ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology

Our analysis reveals that regularity in semantic change is not only multifaceted but also a shared property across many different languages. Example clusters of semantic change in dimension-reduced principal components space. Clusters are obtained via a Gaussian mixture model and illustrate regular domain-domain mappings, where attested samples of semantic shift (source meaning → target meaning) are annotated. Predictive accuracy of concreteness, frequency, and valence in inferring directionality of semantic change for each individual language with at least 100 samples.

Kano model as well as its derivatives is an available requirements analysis tool, which distinguishes the different nonlinear relationships between customer requirements fulfillment and customer satisfaction12. ChatGPT App Xu et al.13 presented an analytical Kano model to classify functional requirements into logical groups. This leads to an optimal trade-off between customer classification and producer capability.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Then, given the object, respondents are asked to choose one of the seven parts in each dimension. The closer the position is to a pole, the closer the respondent believes the object is semantically related to the corresponding adjective. The structure of the designed and developed MLP model for classification with 2405 trainable parameters. This section consists of data records, experimental setup, and experimental results. The experimental results elaborate evaluation of the proposed ontology and predictive analysis.

Importantly, the above-mentioned studies employed an explicit concreteness task, i.e. participants were aware of the purpose of the study, which was shown to increase the evaluated concreteness effect9. Our participants, however, were instructed silently to read the words for comprehension and press a button upon seeing the arbitrary category “color” (implicit categorization task) after which the corresponding data was removed. Therefore, we believe that the EEG patterns evoked by our paradigm reflect the processing of words in more natural conditions. 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).

A 3D projection view of a COS-7 cell demonstrates that the tubular ER is largely flat and singly layered. A 3D projection view of the segmented ER structure of the sample in Supplementary Video 13. N.F.L., K.M.S., E.A., P.L., A.A.L. and G.S.K.S. gave advice and edited the article. C.F.K. supervised the research, coordinated and conceptualized the study and wrote the article.

Additionally, microstate quality features based on these differences were proposed. Within the parental leave reform corpus, articles written by female journalists in left-oriented newspapers were 41.1% (95% CI [37.9–44.4]) neutral, 32.8% (95% CI [29.7–35.9]) negative, 26.1% (95% CI [23.3–29]) positive. Articles written by female journalists in right-oriented newspapers were 42.3% (95% CI [38.9–45.8]) neutral, 31% (95% CI [27.8–34.3]) negative, and 26.7% (95% CI [23.7–29.7]) positive. Articles written by male journalists in left-oriented newspapers were 42.7% (95% CI [36.9–48.5]) neutral, 31% (95% CI [25.7–36.4]) negative, and 26.3% (95% CI [21.4–31.5]) positive. Articles written by male journalists in right-oriented newspapers were 45.5% (95% CI [42.6–48.5]) neutral, 29.3% (95% CI [26.6–32]) negative, and 25.2% (95% CI [22.8–27.7]) positive. In the cross-sectional symptom network of self-acceptance and social support, we found that the symptom “SIA” (Self-acceptance) served as the bridge symptom.

Is Your Data AI-Ready?

To gain mechanistic insight, we turned to the changes in pairwise representational similarity (measured by the difference between within pair similarity at the final and initial Similarity-Based Word Arrangement Task (SWAT) assessments; Fig. 3), which provides a more direct measurement of how the semantic representations of our word set change across learning; Fig. To capture the event selection biases of different media outlets, we employ Truncated SVD (Halko et al. 2011) on the “media-event” matrix to generate media embeddings. In particular, LSA (Deerwester et al. 1990) applies Truncated SVD to the “document-word” matrix to capture the underlying topic-based semantic relationships between text documents and words. LSA assumes that a document tends to use relevant words when it talks about a particular topic and obtains the vector representation for each document in a latent topic space, where documents talking about similar topics are located near each other. By analogizing media outlets and events with documents and words, we can naturally apply Truncate SVD to explore media bias in the event selection process.

In particular, we demonstrate how to train neural networks using either the Continuous Bag-of-Words or the Skip-Gram model. Preprocessing steps such as removing stop words and subsampling frequent words in the tweet corpus helped reduce the number of relevant tokens to enhance retrieval of appropriate tweets. The word window argument sets the maximum distance on either side of a center word where neighboring words are considered for context. For example, a word window of 3 would look both three words ahead and behind the center word to include any words found in the context part of the neural network construction. Though words outside of this window are considered to be part of the same document, words within the same document will share context words where the word windows overlap. For CBOW, these words are the input values for the neural network, and for Skip-Gram, these words are the output values.

