General Learning Research Articles

Biomass hydrothermal gasification characteristics study: based on deep learning for data generation and screening strategies

Hydrothermal gasification is an import-ant way to utilize biomass resources, and accurate prediction of the biomass hydrothermal gasification process is of great significance for the formulation and optimization of process parameters and equipment. Data is a key factor in machine learning, but due to the high experimental costs associated with hydrothermal gasification processes, acquiring a large amount of experimental data for machine learning modeling is a major challenge. To address this issue, this study proposes a data generation and screening strategy based on generative adversarial networks (GAN). The data generation and screening strategy primarily rely on GAN networks trained with real data as data generators and random forest models trained with real data as data screeners. High-quality synthetic data is selected through screening criteria to augment the dataset. Four machine learning models are used to model the biomass hydrothermal gasification process based on synthetic data to validate this strategy. The results show that, compared to the original data, modeling with synthetic data leads to a significant increase in the evaluation metrics for predicting H2, CH4, and CO2, especially during the testing phase. This indicates the rationality of the proposed data generation and screening strategy.

XGrad: Boosting Gradient-Based Optimizers With Weight Prediction.

In this paper, we propose a general deep learning training framework XGrad which introduces weight prediction into the popular gradient-based optimizers to boost their convergence and generalization when training the deep neural network (DNN) models. In particular, ahead of each mini-batch training, the future weights are predicted according to the update rule of the used optimizer and are then applied to both the forward pass and backward propagation. In this way, during the whole training period, the optimizer always utilizes the gradients w.r.t. the future weights to update the DNN parameters, making the gradient-based optimizer achieve better convergence and generalization compared to the original optimizer without weight prediction. XGrad is rather straightforward to implement yet pretty effective in boosting the convergence of gradient-based optimizers and the accuracy of DNN models. Empirical results concerning five popular optimizers including SGD with momentum, Adam, AdamW, AdaBelief, and AdaM3 demonstrate the effectiveness of our proposal. The experimental results validate that XGrad can attain higher model accuracy than the baseline optimizers when training the DNN models.

Quantifying Population-level Neural Tuning Functions Using Ricker Wavelets and the Bayesian Bootstrap

BackgroundExperience changes visuo-cortical tuning. In humans, re-tuning has been studied during aversive generalization learning, in which the similarity of generalization stimuli (GSs) with a conditioned threat cue (CS+) is used to quantify tuning functions. Previous work utilized pre-defined tuning shapes (generalization and sharpening patterns). This approach may constrain the ways in which re-tuning can be characterized since the tuning patterns may not match the prototypical functions. New methodThe present study proposes a flexible and data-driven method for precisely quantifying changes in tuning based on the Ricker wavelet function and the Bayesian bootstrap. This method was applied to EEG and psychophysics data from an aversive generalization learning paradigm. ResultsThe Ricker wavelet model fitted the steady-state visual event potentials (ssVEP), alpha-band power, and detection accuracy data well. A Morlet wavelet function was used for comparison and fit the data better in some situations, but was more challenging to interpret. The pattern of re-tuning in the EEG data, predicted by the Ricker model, resembled the shapes of the best fitting a-priori patterns. Comparison with existing methodsAlthough the re-tuning shape modeled by the Ricker function resembled the pre-defined shapes, the Ricker approach led to greater Bayes factors and more interpretable results compared to a-priori models. The Ricker approach was more easily fit and led to more interpretable results than a Morlet wavelet model. ConclusionThis work highlights the promise of the current method for capturing the precise nature of visuo-cortical tuning, unconstrained by the implementation of a-priori models.

Podcasting as a Tool for Developing Oral Proficiency and Self-regulated Learning Strategies: Insights from Turkish EFL Learners

This study examines the impact of podcasts on high school students' English language learning, focusing on speaking performance and self-regulated learning strategies. The widespread adoption of digital communication technologies has transformed education, necessitating the integration of technology to enhance learning outcomes. Using a qualitative approach, the study investigates the general English learning habits of students, their perceptions of using podcasts to improve speaking skills, and how podcasts influence their ability to self-regulate learning. Findings reveal that students actively engage with English-language media, benefiting from enhanced listening comprehension, vocabulary acquisition, and pronunciation. Recording podcasts fosters confidence and fluency in speaking, while also promoting goal setting, planning, and reflective practices. The study highlights the importance of integrating podcasts into EFL instruction to create a dynamic learning environment that supports students' language proficiency goals. The results contribute to the literature on educational technology and underscore the potential of podcasts to improve language learning experiences.

