Irrelevant Terms Research Articles

The seductive allure of technical language and its effect on covid-19 vaccine beliefs and intentions

Previous research has demonstrated a ‘seductive allure’ of technical or reductive language such that bad (e.g., circular) explanations are judged better when irrelevant technical terms are included. We aimed to explore if such an effect was observable in relation to a covid-19 vaccinations and if this subsequently affected behavioural intentions to take up a covid-19 vaccine. Using a between subjects design we presented participants (N = 996) with one of four possible types of vignette that explained how covid-19 vaccination and herd immunity works. The explanations varied along two factors: (1) Quality, explanations were either good or bad (i.e., tautological); (2) Language, explanations either contained unnecessary technical language or did not. We measured participants’ evaluation of the explanations and intentions to vaccinate. We demonstrate a ‘seductive allure’ effect of technical language on bad vaccine explanations. However, an opposite ‘repellent disdain’ effect occurred for good explanations which were rated worse when they contained technical language. Moreover, we show that evaluations of explanations influence intentions to vaccinate. We suggest that misinformation that includes technical language could be more detrimental to vaccination rates. Importantly, however, clear explanatory public health information that omits technical language will be more effective in increasing intentions to vaccinate.

Confirmatory aspect-level opinion mining processes for tourism and hospitality research: a proposal of DiSSBUS

ABSTRACT We proposed a new rule-based text analysis method to effectively summarize and transform unstructured user-generated content (online customer reviews) into an analysable form for tourism and hospitality research. To differentiate this method, we developed the Disintegrating, Summarizing, Straining, Bagging, Upcycling, and Scoring – DiSSBUS – algorithm which can address the following problems in previous approaches: (1) false identification of irrelevant aspect terms, (2) improper handling of multiple aspects and sentiments within a text unit, and (3) data sparsity. The algorithm’s distinctive advantage is to decompose a single review into a set of bi-terms related to the aspects that are pre-specified based on domain knowledge. Therefore, this algorithm can identify customer opinions on specific aspects, which allows to extract variables of interest from online reviews. To evaluate the performance of our confirmatory aspect-level opinion-mining algorithm, we applied it to customer reviews on restaurants in Hawaii. The findings from the empirical test validated its effectiveness.

A new feature selection metric for text classification: eliminating the need for a separate pruning stage

Terms that occur too frequently or rarely in various texts are not useful for text classification. Pruning can be used to remove such irrelevant terms reducing the dimensionality of the feature space and, thus making feature selection more efficient and effective. Normally, pruning is achieved by manually setting threshold values. However, incorrect threshold values can result in the loss of many useful terms or retention of irrelevant ones. Existing feature ranking metrics can assign higher ranks to these irrelevant terms, thus degrading the performance of a text classifier. In this paper, we propose a new feature ranking metric, which can select the most useful terms in the presence of these too frequently and rarely occurring terms, thus eliminating the need for pruning these terms. To investigate the usefulness of the proposed metric, we compare it against seven well-known feature selection metrics on five data sets namely Reuters-21578 (re0, re1, r8) and WebACE (k1a, k1b) using multinomial naive Bayes and support vector machines classifiers. Our results based on a paired t-test show that the performance of our metric is statistically significant than that of the other seven metrics.

Bayesian analysis of turbulent transport coefficients in 2D interchange dominated ExB turbulence involving flow shear

