Factors Impacting the Empirical Identification of the Bifactor IRT Model of Rating Data

Response data corresponding to educational and psychological instruments may represent different dimensional structures to account for different patterns of the dependencies in the data. One of the dimensional structures that has been increasingly discussed in the literature is the bifactor structure. This structure can effectively separate different sources that influence the responses, which contributes to score validity and provides theoretical insights about the measured trait. Unfortunately, estimating this structure in practice comes with challenges. One such challenge is an empirical identification issue that is seldom discussed in the literature. This issue occurs when an item’s discriminations on the general and specific dimensions (or within-item discriminations) are similar in strength, making it difficult to obtain accurate estimates for those discriminations. The current evidence regarding the empirical identification issue was shown in only limited situations under full information maximum likelihood (FIML) estimation method. The extent to which the within-item discriminations have to be similar before estimation issues arise and whether the similarity depends on sample size, strength of the item discriminations, and item targetedness (i.e., how well the items’ response categories are targeted to the respondents) are unclear. Also, whether the empirical identification issue occurs under other estimation methods is unknown. This dissertation fills these gaps using three simulation studies. The results suggest that the empirical identification issue of the bifactor model due to the item’s discriminations being similar is moderated by the magnitude of the within-item discriminations. In addition, larger sample sizes can mitigate the estimation inaccuracies caused by within-item discriminations being similar and the discriminations being strong in magnitude. The results also show that Bayesian estimation using adaptive informative priors may produce more accurate discrimination estimates than FIML and Bayesian estimation using less informative priors when the empirical identification issue occurs

Inferring Document Readability by Integrating Eye Movement Features

Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEG-based emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract high-level features from raw EEG signals. Our preliminary experiment on the DEAP dataset shows that the learned features perform comparably to the use of manually generated features for emotion recognition

Towards Building a Semantic Grid for E-Learning

Abstract. In an E-learning scenario, educational resources, such as course documents, videos, test-bases, courseware, and teacher information etc., are shared across different schools. DartGrid is built upon several techniques from both Semantic Web and Grid research areas, and is intended to offer a semantic grid toolkit for data integration. In this paper, a Semantic Grid for E-leaning based on DartGrid is introduced, and it provides a Semantic-based distributed infrastructure for E-learning resource sharing. We explore the essential and fundamental roles played by RDF semantics for e-learning, and implement a set of semantically enabled tools and grid services for E-learning such as semantic browser, ontology service, semantic query service, and semantic registration service

A breakdown-free block conjugate gradient method for large-scale discriminant analysis

Rayleigh-Ritz discriminant analysis (RRDA) is an effective algorithm for linear discriminant analysis (LDA), but there are some drawbacks in its implementation. In this paper, we first improved Rayleigh-Ritz discriminant analysis (IRRDA) to make its framework more concise, and established the equivalence theory of the solution space between our discriminant analysis and RRDA. Second, we proposed a new model based on positive definite systems of linear equations for linear discriminant analysis, and certificated the rationality of the new model. Compared with the traditional linear regression model for linear discriminant analysis, the coefficient matrix of our model avoided forming a centralized matrix or appending the original data matrix, but the original matrix itself, which greatly reduced the computational complexity. According to the size of data matrix, we designed two solution schemes for the new model based on the block conjugate gradient method. Experiments in real-world datasets demonstrated the effectiveness and efficiency of our algorithm and it showed that our method was more efficient and faster than RRDA

Sum-frequency generation from etchless lithium niobate empowered by dual quasi-bound states in the continuum

The miniaturization of nonlinear light sources is central to the integrated photonic platform, driving a quest for high-efficiency frequency generation and mixing at the nanoscale. In this quest, the high-quality ($Q$) resonant dielectric nanostructures hold great promise, as they enhance nonlinear effects through the resonantly local electromagnetic fields overlapping the chosen nonlinear materials. Here, we propose a method for the enhanced sum-frequency generation (SFG) from etcheless lithium niobate (LiNbO$_{3}$) by utilizing the dual quasi-bound states in the continuum (quasi-BICs) in a one-dimensional resonant grating waveguide structure. Two high-$Q$ guided mode resonances corresponding to the dual quasi-BICs are respectively excited by two near-infrared input beams, generating a strong visible SFG signal with a remarkably high conversion efficiency of $3.66\times10^{-2}$ (which is five orders of magnitude higher than that of LiNbO$_{3}$ films of the same thickness) and a small full-width at half-maximum less than 0.2 nm. The SFG efficiency can be tuned via adjusting the grating geometry parameter or choosing the input beam polarization combination. Furthermore, the generated SFG signal can be maintained at a fixed wavelength without the appreciable loss of efficiency by selectively exciting the angular-dependent quasi-BICs, even if the wavelengths of input beams are tuned within a broad spectral range. Our results provide a simple but robust paradigm of high-efficiency frequency conversion on an easy-fabricated platform, which may find applications in nonlinear light sources and quantum photonics

Spatiotemporal dynamics of activation in motor and language areas suggest a compensatory role of the motor cortex in second language processing

The involvement of the motor cortex in language understanding has been intensively discussed in the framework of embodied cognition. Although some studies have provided evidence for the involvement of the motor cortex in different receptive language tasks, the role that it plays in language perception and understanding is still unclear. In the present study, we explored the degree of involvement of language and motor areas in a visually presented sentence comprehension task, modulated by language proficiency (L1: native language, L2: second language) and linguistic abstractness (literal, metaphorical, and abstract). Magnetoencephalography data were recorded from 26 late Chinese learners of English. A cluster-based permutation F-test was performed on the amplitude of the source waveform for each motor and language region of interest (ROI). Results showed a significant effect of language proficiency in both language and motor ROIs, manifested as overall greater involvement of language ROIs (short insular gyri and planum polare of the superior temporal gyrus) in the L1 than the L2 during 300–500 ms, and overall greater involvement of motor ROI (central sulcus) in the L2 than the L1 during 600–800 ms. We interpreted the over-recruitment of the motor area in the L2 as a higher demand for cognitive resources to compensate for the inadequate engagement of the language network. In general, our results indicate a compensatory role of the motor cortex in L2 understanding.Peer reviewe

Effect of 4-nonylphenol on the sperm dynamic parameters, morphology and fertilization rate of Bufo raddei

4-Nonylphenol (NP) is a compound that causes endocrine disruption and affects sperm quality of mammals and fish. However, the effects of NP on the sperm and fertilization rate of amphibians remain unknown. This study investigates the in vivo and in vitro effects of NP on the sperm dynamic parameters and fertilization rate of Bufo raddei during the period of amplexus and fertilization, and proposes the induction of these effects. In in vivo assay, male B. raddei were exposed to 3 concentrations of NP (50, 200, or 400 μg/l) or alcohol (0.04‰, control) for 1-3 days. The results suggested that effects on NP on the sperm dynamic parameters, sperm integrity and fertilization rate were not significant (p>0.05). In in vitro assay, the sperm of B. raddei was directly exposed to NP. Based on the results, NP significantly affected the sperm dynamic parameters and integrity (p<0.05). Meanwhile, the sperm reactive oxygen species (ROS) level in the sperm increased significantly (p<0.05), and a negative correlation was recorded between sperm motility and its corresponding ROS level (R=−0.90). Besides, fertilization rate was significantly reduced compared with that of control (p<0.01). The sperm membrane was impaired as well. However, a risk that NP can disrupt the reproduction behavior of B. raddei exists, and the ROS induced by NP and NP itself would be associated with the reduction of fertilization.Keywords: 4-Nonylphenol, Bufo raddei, sperm, morphology, fertilizatio