The order of stimuli matters when learning second-order transitional probabilities.

The order of stimuli within sequences and the transitional probabilities (TPs) it generates are central information in sequence processing. However, less is known about what type of information and how it is extracted by general learning mechanisms. The present study focused on statistical learning of second-order TPs. Second-order TPs are involved when only the combination of two stimuli predicts the third. In a first experiment, TPs depended crucially on the order of presentation of a pair , which led to different predictions depending on the order of the stimuli (i.e., ABC vs.BAF). Eight visuomotor sequences governed by second-order TPs were used and response times (RTs) were recorded for each transition. The task included a learning phase followed by a switch phase during which the second-order TP were reversed (e.g., the sequences ABC and BAF became respectively ABF and BAC). A decrease of RTs between the second and the third stimulus during the learning phase and an increase of RTs during the switch phase suggested that variations of orders within second-order TPs could be learned. Further analyses, however, indicated that such learning was difficult for most participants. A second experiment showed that the difficulty of learning was not solely due to the difficulty to pick up the effect of order of presentation, but that learning second-order transitional probabilities in addition to order would be the main obstacle. These experiments suggest that statistical learning is capable of learning complex associations, even if this remains a challenge for human cognition.

Attention redirection transformer with semantic oriented learning for unbiased scene graph generation

Scene Graph Generation (SGG) plays an important role in scene understanding because all of the objects and relations in an image can be abstracted into a concise topological graph. Due to the complexity of visual scenes, including mutual occlusion between objects and semantic ambiguity, SGG is still a challenging task. Most of the existing models only focused on the context of a single object while contexts provided by paired objects are ignored. In this paper, we propose an Attention Redirection Transformer (ART) to extract pair-level contexts specifically, which is divided into an attention distraction stage and an attention integration stage. In this way, the attention of the model is forced to be redirected, which explores the implicit information in the background. In addition, to incorporate the semantic information of predicates, a Semantic Oriented Learning Module (SOL) is designed, which may assist in getting better textual semantics and also prompts cross-modal information fusion. At last, a self-diversity driven Dual Translation Embedding Module (DTM) is designed, which refines representations of subject and object and makes them distinct. Experimental results on the Visual Genome dataset demonstrate the effectiveness of our proposed method. Moreover, our method outperforms state-of-the-art methods on the mR@K metric. The source codes are released on Github: https://github.com/Nora-Zhang98/ART-SOL.

Deep diffusion learning of mutual-reflective structured light patterns for multi-body three-dimensional imaging

Simultaneous structured light imaging of multiple objects has become more demanding and widely in many scenarios involving robot operations in intelligent manufacturing. However, it is challenged by pattern aliasing caused by mutual reflection between high-reflective objects. To this end, we propose to learn clear fringe patterns from aliased mutual-reflective observations by diffusion models for achieving high-fidelity multi-body reconstruction in line with typical phase-shift algorithms. Regarding mutual reflection imaging as a formation of adding significant noise, we build a supervised generative learning framework based on diffusion models and then train a self-attention-based deep network with a U-Net-like skip-connected encoder-decoder architecture. We demonstrate the generalization capability of the trained model in fringe pattern recovery and its performance in phase and three-dimensional (3D) shape reconstruction. Both experimental results show that the proposed method has the expected feasibility and accuracy, heralding a promising solution for addressing the current challenge in various multi-body mutual-reflective 3D reconstruction tasks.