While turbulent transport is known to dominate the radial particle and energy transport in the plasma edge, a self-consistent description of turbulent transport in mean-field transport codes remains lacking. Mean-field closure models based on the turbulent kinetic energy (k ⊥) and turbulent enstrophy (ζ⊥) have recently been proposed to self-consistently model this transport. This contribution analyses the diffusive particle transport relations of these models by means of the Bayesian framework for parameter estimation and model comparison. The reference data includes a set of isothermal simulations that not only include the scrape-off layer (SOL) but also the outer core region (in which a drift wave-like model is activated) and a set of anisothermal SOL simulations, both obtained with the TOKAM2D turbulence code. This analysis shows that the k ⊥(–ζ⊥) model does not appropriately capture the diffusion coefficients for these new data sets, presumably due to the strong flows in the diamagnetic direction that appear in these new cases. While flow shear is expected to quench the turbulence and the turbulent transport, its effect was not explicitly taken into account in the earlier k ⊥(–ζ⊥) transport models. As flow shear provides a new mechanism for the decorrelation of the turbulence, we propose to introduce an additional time scale in the diffusive transport relation as . Inspiration is drawn from shear decorrelation times reported in literature to propose several new candidate models, which are then analysed in a Bayesian setting. This allowed identifying irrelevant terms for certain models and to rank all models according to the Bayesian evidence. While the new models accounting for shear do improve the match to the data, significant errors still remain. Also, no single model could be identified that performs best for all data sets.

Gentle introduction to rigorous Renormalization Group: a worked fermionic example

Much of our understanding of critical phenomena is based on the notion of Renormalization Group (RG), but the actual determination of its fixed points is usually based on approximations and truncations, and predictions of physical quantities are often of limited accuracy. The RG fixed points can be however given a fully rigorous and non- perturbative characterization, and this is what is presented here in a model of symplectic fermions with a nonlocal (“long-range”) kinetic term depending on a parameter ε and a quartic interaction. We identify the Banach space of interactions, which the fixed point belongs to, and we determine it via a convergent approximation scheme. The Banach space is not limited to relevant interactions, but it contains all possible irrelevant terms with short-ranged kernels, decaying like a stretched exponential at large distances. As the model shares a number of features in common with ϕ4 or Ising models, the result can be used as a benchmark to test the validity of truncations and approximations in RG studies. The analysis is based on results coming from Constructive RG to which we provide a tutorial and self-contained introduction. In addition, we prove that the fixed point is analytic in ε, a somewhat surprising fact relying on the fermionic nature of the problem.

Subjective Cognitive Decline: Mental Health, Loneliness, Pain and Quality Of Life: Poblational Study

Background: Tourists are influenced by pricing effects when deciding to buy a specific product. As this research field continues to evolve, this study reviewed the tourist’s process of price information and tourists’ behavior of related literature, focusing on the analysis of general price decisions theory and highlighting its empirical study with information processing. This study aims to review tourist literatures on price processes and behavior, and identify research term networks and active research countries based on previous literature. Method: The articles found in the ISI Web of Science database were reviewed using bibliometric analysis, focusing on the literatures on price decisions about tourists published in the top 20 marketing journals. We analyzed the bibliographic data through a quantitative analysis approach and a knowledge mapping technique using bibliometric data. Knowledge mapping was performed using VOS viewer (V1.6.6), focalizing on “link strength” of networks based on author keywords, and the text corpus (title and abstract). To identify emerging terms, we manually edited the thesaurus list to exclude the general terms such as tourist, market and category and to avoid irrelevant terms (i.e., pricing strategy, price competition, industry or BOB pricing). We used the full counting method to produce co-occurrence networks of the most-used terms for author and corpus text keyword with a minimum of 2 occurrences for each term, and make the heat hap of active countries publishing about price decisions by tourists. Conclusion: It was found that USA and Germany are leading price decision research about tourists, and Current research focuses on tourists' response to a single price information and ignoring price affect during tourists’ decisions, which is assumed to be in a non-updated and certain information state. Therefore, it is encouraged that the growing community of researchers reveal the dynamical tourist’s price decisions under uncertain with a dual system (cognitive and affect) in the future.

