General Learning Research Articles

Family income and polygenic scores are independently but not interactively associated with cognitive performance among youth genetically similar to European reference populations.

Cognitive abilities are heritable and influenced by socioeconomic status (SES). It is critical to understand the association between SES and cognition beyond genetic propensity to inform potential benefits of SES-based interventions and to determine if such associations vary across (i) cognitive domains, (ii) facets of SES, and/or (iii) genetic propensity for different aspects of cognition. We examined the contributions of neighborhood socioeconomic advantage, family income, and polygenic scores (PGS) for domains of cognition (i.e., general cognitive ability, executive function, learning and memory, fluid reasoning) in a sample of children (ages 9-10; n = 5549) most genetically similar to reference populations from Europe. With some variability across cognitive outcomes, family income and PGS were independently significantly associated with cognitive performance. Within-sibling analyses revealed that cognitive PGS associations were predominantly driven by between-family effects suggestive of non-direct genetic mechanisms. These findings provide evidence that SES and genetic propensity to cognition have unique associations with cognitive performance in middle childhood. These results underscore the importance of environmental factors and genetic influences in the development of cognitive abilities and caution against overinterpreting associations with PGS of cognitive and educational outcomes as predominantly direct genetic effects.

Workshop training to facilitate parental involvement in their children’s mathematics education: parents’ perceptions

This study investigated the effectiveness of workshops on parental involvement in their children’s mathematical education. The study employed a quasi-experimental with a pre-test-post-test design. This study involved 76 parents from one senior secondary school in the Kweneng Region, Botswana. The study included three steps for data analysis: thematic content analysis, hypothesis testing, and descriptive statistical analysis. The results showed that the parental involvement level before and after the intervention marked a difference with a significant increase. Parental involvement variables such as parenting style, parental expectations, home rules, parental supervision, communication between parents and children, children’s home mathematics activities, and parental attitude toward school all improved significantly. The findings further indicated that parental involvement in their child's general and mathematical learning can be improved. As a result, the study recommended that all basic education schools reach out to parents through parent education programs that can assist parents in understanding the value of being active in their children’s mathematics education, which will probably affect their education performance positively.

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

A survey on person and vehicle re‐identification

AbstractPerson/vehicle re‐identification aims to use technologies such as cross‐camera retrieval to associate the same person (same vehicle) in the surveillance videos at different locations, different times, and images captured by different cameras so as to achieve cross‐surveillance image matching, person retrieval and trajectory tracking. It plays an extremely important role in the fields of intelligent security, criminal investigation etc. In recent years, the rapid development of deep learning technology has significantly propelled the advancement of re‐identification (Re‐ID) technology. An increasing number of technical methods have emerged, aiming to enhance Re‐ID performance. This paper summarises four popular research areas in the current field of re‐identification, focusing on the current research hotspots. These areas include the multi‐task learning domain, the generalisation learning domain, the cross‐modality domain, and the optimisation learning domain. Specifically, the paper analyses various challenges faced within these domains and elaborates on different deep learning frameworks and networks that address these challenges. A comparative analysis of re‐identification tasks from various classification perspectives is provided, introducing mainstream research directions and current achievements. Finally, insights into future development trends are presented.

Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising.

Diffusion models (DM) built from a hierarchy of denoising autoencoders have achieved remarkable progress in image generation, and are increasingly influential in the field of image restoration (IR) tasks. In the meantime, its backbone of autoencoders also evolved from UNet to vision transformer, e.g. Restormer. Therefore, it is important to disentangle the contribution of backbone networks and the additional generative learning scheme. Notably, DM shows varied performance across IR tasks, and the performance of recent advanced transformer-based DM on PET denoising is under-explored. In this study, we further raise an intuitive question, "{if we have a sufficiently powerful backbone, whether DM can be a general add-on generative learning scheme to further boost PET denoising}". Specifically, we investigate one of the best-in-class IR models, i.e., DiffIR, which is a latent DM based on the Restormer backbone. We provide a qualitative and quantitative comparison with UNet, SR3 (UNet+pixel DM), and Restormer, on the 25% low dose 18F-FDG whole-body PET denoising task, aiming to identify the best practices. We trained and tested on 93 and 12 subjects, and each subject has 644 slices. It appears that Restormer outperforms UNet in terms of PSNR and MSE. However, additional latent DM over Restormer does not contribute to better MSE, SSIM, or PSNR in our task, which is even inferior to the conventional UNet. In addition, SR3 with pixel space DM is not stable to synthesize satisfactory results. The results are consistent with the natural image super-resolution tasks, which also suffer from limited spatial information. A possible reason would be the denoising iteration at latent feature space cannot well support detailed structure and texture restoration. This issue is more crucial in the IR tasks taking inputs with limited details, e.g., SR and PET denoising.

Airborne LiDAR Point Cloud Classification Using Ensemble Learning for DEM Generation

Airborne laser scanning (ALS) point clouds have emerged as a predominant data source for the generation of digital elevation models (DEM) in recent years. Traditionally, the generation of DEM using ALS point clouds involves the steps of point cloud classification or ground point filtering to extract ground points and labor-intensive post-processing to correct the misclassified ground points. The current deep learning techniques leverage the ability of geometric recognition for ground point classification. However, the deep learning classifiers are generally trained using 3D point clouds with simple geometric terrains, which decrease the performance of model inferencing. In this study, a point-based deep learning model with boosting ensemble learning and a set of geometric features as the model inputs is proposed. With the ensemble learning strategy, this study integrates specialized ground point classifiers designed for different terrains to boost classification robustness and accuracy. In experiments, ALS point clouds containing various terrains were used to evaluate the feasibility of the proposed method. The results demonstrated that the proposed method can improve the point cloud classification and the quality of generated DEMs. The classification accuracy and F1 score are improved from 80.9% to 92.2%, and 82.2% to 94.2%, respectively, by using the proposed methods. In addition, the DEM generation error, in terms of mean squared error (RMSE), is reduced from 0.318–1.362 m to 0.273–1.032 m by using the proposed ensemble learning.