Initial state diversification for efficient AlphaZero-style training

AlphaZero has achieved superhuman performance in Go, chess, and shogi with a general reinforcement learning (RL) algorithm. This achievement is remarkable because AlphaZero does not rely on any training dataset of strong players. However, AlphaZero-style training requires substantial computational resources. Gumbel AlphaZero, a recently introduced more efficient version of AlphaZero, reduces the computational cost of AlphaZero training. The goal of this study is to further improve the playing strength of Gumbel AlphaZero under a limited amount of computational resources. We focus on the diversity in training games, inspired by procedural generation and domain randomization in RL studies, and propose a novel method, initial state diversification. This method diversifies the initial states of a self-play game to encourage the RL agent to understand the game in a more general manner through diverse experiences. For example, in shogi, the initial state of each self-play game is diversified by rearranging the pieces under realistic domain constraints. Experiments demonstrated that training with initial state diversification improves the playing strength of Gumbel AlphaZero in shogi, within the same computational budget for training.

Utilization of transformer model in multimodal data fusion learning: Cross-modal knowledge transfer in the new generation learning space

In response to the difficulties in integrating multimodal data and insufficient model generalization ability in traditional cross-modal knowledge transfer, this article used the Transformer model to explore it in the new generation learning space. Firstly, the article analyzed the processing methods of data and models in cross-modal knowledge transfer, and explored the application of Transformer models in the learning space. This model used natural language processing to represent and extract textual features, Mel Frequency Cepstral Coefficients (MFCCs) to represent and extract audio features, and Faster R-CNN (Faster Region-based Convolutional Neural Network) to represent and extract image features. The article also discussed the implementation process of the Transformer model functionality. The experiment used data from four datasets, including Quora Question Pairs, to test the performance of the model’s cross-modal knowledge transfer through intelligent question answering and task analysis. In single type data testing, the accuracy and recall of the model in this article were better than the comparison model in the three types of data. The highest accuracy and recall in the test set were 91% and 93%, respectively. In the most challenging multimodal intelligent question answering test, the speech-image question answering method achieved an accuracy rate of 89% in answering open questions, indicating that the model had good multimodal data fusion ability. In the analysis experiment of 6 homework prone knowledge points on images with text annotations, the induction accuracy reached 85%, indicating that the model had strong generalization ability. The experimental results showed that the Transformer model had good cross-modal knowledge transfer performance, providing a reference for subsequent research on cross-modal knowledge transfer in the new generation learning space.

Associations of Reversal Learning Performance With Personality Disorder Profile and Drug Abuse History in a Sample of Prison Inmates.

Prison inmate samples present a high prevalence of impulsivity- and compulsivity-related behavioral problems. The Probabilistic Reversal Learning Task (PRLT) is a useful tool to assess decision-making, and we explore its associations with inmates' personality disorder (antisocial personality disorder, APD; obsessive-compulsive personality disorder, OCPD; or both) and history of drug abuse. Mixed-effects methods were used to model acquisition and reacquisition curves across PRLT, in a sample of 275 prison inmates diagnosed with OCPD, APD, or both. Two aspects were assessed: general discrimination learning and decision-making inflexibility. Participants with a mixed personality disorder profile showed a clear pattern of decisional inflexibility. A history of drug abuse was associated with a general poorer performance but not with decision-making inflexibility. Inability to adapt to changing contingencies, and thus to adverse consequences of previously rewarded choices, was not linked to compulsivity, as hypothesized to be present in OCPD and substance use disorders, but to the mixed APD/OCPD profile.

A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model

Smart agriculture has become an inevitable trend in the development of modern agriculture, especially promoted by the continuous progress of large language models like chat generative pre-trained transformer (ChatGPT) and general language model (ChatGLM). Although these large models perform well in general knowledge learning, they still have certain limitations and errors when facing agricultural professional knowledge about crop disease identification, growth stage judgment, and so on. Agricultural data involves images and texts and other modalities, which play an important role in agricultural production and management. In order to better learn the characteristics of different modal data in agriculture, realize cross-modal data fusion, and thus understand complex application scenarios, we propose a framework AgriVLM that uses a large amount of agricultural data to fine-tune the visual language model to analyze agricultural data. It can fuse multimodal data and provide more comprehensive agricultural decision support. Specifically, it utilizes Q-former as a bridge between an image encoder and a language model to achieve a cross-modal fusion of agricultural images and text data. Then, we apply a Low-Rank adaptive to fine-tune the language model to achieve an alignment between agricultural image features and a pre-trained language model. The experimental results prove that AgriVLM demonstrates great performance in crop disease recognition and growth stage recognition, with recognition accuracy exceeding 90%, demonstrating its capability to analyze different modalities of agricultural data.