Effective Electrodynamics Theory for the Hyperbolic Metamaterial Consisting of Metal–Dielectric Layers

In this work, we study the dynamical behaviors of the electromagnetic fields and material responses in the hyperbolic metamaterial consisting of periodically arranged metallic and dielectric layers. The thickness of each unit cell is assumed to be much smaller than the wavelength of the electromagnetic waves, so the effective medium concept can be applied. When electromagnetic (EM) fields are present, the responses of the medium in the directions parallel to and perpendicular to the layers are similar to those of Drude and Lorentz media, respectively. We derive the time-dependent energy density of the EM fields and the power loss in the effective medium based on Poynting theorem and the dynamical equations of the polarization field. The time-averaged energy density for harmonic fields was obtained by averaging the energy density in one period, and it reduces to the standard result for the lossless dispersive medium when we turn off the loss. A numerical example is given to reveal the general characteristics of the direction-dependent energy storage capacity of the medium. We also show that the Lagrangian density of the system can be constructed. The Euler–Lagrange equations yield the correct dynamical equations of the electromagnetic fields and the polarization field in the medium. The canonical momentum conjugates to every dynamical field can be derived from the Lagrangian density via differentiation or variation with respect to that field. We apply Legendre transformation to this system and find that the resultant Hamiltonian density is identical to the energy density up to an irrelevant divergence term. This coincidence implies the correctness of the energy density formula we obtained before. We also give a brief discussion about the Hamiltonian dynamics description of the system. The Lagrangian description and Hamiltonian formulation presented in this paper can be further developed for studying the elementary excitations or quasiparticles in other hyperbolic metamaterials.

Slow scrambling and hidden integrability in a random rotor model

We analyze the out-of-time-order correlation functions of a solvable model of a large number, $N$, of $M$-component quantum rotors coupled by Gaussian-distributed random, infinite-range exchange interactions. We focus on the growth of commutators of operators at a temperature $T$ above the zero temperature quantum critical point separating the spin-glass and paramagnetic phases. In the large $N,~M$ limit, the squared commutators of the rotor fields do not display any exponential growth of commutators, in spite of the absence of any sharp quasiparticle-like excitations in the disorder-averaged theory. We show that in this limit, the problem is integrable and point out interesting connections to random-matrix theory. At leading order in $1/M$, there are no modifications to the critical behavior but an irrelevant term in the fixed-point action leads to a small exponential growth of the squared commutator. We also introduce and comment on a generalized model involving $p$-pair rotor interactions.

Analysis of the Causes of Cyanobacteria Bloom: A Review

Among water blooms, cyanobacteria bloom occurs over the widest range and is much more harmful than other blooms. Its occurrence in inland water bodies is affected by many factors, such as meteorology, hydrology, and human activities. Therefore, the study of the causes of cyanobacterial bloom has become a major focus of scholars. The China Knowledge Network Journal Database contains 143 papers from China and abroad from the years 2004 to 2019 that are relevant to the study of cyanobacteria bloom. We begin by analyzing keywords in these studies and creating a keyword distribution map which indicates the factors related to the blooms. Based on parameters such as the frequency of words appearing in the text, the full text of each of the 143 papers is analyzed to form a word cloud created by a program written in Python language. After irrelevant terms are eliminated, the word cloud map can reveal potential factors that were not identified by keywords alone. After completing this macro analysis, we examined approximately 100 related papers from the China Knowledge Network Journal Database and Web of Science Database published from 2014 to 2019. Finally, we summarize the main reasons for the outbreak of water blooms. The factors causing blooms can be divided into natural factors and human factors. Among the natural factors are illumination, water temperature and nutrient salt conditions. The human factors are generally related to large-scale water conservancy projects. This paper analyzes and summarizes these factors, and provides a reference to aid in the prevention and treatment of algal blooms. The information in the paper has a certain practical significance for the protection of water environments.

