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

The contents that are reported are generally about news media, issues or questions, opinions, which constitute the covarying collexemes in the NP slot to attribute the meaning pattern of “report”. This will be instantiated by examples in (8), in which contents of the reporting verbs baodao ‘report’ and tichu ‘propose’ are zhongdong wenti ‘middle east issues’ as shown in (8a) and wenti ‘question’ as shown in (8b) respectively. The first typical meaning pattern of the NP de VP construction pairs the lexical items in the NP slot that denote the sense of “regulation” and those in the VP slot that denote the sense of “implementation”. The former is briefly realized by such lexical items as zhengce ‘policy’, zhidu ‘regulation’, gongneng ‘function’, and zuoyong ‘function’, and the latter is generally realized by verbs such as zhiding ‘enact’, guanche ‘implement’, lvxing ‘perform’, and xingshi ‘perform’. This pairing could be exemplified by the sentence in (2), in which zhengce ‘policy’ which expresses the sense of “regulations” significantly covaries with zhiding ‘enact’ which expresses the sense of “implementation”. Implementation of the covarying collexeme analysis produced the results that are presented in Table 2.

The comparable calculation was then made using tool predictions on adjacent H&E stained tissue sections. For the tool prediction, ADM and dysplasia predictions were grouped into the panK stain because pan-keratin immunostaining does not distinguish ADM and neoplastic tissues. Because stain area is specific and more biologically targeted than the rough annotations that incorporate ChatGPT empty lumens and mislabeled features, the models’ immunostain Spearman correlation scores are much more reflective of their overall accuracy and sensitivity. When the prediction masks are compared qualitatively and quantitatively to the stained images, the models are able to predict the spatial localization of the immunostaining (Fig. 2a and Supplemental Fig. 3).

Data collection is one of the most crucial aspects of any research project, as it can determine its success or failure. In order to avoid this, it is essential to implement a reliable data capture system, in addition to employing a trained data collector1. All implementations of model estimation, validation and statistical analysis were done using custom-written scripts or scripts modified from available MATLAB packages such as the MVGC toolbox84, the SIFT toolbox85, the WOSSPA package86 and the ARfit package. Note that, in the current definition of PDC, every connection πij is the information flow from region j to i and is normalized by the strength of all incoming connections to the i-region, which produces stable and interpretable results51,83. Topographic maps of the effect of Prime Type (related minus unrelated) for consistent and inconsistent words on the N1 and P2 and for all words on the N400.

Tendency of process shifts

Due to experimental time constraints, we were unable to include additional words beyond those in the to-be-learned pairs in our SWAT protocol that would enrich our measurement of semantic space and serve as a null hypothesis test, as they should undergo little or no representational change. These design constraints may have resulted in a truncated range of semantic relatedness across all pairs. Recent work has shown that the effect of semantic relatedness may depend on the range of strength of association across the entire stimulus set15, so future work may opt to choose a broader range to determine if this impacts the results. Other work has suggested that prior knowledge plays a crucial role in the symmetry of concept representations after learning39,41,52.

For instance, we may use consumer surveys in conjunction with our methods to gain a more comprehensive understanding of the market. However, a plot demonstrating the distribution of change rates for all meanings (Supplementary Figure S6) indicates a large variation by meanings for which the reconstruction is based on fewer than 15 datapoints. These meanings, including their loss rates, are given in Supplementary Table S7, with their involved core concepts in Supplementary Table S8. As we can see in these files, the meanings range from almost complete stability (0.11 probability to change) to almost complete instability (0.99 probability to change).

Besides ontologies, simple standards such as the Humanitarian Exchange Language (HXL) help speed up data processing and create interoperability across data sources. HXL is a project by the United Nations Office for the Coordination of Humanitarian Affairs to coordinate disaster response using semantic web technologies. It uses simple marking through hashtags and aims to contribute to automating processes to improve information flow to decision-makers19. In addition to the above-mentioned analysis, we were also curious to see how this variability would differ if a sample smaller than the whole dataset were used. As mentioned in the pre-processing section, we had an average of 550 trials per subject for both conditions, meaning that around 270 trials per condition were used to estimate the autoregressive parameters. We repeated the bootstrap analysis for 15, 25, …, to 95% of all available trials to estimate the variability.