Características identificadas por los docentes de educación básica en el departamento de Caldas (Colombia) en estudiantes con talentos y/o capacidades excepcionales

Exceptional talents and/or abilities are recognized in children or adolescents who demonstrate a greater general learning capacity or in a specific area of knowledge, placing themselves above their reference group. Although in the Colombian context the presence of this population in classrooms is a reality, there are still no clear routes for the recognition of their individual differences. The objective of this study is to identify the characteristics that primary education teachers in Caldas (Colombia) recognize in students with exceptional talents and/or abilities. This is a quantitative, cross-sectional, descriptive and correlational study. The sample was selected intentionally and by convenience, and consisted of a total of 125 participants. The results showed that the predominant indicators are creativity, autonomy and the ability to apply knowledge to solve problems. In addition, it was observed that a higher level of academic training does not necessarily guarantee access, knowledge or mastery of specific topics in the management of students with high intellectual abilities. To change stereotypes, teachers must understand the nature of giftedness and talents and reflect on their beliefs to become aware of their own stereotypes and their potential effects on their classroom behavior and student development.

The effect of professional identity on nursing academic achievement: the chain mediating effect of general self-efficacy and learning engagement

BackgroundExploring the impact of professional identity on the academic performance of nursing students is crucial for understanding how to improve educational outcomes in this field. Professional identity not only shapes students’ self-concept but also influences their motivation, commitment, and success in their studies. However, the mechanisms underlying this relationship are not fully elucidated. This study aims to examine the potential mediating roles of general self-efficacy and learning engagement in this relationship.MethodsA total of 1097 Chinese nursing students participated in this study. They were assessed using the Professional Identity Questionnaire for Undergraduate Students, the General Self-Efficacy Scale, the Utrecht Work Engagement Scale for Students, and the College Students Academic Achievement Scale. The data were analyzed using Pearson correlation, structural equation modeling, and deviation-corrected percentile bootstrap techniques.Results(1) Professional identity (PI) was positively correlated with academic achievement (AA)(r = 0.446, P < 0.01), and the direct path of professional identity on academic achievement was significant (β = 0.301, t = 19.816, P < 0.001). (2) Professional identity positively predicted general self-efficacy(GSE) (β = 0.183, t = 16.427, P < 0.001) and learning engagement(LE) (β = 0.640, t = 22.494, P < 0.001). GSE positively predicted LE(β = 0.645, t = 9.320, P < 0.001) and AA(β = 0.458, t = 12.376, P < 0.001). LE positively predicted AA (β = 0.169, t = 10.877, P < 0.001). (3) GSE and LE play a significant mediating role between PI and AA. The mediating effect includes three paths: PI→GSE→AA(the mediating effect value: 0.084) and PI→LE→AA (the mediating effect value: 0.108). PI→GSE→LE→AA (the mediating effect value: 0.020).ConclusionProfessional identity exerts both direct and indirect influences on the academic achievement of nursing students, mediated through general self-efficacy and learning engagement. These findings underscore the importance of nurturing professional identity as a strategic approach to enhancing academic success in nursing education, providing a foundation for targeted interventions that foster personal and academic growth.

MMFA-DTA: Multimodal Feature Attention Fusion Network for Drug-Target Affinity Prediction for Drug Repurposing Against SARS-CoV-2.

The continuous emergence of novel infectious diseases poses a significant threat to global public health security, necessitating the development of small-molecule inhibitors that directly target pathogens. The RNA-dependent RNA polymerase (RdRp) and main protease (Mpro) of SARS-CoV-2 have been validated as potential key antiviral drug targets for the treatment of COVID-19. However, the conventional new drug R&D cycle takes 10-15 years, failing to meet the urgent needs during epidemics. Here, we propose a general multimodal deep learning framework for drug repurposing, MMFA-DTA, to enable rapid virtual screening of known drugs and significantly improve discovery efficiency. By extracting graph topological and sequence features from both small molecules and proteins, we design attention mechanisms to achieve dynamic fusion across modalities. Results demonstrate the superior performance of MMFA-DTA in drug-target affinity prediction over several state-of-the-art baseline methods on Davis and KIBA data sets, validating the benefits of heterogeneous information integration for representation learning and interaction modeling. Further fine-tuning on COVID-19-relevant bioactivity data enhances model predictions for critical SARS-CoV-2 enzymes. Case studies screening the FDA-approved drug library successfully identify etacrynic acid as the potential lead compound against both RdRp and Mpro. Molecular dynamics simulations further confirm the stability and binding affinity of etacrynic acid to these targets. This study proves the great potential and advantages of deep learning and drug repurposing strategies in supporting antiviral drug discovery. The proposed general and rapid response computational framework holds significance for preparedness against future public health events.

Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT

BackgroundThe intricate three-dimensional anatomy of the inner ear presents significant challenges in diagnostic procedures and critical surgical interventions. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNN), have shown promise for segmenting specific structures in medical imaging. This study aimed to train and externally validate an open-source U-net DL general model for automated segmentation of the inner ear from computed tomography (CT) scans, using quantitative and qualitative assessments.MethodsIn this multicenter study, we retrospectively collected a dataset of 271 CT scans to train an open-source U-net CNN model. An external set of 70 CT scans was used to evaluate the performance of the trained model. The model’s efficacy was quantitatively assessed using the Dice similarity coefficient (DSC) and qualitatively assessed using a 4-level Likert score. For comparative analysis, manual segmentation served as the reference standard, with assessments made on both training and validation datasets, as well as stratified analysis of normal and pathological subgroups.ResultsThe optimized model yielded a mean DSC of 0.83 and achieved a Likert score of 1 in 42% of the cases, in conjunction with a significantly reduced processing time. Nevertheless, 27% of the patients received an indeterminate Likert score of 4. Overall, the mean DSCs were notably higher in the validation dataset than in the training dataset.ConclusionThis study supports the external validation of an open-source U-net model for the automated segmentation of the inner ear from CT scans.Relevance statementThis study optimized and assessed an open-source general deep learning model for automated segmentation of the inner ear using temporal CT scans, offering perspectives for application in clinical routine. The model weights, study datasets, and baseline model are worldwide accessible.Key PointsA general open-source deep learning model was trained for CT automated inner ear segmentation.The Dice similarity coefficient was 0.83 and a Likert score of 1 was attributed to 42% of automated segmentations.The influence of scanning protocols on the model performances remains to be assessed.Graphical

Every cloud has a silver lining: a person-centred analysis of foreign language learners’ motivation and engagement in Chinese universities

ABSTRACT Purpose This study aimed to identify different profiles of foreign language learners' motivation and engagement (ME) and examine their relationships with university-level learning environments (i.e. supportive facilities, academic freedom, and learning resources) and students' generic learning outcomes (i.e. academic efficacy and overall satisfaction with language learning). Methodology With a sample of 1,005 foreign language learners in Chinese universities, the study adopted a person-centred approach and conducted the latent profile analysis to identify distinct ME profiles. The robust three-step method (R3STEP) was used to test how university-level learning environments contributed to the categories of language learners' ME. The Block-Croon-Hagenaars approach (BCH) was conducted to reveal how different ME profiles were related to learners' generic learning outcomes. Findings The study identified three profiles of language learners' ME with distinctive characteristics, and unraveled their relationships with environmental antecedents and students' learning outcomes. Specifically, academic freedom and learning resources played a significant role in shaping these profiles. Interestingly, the seemingly “maladaptive” learners reported higher levels of academic efficacy and overall satisfaction. Value This study provided new insights into the positive role of maladaptive motivation, especially negative emotions, in enhancing engagement and learning outcomes among Chinese foreign language learner.