It may be that BERT Tone is not the ideal tool to detect sentiment differences in traditional media articles. Alternatively, social media may have been the place where heated or emotive discussions happened. In Norway, for example, it was a social media campaign that catalysed a countermobilization in response to a 2018 parental leave reform33. Finally, we analysed news articles from a relatively restricted time period, and future work could compare the difference between control and parental leave articles prior to 2019, with the difference between control and parental leave articles after 2019. Very recently, studies have specifically investigated connectivity differences between abstract and concrete single word reading45,46. Even though these studies relied on very different methods compared to ours, the results were more or less converging.

UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

For example, the manner that decisions are made in business (i.e., dahui ‘meeting’ and huiyi ‘meeting’), the means that agreements are reached (i.e., hetong ‘contract’ and xieyi ‘agreement’), etc. Concerning the covarying collexemes in the VP slot, these lexical items in the NP slot frequently cooccur with verbs such as zhaokai ‘convene’, qianshu ‘sign’, and lvxing ‘perform’. Lexical items denoting the meaning pattern of “business” and their covarying collexemes in the VP slot are exemplified by (13a) and (13b).

Further details regarding the dataset can be accessed at the repository and its corresponding article (Olejarczyk and Jernajczyk, 2017). The study validated the new features on the Warsaw database for SCZ recognition to assess the effectiveness of microstate semantic features and quality features. The results demonstrated that the method proposed in this paper achieves optimal SCZ recognition accuracy. The study proposed a dual-template microstate construction strategy to effectively capture differences in microstates between SCZ patients and the healthy group.

Following Roark et al., we call the resulting surprisal predictions Syntactic Surprisal. For word predictions, on the other hand, the context includes not only that contributed by the preceding words, but also the structure up to, but not including, the generation of the word. Again following Roark et al. (2009), we call the surprisal values computed in this way Lexical Surprisal.

Accuracy has dropped greatly for both, but notice how small the gap between the models is! Our LSA model is able to capture about as much information from our test data as our standard model did, with less than half the dimensions! Since this is a multi-label classification it would be best to visualise this with a confusion matrix (Figure 14). Our results look significantly better when you consider the random classification probability given 20 news categories.

As the publishers indicated, both the major international and regional journals are mixed up together. Whereas several countries published articles evenly in international journals, a few tended to dominate regional journals. Particularly, in some regional journals, more than 95% of the articles originate from one country (i.e., Iran for Language Related Research, Japan for English Linguistics, and South Korea for Communication Sciences and Disorders).

We did not attempt to create a large network with many regions of interest even though other regions reported in the literature could have potentially been of interest to our study. We also do not recommend analyzing GC with only a portion of all ROIs to decrease computational complexity. As all causal factors need to be incorporated in the model, Granger Causality may produce misleading results when the true relationship involves more variables than those that have been selected107. In our case, the network constituted all regions with either common or differential neural activation between our two paradigms. We can, however, not preclude that not all ROIs playing a causal role in neural dynamics have been successfully identified, as some may have been downregulated during task performance37.

• Such a lexicogrammatical system is greatly influenced by other essential strata in SFL, i.e., the context that links lexicogrammar with semantics.
• Since the news articles considered in this work are written in Italian, we used a BERT tokenizer to pre-process the news articles and a BERT model to encode them; both pre-trained on a corpus including only Italian documents.
• Namely, the optimal topic quantity K is determined when Perplexity-AverKL is the smallest.

In the customer requirements analysis stage, customers have established a preliminary perceptual cognition when they interact with product function and structure. If customers are placed in the analogical reasoning environment, the product feedback information is processed ulteriorly in their brain according to the given analogical stimuli, invisible and creative product requirements existing in the customer’s brain can be discovered. Meanwhile, it is inevitable that analogical stimuli results are not always positive and can be incorrect in the far-domain stimuli environment especially.

Furthermore, the overall productivity of Indonesia, Iran, Malaysia, and Saudi Arabia had grown intensely in recent years; in fact, its recent productivity almost nearly accounts for the corresponding countries’ entire scale of research for the past 22 years. Nederhof (2011) examined the bibliometric perspective of ‘language and linguistics’ research and ‘literature’ research in The Netherlands. Specifically, the study divided the sampled studies into two groups depending on their target audiences, domestic or international. Then, for each group, which languages were studied and in which languages each was written formed the basis of the study’s evaluation. However, the study was comprised of old sample data, namely, publications from 1982 to 1991.