A Novel Inherent Distinguishing Feature Selector for Highly Skewed Text Document Classification

A major problem text classification faces is the high dimensional feature space of the text data. Feature selection (FS) algorithms are used for eliminating the irrelevant and redundant terms, thus increasing accuracy and speed of a text classifier. For text classification, FS algorithms have to be designed keeping the highly imbalanced classes of the text data in view. To this end, more recently ensemble algorithms (e.g., improved global feature selection scheme (IGFSS) and variable global feature selection scheme (VGFSS)) were proposed. These algorithms, which combine local and global FS metrics, have shown promising results with VGFSS having better capability of addressing the class imbalance issue. However, both these schemes are highly dependent on the underlying local and global FS metrics. Existing FS metrics get confused while selecting relevant terms of a data with highly imbalanced classes. In this paper, we propose a new FS metric named inherent distinguished feature selector (IDFS), which selects terms having greater relevance to classes and is highly effective for imbalanced data sets. We compare performance of IDFS against five well-known FS metrics as a stand-alone FS algorithm and as a part of the IGFSS and VGFSS frameworks on five benchmark data sets using two classifiers, namely support vector machines and random forests. Our results show that IDFS in both scenarios selects smaller subsets, and achieves higher micro and macro $$F_1$$ values, thus outperforming the existing FS metrics.

Query expansion via learning change sequences

Proksch has proved the changed terms of source code negatively affect code search quality. However, current query expansion (QE) methods always ignore it. In this paper we propose a novel QE method based on the semantics of change sequences (QESC). It not only captures which changes occurred by ext racting change sequences from Github commits, but also understands why changes occurred by learning sequence semantics with Deep Belief Network (DBN). Thus it could extract relevant terms to expand or irrelevant terms to exclude from the changes semantically similar to a query. Our experimental results show QESC outperforms the existing QE methods by 15–23% in terms of precision on inspecting the first query result.

A substrate-based ontology for human solute carriers.

Solute carriers (SLCs) are the largest family of transmembrane transporters in the human genome with more than 400 members. Despite the fact that SLCs mediate critical biological functions and several are important pharmacological targets, a large proportion of them is poorly characterized and present no assigned substrate. A major limitation to systems‐level de‐orphanization campaigns is the absence of a structured, language‐controlled chemical annotation. Here we describe a thorough manual annotation of SLCs based on literature. The annotation of substrates, transport mechanism, coupled ions, and subcellular localization for 446 human SLCs confirmed that ~30% of these were still functionally orphan and lacked known substrates. Application of a substrate‐based ontology to transcriptomic datasets identified SLC‐specific responses to external perturbations, while a machine‐learning approach based on the annotation allowed us to identify potential substrates for several orphan SLCs. The annotation is available at https://opendata.cemm.at/gsflab/slcontology/. Given the increasing availability of large biological datasets and the growing interest in transporters, we expect that the effort presented here will be critical to provide novel insights into the functions of SLCs.

A New Hybrid Document Clustering for PRF-Based Automatic Query Expansion Approach for Effective IR

Automatic query expansion (AQE) is an effective measure to improve information retrieval performance by including additional terms in a user query. The pseudo relevance feedback (PRF) method employed for AQE so far has suffered from a major problem of query drift. Therefore, keeping it in view, a new hybrid document clustering for PRF based AQE approach is proposed in the present article. In this, Fuzzy logic and Particle Swarm Optimization (PSO) are used to construct document clusters. Further, a new and effective hybrid PSO and Fuzzy logic-based term weighting approach is followed to find more suitable additional query terms using a weighted score of four IR evidences which is considered maximized. Moreover, a combined semantic filtering method along with query terms re-weighting algorithms are also used to remove noisy or irrelevant terms semantically. The performance of the presented approaches in this article is tested and compared with other approaches on three benchmark data sets. The comparative analysis of all the tested approaches illustrates the superior performance of the proposed approach.

"Tell Me About Your Child": A Grounded Theory Study of Mothers' Understanding of Language Disorder.