According to the dual memory theory56 this process occurs by creating an episodic “cue memory” where the cue and target are encoded in the context of a retrieval task, whereas restudying creates a bidirectional association. The transfer-appropriate processing account32 posits that the benefit of testing stems from greater episodic contextual similarity between retrieval practice and the final test, relative to restudying. Showing that pairs are drawn together, however, does not show how they become more similar. It is possible that both items within a pair change symmetrically to become more similar to each other; alternatively, one item may remain relatively stable while the other changes. Extant literature investigating these potential hypotheses39,40,52,53,54 tends to compare outcome measures like accuracy and reaction times when probing pairs in the forward (i.e. A→B) vs backward (i.e. B→A) directions. These measures, while useful for answering some questions, are less effective for exploring associative asymmetry of changes in semantic space, as they cannot compare the overall representations of concepts.

Models are evaluated based on randomly chosen and similarity-adjusted target candidates. Figure 4 summarizes the results from target inference with aggregated data across languages. The gray bars indicate predictive accuracy of the similarity and analogy models evaluated respectively on random selections of alternative target candidates. In each of these test cases, a source meaning is given, and models are applied to infer its ground-truth target meaning among four alternative candidate meanings (with chance accuracy being 20%). The black bars indicate the same except on similarity-adjusted selections of alternative target candidates. We evaluate the proposed models that infer the directionality and source-target mapping of semantic change against DatSemShift using data in aggregate and from individual languages that contain at least 100 attested cases of semantic change as recorded in the database.

Supplementary Information

Compared to GPT-4-turbo and Gemini-1.0-Pro-001, its ability to correctly detect nonsense and sensible phrases is further improved (higher correct rejection rate and lower false alarm rate). However, it is more informative to take LLM ratings of each individual phrase and test the probability that its rating came from the same distribution as the human responses to that phrase. We conducted a series of phrase-wise statistical tests to compare each LLM to human meaningfulness ratings. Note that one natural solution to this problem is topic modeling, which you should have a look into if you haven’t done it yet. In this article we will explore other tangentially related conceptualizations of this task. This will open a browser window where you can freely explore the semantic relations in the corpus.

To our best knowledge, the present research is the first research to explore the symptom-level relations between self-acceptance, social support, and meaning in life, providing fresh insights into understanding the complex associations between the aforementioned variables. First, the current study uses a cross-sectional design, not allowing causal conclusions to be drawn. Thus, future studies can employ longitudinal studies to explore the complex causal relations between the symptoms of these variables.

A separate UNet model was trained for each annotated ductal tissue type (normal acinar, ADM, and Dysplasia)19. To make each model specific to its respective tissue type, each model’s training set was made to incorporate small portions of the other tissue types as negative controls. The training sets were made using 80% of the total relevant tissue tiles and ~ 5–10% of the total of other tissue tiles. Tiles were augmented during training with flips, rotations, and shears to overcome the small dataset size. Training for all three models lasted for 50 epochs, used a batch size of 32 tiles and had a learning rate of 7e-4, implementing the Adam optimizer.

Yet in Germany the divide runs through the two main parties of the centre-left and the centre-right. The good news from Europe is that, in most countries, a majority (or, at least, a significant plurality) of citizens supports the idea of increasing the supply of weapons and ammunition to Ukraine. In only three countries – Greece, Bulgaria, and Italy – is there a majority of people opposed to this initiative. It may therefore be possible for European and Ukrainian leaders to agree to send more military aid.

Using less than 30 minutes of human demonstration data, the framework learns to adjust the speed and gait of the robot based on the perceived semantics of the environment. One limitation of our framework is that it only adjusts locomotion skills for standard walking and does not support more agile behaviors such as jumping, which can be essential for traversing more difficult terrains with gaps or hurdles. Another limitation is that our framework currently requires manual steering commands to follow a desired path and reach the goal.

In future work, we plan to look into a deeper integration of high-level skill policy with the low-level controller for more agile behaviors, and incorporate navigation and path planning into the framework so that the robot can operate fully autonomously in challenging off-road environments. The hierarchical framework consists of a high-level skill policy and a low level motor controller. The skill policy selects a locomotion skill based on camera images, and the motor controller converts the selected skill into motor commands. The high-level skill policy is further decomposed into a learned speed policy and a heuristic-based gait selector. To decide a skill, the speed policy first computes the desired forward speed, based on the semantic information from the onboard RGB camera. For energy efficiency and robustness, quadrupedal robots usually select a different gait for each speed, so we designed the gait selector to compute a desired gait based on the forward speed.