Moreover, ‘Betweenness Centrality’ gauges how many times a given country was located in the shortest path of another collaboration relationship and represents the magnitude to which the country can control the information flow in its own collaboration network. The higher the centrality, the more power that country had over its information flow (Lee, 2020). Table 3 also depicted the number of internationally co-authored articles and the most frequent collaborating countries. China and Japan published the largest number of internationally co-authored articles and collaborated with the most diverse range of countries.

As discussed earlier, semantic analysis is a vital component of any automated ticketing support. It understands the text within each ticket, filters it based on the context, and directs the tickets to the right person or department (IT help desk, legal or sales department, etc.). These chatbots act as semantic analysis tools that are enabled with keyword recognition and conversational capabilities. These tools help resolve customer problems in minimal time, thereby increasing customer satisfaction.

Two word test phrase generation and human rating collection

Significantly attracted instances to the NP de VP construction demonstrate that most instances belong to the former kind while only a rather small proportion of these instances belong to the latter kind. Although researchers as such have uncovered the semantic relationship between the NP and the VP in the construction, they do not count on the typical meanings that these elements could denote or how these meanings could be patterned. The datasets generated during and/or analyzed during the current study are not publicly available but will be made available by the corresponding author upon reasonable request. 7 also depicts three sub-groups of relatively well-connected countries, discovered by the modularity-based community detection technique (Newman, 2006).

Initially, an analogy-inspired verbal protocol analysis experiment is implemented to obtain detailed customer requirements descriptions of elevator. Then, full connection layers and a softmax layer are added to the output-end of Chinese bidirectional encoder representations from Transformers (BERT) pre-training language model. The above deep transfer model is utilized to realize the customer requirements classification among functional domain, behavioral domain and structural domain in the customer requirement descriptions of elevator by fine-tuning training. Moreover, the ILDA is adopted to mine the functional customer requirements that can represent customer intention maximally. Finally, an effective accuracy of customer requirements classification is acquired by using the BERT deep transfer model.

To that end, journal articles about ‘language and linguistics’ published by the other 28 Asian countriesFootnote 5 were searched for in the same way as our target articles were. That is, using Scopus’ detailed search, articles related to the field published by the other 28 countries from 2000 to 2021 were collected, after excluding predatory journals. Figures 1 and 2 detail the comparison of the semantics analysis ‘language and linguistics’ publications by country. To evaluate recent trends comprehensively, the inclusive years for defining our sample articles were 2000 to 2021. Especially for journals that were not continuously indexed in the sources within the past 22 years, the articles published during the period when each journal was indexed in Scopus were only collected to sample quality articles.

This is also complicated by the fact that multiple cognitive mechanisms (e.g., metaphor, metonymy) might be at work in the historical development of semantic change. Compared with the bias in news articles, event selection bias is more obscure, as only events of interest to the media are reported in the final articles, while events deliberately ignored by the media remain invisible to the public. Similar to the co-occurrence relationship between words mentioned earlier, two media outlets that frequently select and report on the same events should exhibit similar biases in event selection, as two words that occur frequently in the same contexts have similar semantics. Therefore, we refer to Latent Semantic Analysis (LSA (Deerwester et al. 1990)) and generate vector representation (i.e., media embedding) for each media via truncated singular value decomposition (Truncated SVD (Halko et al. 2011)). Essentially, a media embedding encodes the distribution of the events that a media outlet tends to report on.

This deficiency has resulted in slow progress in the semantic analysis of translated texts. The other hurdle arises from the difficulty with extracting semantic features from texts across various corpora while minimizing the interference from different topics and content within these texts. To overcome these hurdles, the current study draws upon the insights from two natural language processing tasks and employs an approach driven by shallow semantic analysis, viz. Even though there is no consensus among linguists as to what exactly constitutes a concrete or abstract word, neuroscientists found clear evidence of a “concreteness” effect. This can, for instance, be seen in patients with language impairments due to brain injury or developmental disorder who are capable of perceiving one category better than another.