Purpose The purpose of this study was to generate a theory grounded in data explaining caregivers' understanding of their child's language disorder and the perceived role of speech-language pathologists in facilitating this knowledge. Method This study employed grounded theory as a conceptual framework. Qualitative data were generated based on semistructured interviews conducted with 12 mothers of children who had received speech-language pathology services. Results The following themes emerged from the data analysis: (a) Many mothers reported receiving confusing or irrelevant diagnostic terms for language disorder, (b) mothers of children with language disorders were distressed about their children's language problems, (c) mothers did not always trust or understand their children's speech-language pathologist, and (d) mothers were satisfied with the interventions their child had been receiving. Mothers described their children's language disorder using a total of 23 labels, most of which were not useful for accessing meaningful information about the nature of their child's communication problem. Generally, mothers reported they did not receive language-related diagnostic labels from speech-language pathologists for their child's language disorder. Conclusions Two theories were generated from the results: (a) Lack of information provided to mothers about their child's language disorder causes mothers psychological harm that appears to be long lasting. (b) Difficulties in successfully relaying information about language disorders to parents result in negative perceptions of speech-language pathology. Implications and future directions are discussed. Supplemental Material https://doi.org/10.23641/asha.12177390.

Annealing strategy for an enhance rule pruning technique in ACO-Based rule classification

<span>Ant colony optimization (ACO) was successfully applied to data mining classification task through ant-mining algorithms. Exploration and exploitation are search strategies that guide the learning process of a classification model and generate a list of rules. Exploitation refers to the process of intensifying the search for neighbors in good regions, </span><span>whereas exploration aims towards new promising regions during a search process. </span><span>The existing balance between exploration and exploitation in the rule construction procedure is limited to the roulette wheel selection mechanism, which complicates rule generation. Thus, low-coverage complex rules with irrelevant terms will be generated. This work proposes an enhancement rule pruning procedure for the ACO algorithm that can be used in rule-based classification. This procedure, called the annealing strategy, is an improvement of ant-mining algorithms in the rule construction procedure. Presented as a pre-pruning technique, the annealing strategy deals first with irrelevant terms before creating a complete rule through an annealing schedule. The proposed improvement was tested through benchmarking experiments, and results were compared with those of four of the most related ant-mining algorithms, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with hybrid pruner. </span><span>Results display that our proposed technique achieves better performance in terms of classification accuracy, model size, and </span><span>computational time. </span><span>The proposed annealing schedule can be used in other ACO variants for different applications to improve classification accuracy.</span>

Deep learning the semantics of change sequences for query expansion

SummaryThe overexpansion problem negatively affects the quality of query expansion. To improve the quality of queries for searching code, this paper proposed a DBN‐based algorithm for effective query expansion. The deep belief network (DBN) model is trained on the code sequences and their change sequences, which aims to capture the meaningful terms during the evolution of source code. In contrast to previous studies, the proposed model not only extracts relevant terms to expand a query but also excludes irrelevant terms from the query. It addresses two problems in query expansion, including the overexpansion of the original query and the negative influence of the changed terms in the target source code. Experiments on both artificial queries and real queries show that the proposed algorithm outperforms several query expansion algorithms for code search.

Enhance code search via reformulating queries with evolving contexts

To improve code search, many query expansion (QE) approaches use APIs or crowd knowledge for expanding a query. However, these approaches may sometimes negatively impact the retrieval performance. This is because they can’t distinguish the relevant terms from the irrelevant ones among a large set of candidate expansion terms and expand a query with irrelevant terms. In this paper, we propose QREC, a query reformulation approach with evolving contexts that refer to new/deleted terms and dependent terms during the code evolution. By considering the new terms as the relevant and the deleted terms as the irrelevant, QREC could reformulate a query with appropriate expansion terms. The experimental results show that QREC outperforms the state-of-the-art QE approaches (e.g., CodeHow and QECK) by 9–11% and improves the precision of the code search algorithms IR, Portfolio and VF by up to 37–45%.