Matching Occupational Injury and Illness Data Using Augmented Twin Neural Networks

When record linkage efforts involve complex characteristics there is ample potential for general purpose machine learning (ML) techniques to succeed where traditional probabilistic approaches might fall short. However, there can still be pre-processing (e.g. geocoding) and hand-picked comparators that can further improve linkage outcomes using standard ML models. In this project we present a fusion of these sides we are calling an Augmented Twin Neural Network. This approach leverages the inherent flexibility of Twin Neural Networks in a record linkage context while adding additional layers to allow for hand curated comparators that may be difficult for ML optimizers to implicitly identify without sufficiently large, labeled data sets. The framework is used to match establishments from the BLS Survey of Occupational Injuries and Illnesses to establishments in the OSHA Injury Tracking Application data. The difficulties inherent in matching company names and addresses and the existence of multi-establishment firms make this a prime application for testing. Linkage outcome metrics of this augmented algorithm are compared both with results from probabilistic methods (e.g. Fellegi-Sunter) and standard machine learning methods to illustrate the added benefits.

Together again but no need to play: Dissociating effects of isolation and separation on social interaction in female rats (Rattus norvegicus).

Play behavior has been extensively studied across species, but its direct role in social relationships remains unclear. Here we use an "isolation versus separation" protocol to identify behaviors associated with relationship renewal in adolescent female rats. Members of a dyad that had been separated for 24 hr, without isolation from other peers, initially increased investigative behaviors relative to nonseparated peers; however, in contrast with social isolation, separation by itself did not increase rough-and-tumble play. The data suggest that increased play following isolation depends on general motivations, rather than a "peer-specific" drive to renew relationships with an individual. This is consistent with a role of play in more general social learning rather than reestablishing bonds or expectations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Coarse-to-Fine Structure and Semantic Learning for Single-Sample SAR Image Generation

Synthetic Aperture Radar (SAR) enables the acquisition of high-resolution imagery even under severe meteorological and illumination conditions. Its utility is evident across a spectrum of applications, particularly in automatic target recognition (ATR). Since SAR samples are often scarce in practical ATR applications, there is an urgent need to develop sample-efficient augmentation techniques to augment the SAR images. However, most of the existing generative approaches require an excessive amount of training samples for effective modeling of the SAR imaging characteristics. Additionally, they show limitations in augmenting the interesting target samples while maintaining image recognizability. In this study, we introduce an innovative single-sample image generation approach tailored to SAR data augmentation. To closely approximate the target distribution across both the spatial layout and local texture, a multi-level Generative Adversarial Network (GAN) architecture is constructed. It comprises three distinct GANs that independently model the structural, semantic, and texture patterns. Furthermore, we introduce multiple constraints including prior-regularized noise sampling and perceptual loss optimization to enhance the fidelity and stability of the generation process. Comparative evaluations against the state-of-the-art generative methods demonstrate the superior performance of the proposed method in terms of generation diversity, recognizability, and stability. In particular, its advantages over the baseline method are up to 0.2 and 0.22 in the SIFID and SSIM, respectively. It also exhibits stronger robustness in the generation of images across varying spatial sizes.

CENN: Capsule-enhanced neural network with innovative metrics for robust speech emotion recognition

Speech emotion recognition (SER) plays a pivotal role in enhancing Human-computer interaction (HCI) systems. This paper introduces a groundbreaking Capsule-enhanced neural network (CENN) that significantly advances the state of SER through a robust and reproducible deep learning framework. The CENN architecture seamlessly integrates advanced components, including Multi-head attention (MHA), residual module, and capsule module, which collectively enhance the model's capacity to capture both global and local features essential for precise emotion classification. A key contribution of this work is the development of a comprehensive reproducibility framework, featuring novel metrics: General learning reproducibility (GLR) and Correct learning reproducibility (CLR). These metrics, alongside their fractional and perfect variants, offer a multi-dimensional evaluation of the model's consistency and correctness across multiple executions, thereby ensuring the reliability and credibility of the results. To tackle the persistent challenge of overfitting in deep learning models, we propose an innovative overfitting metric that considers the intricate relationship between training and testing errors, model complexity, and data complexity. This metric, in conjunction with the newly introduced generalization and robustness metrics, provides a holistic assessment of the model's performance, guiding the application of regularization techniques to maintain generalizability and resilience. Extensive experiments conducted on benchmark SER datasets demonstrate that the CENN model not only surpasses existing approaches in terms of accuracy but also sets a new benchmark in reproducibility. This work establishes a new paradigm for deep learning model development in SER, underscoring the vital importance of reproducibility and offering a rigorous framework for future research.