Specifically, these clusters, except for clusters 1 and 9 could not capture a common sense, generally denote senses of “cognition”, “augmentation”, “implementation”, “achievement”, “establishment”, and “report”, which are to be detailed in the following paragraphs of this section. This study carried out an in-depth analysis of research trends over the past two decades in Asian ‘language and linguistics’, focusing particularly on 13 target Asian countries. As a prerequisite for conducting this analysis, it was imperative to determine whether the research produced by the target 13 countries is truly a representative sample of Asian ‘language and languages’ research. Thus, this paper compared the research productivity of our 13 selected countries with 28 other Asian countries.

“These design principles could help us prevent the disparate impacts that AI could have on different groups,” explained Naghizaeh. Naghizadeh recently joined UC San Diego as part of the Designing Just Futures hiring initiative, which draws faculty who are dedicated to designing a more equitable society through human-centered design. “It was the values outlined in the initiative—including interdisciplinary research, teaching and outreach activities that emphasize societal impact and advancing diversity that resonated with me,” said Naghizadeh on what attracted her to the role.

When authoring an XLSForm, the user must insert one extra column in the spreadsheet and fill it with HXL hashtags identifying the type of information in each column. The form builder ChatGPT also provides an intuitive way to relate a hashtag to an instrument’s field. To better represent collected data, fields in research forms can be annotated with semantic vocabularies.

Table 1 shows the full list of ERKs, with the RelFreq column indicating the ratio of the number of times they appear in the text to the total number of news articles. From the Consumer Confidence Climate survey, we extracted economic keywords that were recurring in the survey’s questions. We then extended this list by adding other relevant keywords that matched the economic literature and the independent assessment of three economics experts. The inclusion of external experts to validate the selection of keywords is aligned with the methodology used in similar studies39. These keywords provide insight into the concerns and priorities of Italian society. From the basic necessities of home and rent to the complexities of the economy and politics, these words refer to some of the challenges and opportunities individuals and institutions face.

Computer-aided interrogation of medical imaging is being applied to accelerate and improve diagnosis in human patients1,2,3,4. Similarly, deep learning technologies can greatly improve analyses in animal disease models which require the measurement of disease progression in large numbers of tissue samples resulting either from pharmacological or genetic manipulations. B) The tendency of shifts within one process is that there is a large proportion of shifts occurring within material clauses. Among them, more shifts can be seen among material-transformative clauses, which can lead to configurational changes in various categories of participants and different ways of interpreting experiential meaning. The tendency of shifts being more among material-transformative clauses than between material-creative and -transformative clauses implies that the material domain of meaning in the ST does not change too much in translation.

Data are collected during the interviewer’s interaction with the research participant through the form available in the KoBoToolbox system. After that, the Processor extracts, transforms, and processes the data for later storage in the REDCap database. The Processor also monitors possible changes in the data through the Data Entry Trigger, offering flexibility for new processing while enhancing the security of the research data. The Ontology Service guarantees semantic interoperability between the applications and formularies that use different versions of the same ontology or even between different ontologies by maintaining the history of changes and mapping the concepts from one ontology version to another. This service accepts annotated files with an ontology version that can be converted to an older or newer version of the same ontology and annotated files to be converted to a correlated ontology (in the latter case, a prior mapping of ontology properties as metadata is required).

This was not because our task was too noisy, because a significant effect was found on the N400 window with the Bayes Factor result suggesting there was very strong evidence supporting the correlation, suggesting the task is reliable enough to pick up individual differences. Thus, at least for early processing, we can find no evidence for meaningful individual differences in early semantic processing affecting inconsistent but not consistent words. We examined the difference between the size of the priming effect with consistent and inconsistent words using the two different prime groups.

The red cluster shows some variability where both its source and target senses are mainly verbs. Interestingly we observed that the shifts in this cluster are pejorative, with a clear drop in valence (e.g., from neutral to negative sentiment). In principle, we can tackle the target inference problem described by calculating the probability of all possible meanings as candidate target sj for a given source meaning si. In practice, the possible set of meanings can be very large, so we operationalize this inference task by using models to choose an appropriate target meaning from a small yet controlled pool of alternative meanings for a given source meaning. With the hypotheses described, we develop the following simple models to predict the direction of semantic change that occurs between senses si and sj.

Another assumption that might make a difference to the pattern of data is what windows were chosen to analyze the data with. In this respect, unlike the N1 and P2 windows, where an automatic window finding procedure and the same sized windows were used, the duration of the N400 window was different (100 ms) and was not as wide as many studies. Given this, we manipulated the size of the window around the most negative point on the N400 (402 ms) in 25 ms blocks.