For instance, the original text “物必先腐, 而后虫生” (Things must rot first, and then insects would grow) (Xi, 2014a, p. 16) consists of two material clauses. While in the translation “Worms can only grow in something rotten” (Xi, 2014b, p. 17), one of the material clauses was rendered as “something rotten”, functioning as a circumstance in the whole clause following the proposition “in”. A The distribution of the number ChatGPT App of authors per article and b the yearly distribution of the number of authors per article. Finally, our research highlights the importance of media communication in shaping public opinion and influencing consumer behavior. As such, it is crucial for businesses and policymakers to be aware of the potential impact of media on consumer confidence and take appropriate measures to mitigate any negative effects.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

You can foun additiona information about ai customer service and artificial intelligence and NLP. It indicates the TT is inclined to simplify certain experiences to make the language acceptable in English, by omitting unnecessary processes or condensing several processes into one. Such a tendency can bring about a change in the intensiveness of experiential meaning realized in clauses. First, the tendency of participant and circumstance nominalization is ascribed to the different language habits. English prioritizes the use of nouns and nominal groups to present an experience of the world, while in Chinese verbs and verbal phrases are always the first choices.

In the study of crisis informatics, social media can function as part of the toolset used in crisis preparation and emergency preparedness17; and for response and communication during the event18,19,20. Poblet et al. describe the roles of social media separated across distinct data types as a crowdsourced, multi-tiered tool18. Social media can be used as a source of data, because it can function as the product of the “crowd as a sensor”18 by providing location data or other metadata that can be correlated with known datasets “…especially in the mitigation and preparedness phases [of disaster management]”18. Of particular interest is the “crowd as a reporter”18, wherein social media users report “first-hand information on events as they are unfolding” to a specific social media platform18. Predictive accuracy of the analogy model and the similarity model in inferring target meanings of semantic change.

• The hierarchical framework consists of a high-level skill policy and a low level motor controller.
• First, the tendency of the large proportion of shifts within the material clause is connected to the levels of delicacy.
• The former story focused on the characters’ bodily movements, including single-limb and whole-body actions performed in isolation or during interactions with objects and other people (e.g., Johnny ran quickly to the place where the clown was jumping and dancing).

Furthermore, the availability of high-quality, diverse, and sufficiently large datasets for training and evaluating algorithms remains a bottleneck in research and development. To address this challenge, we present a proof-of-concept study that utilizes the MOX2-5 activity sensor8 to generate a comprehensive dataset for physical activity monitoring. Synthetic datasets offer a promising solution to the problem of scarcity of real-world data, giving researchers and practitioners access to a wider range of scenarios and activities. This study not only releases the MOX2-5 dataset to the public but also showcases the viability and efficacy of synthetic datasets in enhancing the accessibility of training data for activity recognition models.

Curb Your Hallucination: Open Source Vector Search for AI

Harald Hammarström has performed the probabilistic Markov model estimates of ancestral states and extracted gain and loss rates of meanings. Matrix of semantic relations coded in the data, including type, class, example and key used for coders. The data consists of lexeme lists in languages, which have been compiled using a concept list (Poornima and Good, 2010; List et al., 2016; Dellert and Buch, 2018). In this specific case, the lists constitute of culture terms reflecting cultural objects and activities of high age and importance, which have been in daily use at least since the Chalcolithic 7,000–5000 years BP (Carling, 2016, 2019; Carling et al., 2019a,b).

The sets were matched on concreteness, frequency, length of cue and target, word2vec and LSA cosine similarity measures. Each set of words was randomly assigned to either the test or restudy condition independently for each participant. Memorability of the pairs of words was measured post-hoc by computing the average recall accuracy of the pair across participants; there was a range of accuracy across pairs, ranging from 91% (GENDER- FEMALE) to 5% (CHILDREN – BIRD) of participants recalling any given pair (Supplementary Fig. 2). Despite the range of memorability across all pairs, there was no statistically significant difference in mean memorability across the two sets of words pairs (see Supplementary Note 1 for more details). One potential limitation of our work comes from our use of pairwise similarity metrics derived indirectly via imputation. If our imputation method was unreliable, it might cast doubt on our behavioral representational change results.

Word embedding is the generic term for assigning numeric values to words, with the mathematical operations between those numeric values implying some semantic or syntactic relevance6. These numeric values are assigned based on a computer generated algebraic representation of observed contextual relationships. Such representations are semantics analysis critical in designating syntactic intent in a manner such that it is capable of being interpreted by a computer. To provide this function within such a model, word embeddings must be created based upon an algorithmic approximation of natural language. Without such a framework, words would lack the necessary connections to each other.