Particle Grey Wolf Optimizer (PGWO) Algorithm and Semantic Word Processing for Automatic Text Clustering

Text mining refers to the process of extracting the high-quality information from the text. It is broadly used in applications, like text clustering, text categorization, text classification, etc. Recently, the text clustering becomes the facilitating and challenging task used to group the text document. Due to some irrelevant terms and large dimension, the accuracy of text clustering is reduced. In this paper, the semantic word processing and novel Particle Grey Wolf Optimizer (PGWO) is proposed for automatic text clustering. Initially, the text documents are given as input to the pre-processing step which caters the useful keyword for feature extraction and clustering. Then, the resultant keyword is applied to wordnet ontology to find out the synonyms and hyponyms of every keyword. Subsequently, the frequency is determined for every keyword which is used to build the text feature library. Since the text feature library contains the larger dimension, the entropy is utilized to select the most significant feature. Finally, the new algorithm Particle Grey Wolf Optimizer (PGWO) is developed by integrating the particle swarm optimization (PSO) into the grey wolf optimizer (GWO). Thus, the proposed algorithm is used to assign the class labels to generate the different clusters of text documents. The simulation is performed to analyze the performance of the proposed algorithm, and the proposed algorithm is compared with existing algorithms. The proposed method attains the clustering accuracy of 80.36% for 20 Newsgroup dataset and the clustering accuracy of 79.63% for Reuter which ensures the better automatic text clustering.

Klein bottle entropy of compactified boson conformal field theory

We extend the scope of the Klein bottle entropy, originally introduced by [Tu, Phys. Rev. Lett. 119, 261603 (2017)] in the rational conformal field theory (CFT), to the compactified boson CFT, which are relevant to the studies of Luttinger liquids. We first review the Klein bottle entropy in rational CFT and discuss details of how to extract the Klein bottle entropy from lattice models using the example of the transverse field Ising model. We then go beyond the scope of rational CFT and study the Klein bottle entropy $\ln g$ in the compactified boson CFT, which turns out to have a straightforward relation to the compactification radius $R$, $\ln g = \ln R$. This relation indicates a convenient and efficient method to extract the Luttinger parameter from lattice model calculations. Our numerical results on the Klein bottle entropy in the spin-$1/2$ XXZ chain show excellent agreement with the CFT predictions, up to some small deviations near the isotropic point, which we attribute to the marginally irrelevant terms. For the $S = 1$ XXZ chain that cannot be exactly solved, our numerical results provide an accurate numerical determination of the Luttinger parameter in this model.

DETERMINATION OF THE LÉVY EXPONENT IN ASSET PRICING MODELS

We consider the problem of determining the Lévy exponent in a Lévy model for asset prices given the price data of derivatives. The model, formulated under the real-world measure [Formula: see text], consists of a pricing kernel [Formula: see text] together with one or more non-dividend-paying risky assets driven by the same Lévy process. If [Formula: see text] denotes the price process of such an asset, then [Formula: see text] is a [Formula: see text]-martingale. The Lévy process [Formula: see text] is assumed to have exponential moments, implying the existence of a Lévy exponent [Formula: see text] for [Formula: see text] in an interval [Formula: see text] containing the origin as a proper subset. We show that if the prices of power-payoff derivatives, for which the payoff is [Formula: see text] for some time [Formula: see text], are given at time [Formula: see text] for a range of values of [Formula: see text], where [Formula: see text] is the so-called benchmark portfolio defined by [Formula: see text], then the Lévy exponent is determined up to an irrelevant linear term. In such a setting, derivative prices embody complete information about price jumps: in particular, the spectrum of the price jumps can be worked out from current market prices of derivatives. More generally, if [Formula: see text] for a general non-dividend-paying risky asset driven by a Lévy process, and if we know that the pricing kernel is driven by the same Lévy process, up to a factor of proportionality, then from the current prices of power-payoff derivatives we can infer the structure of the Lévy exponent up to a transformation [Formula: see text], where [Formula: see text] and [Formula: see text] are constants.