How to evaluate local fit (residuals) in large structural equation models.

Consistent with reporting standards for structural equation modelling (SEM), model fit should be evaluated at two different levels, global and local. Global fit concerns the overall or average correspondence between the entire data matrix and the model, given the parameter estimates for the model. Local fit is evaluated at the level of the residuals, or differences between observed and predicted associations for every pair of measured variables in the model. It can happen that models with apparently satisfactory global fit can nevertheless have problematic local fit. This may be especially true for relatively large models with many variables, where serious misspecification is indicated by some larger residuals, but their contribution to global fit is diluted when averaged together with all the other smaller residuals. It can be challenging to evaluate local fit in large models with dozens or even hundreds of variables and corresponding residuals. Thus, the main goal of this tutorial is to offer suggestions about how to efficiently evaluate and describe local fit for large structural equation models. An empirical example is described where all data, syntax and output files are freely available to readers.

Firefly algorithm-based LSTM model for Guzheng tunes switching with big data analysis.

Guzheng tune progression involves intricately harmonizing melodic motif transitions. Effectively navigating this vast creative possibility space to expose musically consistent elaborations presents challenges. We develop a specialized large long short-term memory (LSTM) model for generating musically consistent Guzheng tune transitions. First, we propose novel firefly algorithm (FA) enhancements, e.g., adaptive diversity preservation and adaptive swim parameters, to boost exploration effectiveness for navigating the vast creative combinatorics when generating Guzheng tune transitions. Then, we develop a specialized stacked LSTM architecture incorporating residual connections and conditioned embedding vectors that can leverage long-range temporal dependencies in Guzheng music patterns, including unsupervised learning of concise Guzheng-specific melody embedding vectors via a variational autoencoder, encapsulating unique harmonic signatures from performance descriptors to provide style guidance. Finally, we use LSTM networks to develop adversarial generative large models that enable realistic synthesis and evaluation of Guzheng tunes switching. We gather an extensive 10+ hour corpus of solo Guzheng recordings spanning 230 musical pieces, 130 distinguished performing artists, and 600+ audio tracks. Simultaneously, we conduct thorough Guzheng data analysis. Comparative assessments against strong baselines over systematic musical metrics and professional listeners validate significant generation fidelity improvements. Our model achieves a 63 % reduction in reconstruction error compared to the standard FA optimization after 1000 iterations. It also outperforms baselines in capturing characteristic motifs, maintaining modality coherence with under 2 % dissonant pitch errors, and retaining desired rhythmic cadences. User studies further confirm the superior naturalness, novelty, and stylistic faithfulness of the generated tune transitions, with ratings close to real data.

A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model.

In recent years, artificial intelligence has made remarkable strides, improving various aspects of our daily lives. One notable application is in intelligent chatbots that use deep learning models. These systems have shown tremendous promise in the medical sector, enhancing healthcare quality, treatment efficiency, and cost-effectiveness. However, their role in aiding disease diagnosis, particularly chronic conditions, remains underexplored. Addressing this issue, this study employs large language models from the GPT series, in conjunction with deep learning techniques, to design and develop a diagnostic system targeted at chronic diseases. Specifically, performed transfer learning and fine-tuning on the GPT-2 model, enabling it to assist in accurately diagnosing 24 common chronic diseases. To provide a user-friendly interface and seamless interactive experience, we further developed a dialog-based interface, naming it Chat Ella. This system can make precise predictions for chronic diseases based on the symptoms described by users. Experimental results indicate that our model achieved an accuracy rate of 97.50% on the validation set, and an area under the curve (AUC) value reaching 99.91%. Moreover, conducted user satisfaction tests, which revealed that 68.7% of participants approved of Chat Ella, while 45.3% of participants found the system made daily medical consultations more convenient. It can rapidly and accurately assess a patient's condition based on the symptoms described and provide timely feedback, making it of significant value in the design of medical auxiliary products for household use.

Confounder balancing in adversarial domain adaptation for pre-trained large models fine-tuning.

The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them the better foundation models in the adversarial domain adaptation (ADA) methods to transfer knowledge learned from the source domain to target domains. However, existing ADA methods fail to account for the confounder properly, which is the root cause of the source data distribution that differs from the target domains. This study proposes a confounder balancing method in adversarial domain adaptation for PLMs fine-tuning (CadaFT), which includes a PLM as the foundation model for a feature extractor, a domain classifier and a confounder classifier, and they are jointly trained with an adversarial loss. This loss is designed to improve the domain-invariant representation learning by diluting the discrimination in the domain classifier. At the same time, the adversarial loss also balances the confounder distribution among source and unmeasured domains in training. Compared to newest ADA methods, CadaFT can correctly identify confounders in domain-invariant features, thereby eliminating the confounder biases in the extracted features from PLMs. The confounder classifier in CadaFT is designed as a plug-and-play and can be applied in the confounder measurable, unmeasurable, or partially measurable environments. Empirical results on natural language processing and computer vision downstream tasks show that CadaFT outperforms the newest GPT-4, LLaMA2, ViT and ADA methods.

The potential of 'Segment Anything' (SAM) for universal intelligent ultrasound image guidance.

Ultrasound image guidance is a method often used to help provide care, and it relies on accurate perception of information, and particularly tissue recognition, to guide medical procedures. It is widely used in various scenarios that are often complex. Recent breakthroughs in large models, such as ChatGPT for natural language processing and Segment Anything Model (SAM) for image segmentation, have revolutionized interaction with information. These large models exhibit a revolutionized understanding of basic information, holding promise for medicine, including the potential for universal autonomous ultrasound image guidance. The current study evaluated the performance of SAM on commonly used ultrasound images and it discusses SAM's potential contribution to an intelligent image-guided framework, with a specific focus on autonomous and universal ultrasound image guidance. Results indicate that SAM performs well in ultrasound image segmentation and has the potential to enable universal intelligent ultrasound image guidance.

Fitting Large Factor Analysis Models With Ordinal Data.

A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and misspecification affect parameter estimates, standard errors of parameter estimates, and selected fit indices. As the number of items increased, the number of admissible solutions and accuracy of parameter estimates improved, even when models were misspecified. Also, standard errors of parameter estimates were closer to empirical standard deviation values as the number of items increased. When evaluating goodness-of-fit for ordinal CFA with many observed indicators, researchers should be cautious in interpreting the root mean square error of approximation, as this value appeared overly optimistic under misspecified conditions.

Systematic analysis of residual density suggests that a major limitation in well-refined X-ray structures of proteins is the omission of ordered solvent.

In the residual electron density map of a fully refined X-ray protein model, there should be no peaks arising from modeling errors or missing atoms. Any residual peaks that do occur should be contributed by random residual intensity differences between the model and the data. If the model is incomplete (i.e., some atoms are missing), there will be more positive peaks than negative ones. On the other hand, if the model includes inappropriately located atoms, there will be an excess of negative peaks. In this study, random residual peaks are quantified using the probability density function P(x), which is defined as the probability for a peak having peak height between x and x + dx. It is found that P(x) is single-exponential and symmetric for both positive and negative peaks. Thus, P(x) can be used to discriminate residual peaks contributed by random noise in complete models from residual peaks being attributable to modeling errors in incomplete models. For a number of representative structures in the PDB it is found that P(x) has far more large (greater than 5 sigma) positive peaks than large negative peaks. This excess of large positive peaks suggests that the main defect in these refined structures is the omission of ordered water molecules.