Correlations between tasks

What is more, that number increased to 6290 articles in 2021, the sample’s final year. The scale of the 2021 publications is the result of a 16.9% annual growth in productivity. That is, except the two years (2002 and 2004) for which there was a decrease, the number of all the Asian ‘language and linguistics’ publications has grown about 17% each year on average.

Abstract thought and verbal information transfer are two innate cognitive functions of human beings. However, how our brains understand abstract language and how the underlying neural pathways and systems differ from those involved in processing concrete, tangible concepts is not yet clear1. Abstract words refer to notions which cannot be touched or sensed, which is why their processing cannot merely rely on the motor and perceptual systems. Experimental data coming from behavioral, neuroimaging (fMRI) and electrophysiological (EEG, MEG) studies of both healthy individuals2 and patients suffering from brain disorders3,4,5 show that abstract and concrete words are likely to be processed differently. For example, concrete words have been shown to be learned at an earlier stage of life and understood and retrieved faster1. Since it is still unclear how exactly the processes underlying this effect work, various methods and tools have been employed to study them.

Therefore, literary texts and their TT in political texts can still not eliminate the texture of political texts themselves to a certain degree. The process shifts we analyzed here are those among different process types, within one process type, and the expansion and compression of process types, as in Section “Types of transitivity shifts for comparative analysis”. Regarding the overall trend of how various process types construct the experiential meaning in ST and TT, Tables 2, 3 illustrate that among the six process types, the material clause is used the most, accounting for over 50% of the ST and TT. Relational process ranks the second and mental process the third, which proved again Halliday and Matthiessen (2004) statement that material, mental, and relational clauses constitute the three principal types that occur more often in many discourses.

The NP de VP construction in modern Chinese has long been investigated in academia. It has been discussed no less frequently than other Chinese expressions such as taishang zuo zhe zhuxituan ‘on the platform sits the presidium’ and wangmian qisui si le fuqin ‘Wang Mian’s father died when he was seven’. The NP de VP construction is a grammatical pattern in which the NP in the first slot represents a nominal phrase and the VP in the third slot represents a verbal phrase; the two types of phrases are threaded by the possessive particle de. This construction could be typically instantiated by mubiao de shixianFootnote 1 ‘realization of target’ in example (1), in which mubiao ‘target’ is a nominal phrase or NP and shixian ‘realize’ is a verbal phrase or VP. The construction itself is generally regarded as a nominal phrase as a whole (e.g., Guo, 2000; Lu, 2003; Jin, 2019; Yang and Xiong, 2021; Li, 2021, etc.).

In addition to the abovementioned meaning patterns that verbs in the VP slot of the construction could denote, there are also some other verbs that could not be patterned into a single meaning group. However, they are also equally important to represent the typical meanings of lexical items in the VP slot. Those verbs mainly include zhaokai ‘convene’, chengdan ‘undertake’, yingxiang ‘influence’, etc. Lexical items in the NP slot generally denote senses of organization or system (e.g., tizhi ‘regulation’, tixi ‘system’, etc.), personal traits (e.g., zeren ‘responsibility’, zhenxin ‘sincerity’), etc. However, this way of semantically patterning the lexical items in both slots of the NP de VP construction will unavoidably impose researchers’ subjectivity into the meaning patterns; furthermore, the results are usually problematic and unsuccessful at length (cf. Gries and Stefanowitsch, 2010).

• Moreover, the P-RSF metric offered better classification than analyses based on the texts’ overall semantic structure (also obtained via GloVe).
• The computing resources and the related technical support used for this work were provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff.
• To nowcast CCI indexes, we trained a neural network that took the BERT encoding of the current week and the last available CCI index score (of the previous month) as input.
• If a media focuses on a topic, it will tend to report events related to that topic and otherwise ignore them.

The results denote that setting more topic quantity does not lead to better model performance due to worse measurable indicator values. On the one hand, the number of types of main functional customer requirements for conceptual design of elevator is not too large. Finally, five kinds of functional requirements and corresponding keywords as well as their weight coefficients in the topic-word distribution are shown in the Table 3. It can be seen from the table that customer requirements from topic1 to topic 5 are mainly aimed at the elevator operating state, elevator intelligence, elevator internal environment and elevator stability optimization and elevator sightseeing.