less similar to their root hypernyms than those in ES.
• Several studies on general word and sentence reading uncovered similar characteristics of the network.

We then train the speed policy using standard supervised learning to predict the human operator’s speed command. As it turns out, the human demonstration is both safe and high-quality, and allows the robot to learn a proper speed choice for different terrains. This paper tackles the challenge of using social media content, especially Twitter, for emergency response use during disasters.

In this paper, we identify several fixed microstate sequences in patients that exhibit significant differences compared to healthy subjects. As previously discussed, the topological structures of microstates B and D exhibit substantial alterations in SCZ patients compared to healthy individuals. Baradits et al. (2020) found the transition from one state to another may represent the sequence of networks that constitute large-scale brain networks. Disturbance in such a structure of network operations may result in disconnection between brain networks, which thereby leads to dysfunctional behavior. In contrast, MD and TCR of microstate A showed the opposite behavior and were only sensitive to the microstate sequence of SCZ patients. The figure illustrates that the microstate distributions of the SCZ patients and the healthy individuals (Healthy Control, HC) exhibit a general similarity, with localized differences in microstates B and D.

However, in English, repetition and similar expressions being used altogether simultaneously in most cases are not the preferable options and tend to be omitted or shifted in one sentence. For the tendency of shifts within one process, Table 5 demonstrates that TT shifts are more likely to occur within the material clauses than in any other type of process. Comparatively, only a few shifts are changes between material-transformative and material-creative clauses, where the Actor or Goal participant “is construed as being brought into existence as the process unfolds” (Halliday and Matthiessen, 2004, p. 184). Further, shifts barely take place within the three non-nuclear process types, similar to the shift pattern among different processes. Our sample consists of a total of 318 items in ST and 305 translations, of which eight entries were quoted two times with the same translations, six were not translated, and one was rendered with two different translations.

Even though the results are inconclusive, neuroimaging studies on healthy subjects also provide a spatial and temporal account of differences in the processing of abstract versus concrete words. A description of the neural pathways during abstract word reading, the manner in which the connectivity patterns develop over the different stages of lexical and semantic processing compared to that of concrete word processing are still debated. We conducted a high-density EEG study on 24 healthy young volunteers using an implicit categorization task. From this, we obtained high spatio-temporal resolution data and, by means of source reconstruction, reduced the effect of signal mixing observed on scalp level.

Conventional microstate analysis usually mixes the EEG signals from the SCZ patients and the healthy individuals to generate a set of microstate templates for subsequent analysis and feature extraction. However, according to neuroimaging studies, SCZ patients exhibit significant difference in brain structure and function, which may result in different topographies of scalp potential. Therefore, employing the same template for modeling both datasets may overlook these differences.

The final limitation of the tools is its failure to make accurate predictions in areas of tissue folding or out of focus imaging, but these are obstacles for any image-based measurement tool (including human annotators) and are avoidable with good technique. (a) In test images the predicted histologic features visually align with what is expected from the H&E images. This shows the models’ utility in discerning novel information regarding ductal features that cannot be detected via staining. The models were used to predict the changes stain distributions (b) and cancer histologic features (c) in murine models with induced cancer. Predictions show significant changes in all stains and features between time points, and quantifies specific features that were not discernable in immunostaining alone.