GDMol: Generative Double-Masking Self-Supervised Learning for Molecular Property Prediction.

Effective molecular feature representation is crucial for drug property prediction. Recent years have seen increased attention on graph neural networks (GNNs) that are pre-trained using self-supervised learning techniques, aiming to overcome the scarcity of labeled data in molecular property prediction. Traditional GNNs in self-supervised molecular property prediction typically perform a single masking operation on the nodes and edges of the input molecular graph, masking only local information and insufficient for thorough self-supervised training. Hence, we propose a model for molecular property prediction based on generative double-masking self-supervised learning, termed as GDMol. This integrates generative learning into the self-supervised learning framework for latent representation, and applies a second round of masking to these latent representations, enabling the model to better capture global information and semantic knowledge of the molecules for a richer, more informative representation, thereby achieving more accurate and robust molecular property prediction. Our experiments on 5 datasets demonstrated superior performance of GDMol in predicting molecular properties across different domains. Moreover, we used the masking operation to traverse through the gradient changes of each node, the magnitude and sign of which reflect the positive and negative contribution respectively of the local structure in the molecule to the prediction outcome. This in-depth interpretative analysis not only enhances the model's interpretability, but also provides more targeted insights and direction for optimizing drug molecules. In summary, this research offers novel insights on improving molecular property prediction tasks, and paves the way for further research on the application of generative learning and self-supervised learning in the field of chemistry.

Decomposed Prototype Learning for Few-Shot Scene Graph Generation

Today's scene graph generation (SGG) models typically require abundant manual annotations to learn new predicate types. Therefore, it is difficult to apply them to real-world applications with massive uncommon predicate categories whose annotations are hard to collect. In this paper, we focus on Few-Shot SGG (FSSGG) , which encourages SGG models to be able to quickly transfer previous knowledge and recognize unseen predicates well with only a few examples. However, current methods for FSSGG are hindered by the high intra-class variance of predicate categories in SGG: On one hand, each predicate category commonly has multiple semantic meanings under different contexts. On the other hand, the visual appearance of relation triplets with the same predicate differs greatly under different subject-object compositions. Such great variance of inputs makes it hard to learn generalizable representation for each predicate category with current few-shot learning (FSL) methods. However, we found that this intra-class variance of predicates is highly related to the composed subjects and objects. To model the intra-class variance of predicates with subject-object context, we propose a novel Decomposed Prototype Learning (DPL) model for FSSGG. Specifically, we first construct a decomposable prototype space to capture diverse semantics and visual patterns of subjects and objects for predicates by decomposing them into multiple prototypes. Afterwards, we integrate these prototypes with different weights to generate query-adaptive predicate representation with more reliable semantics for each query sample. We conduct extensive experiments and compare with various baseline methods to show the effectiveness of our method.

SVQ-MAE: an efficient speech pre-training framework with constrained computational resources

Self-supervised learning for speech pre-training models has achieved remarkable success in acquiring superior speech contextual representations by learning from unlabeled audio, excelling in numerous downstream speech tasks. However, the pre-training of these models necessitates significant computational resources and training duration, presenting a high barrier to entry into the realm of pre-training learning. In our efforts, by amalgamating the resource-efficient benefits of the generative learning model, Masked Auto Encoder, with the efficacy of the vector quantization method in discriminative learning, we introduce a novel pre-training framework: Speech Vector Quantization Masked Auto Encoder (SVQ-MAE). Distinct from the majority of SSL frameworks, which require simultaneous construction of speech contextual representations and mask reconstruction within an encoder-only module, we have exclusively designed a decoupled decoder for pre-training SVQ-MAE. This allows the additional decoupled decoder to undertake the mask reconstruction task solely, reducing the learning complexity of pretext tasks and enhancing the encoder’s efficiency in extracting speech contextual representations. Owing to this innovation, by using only 4 GPUs, SVQ-NAE can achieve high performance comparable to wav2vec 2.0, which requires 64 GPUs for training. In the Speech Processing Universal Performance Benchmark, SVQ-MAE surpasses wav2vec 2.0 in both keyword spotting and emotion recognition tasks. Furthermore, in cross-lingual ASR for Mandarin, upon fine-tuning on AISHELL-1, SVQ-MAE achieves a Character Error Rate of 4.09%, outperforming all supervised ASR models.

Feature Selection as Deep Sequential Generative Learning

Feature selection aims to identify the most pattern-discriminative feature subset. In prior literature, filter (e.g., backward elimination) and embedded (e.g., LASSO) methods have hyperparameters (e.g., top- k , score thresholding) and tie to specific models, thus, hard to generalize; wrapper methods search a feature subset in a huge discrete space and is computationally costly. To transform the way of feature selection, we regard a selected feature subset as a selection decision token sequence and reformulate feature selection as a deep sequential generative learning task that distills feature knowledge and generates decision sequences. Our method includes three steps: (1) We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses. Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores. (2) We leverage the trained feature subset utility evaluator as a gradient provider to guide the identification of the optimal feature subset embedding; (3) We decode the optimal feature subset embedding to autoregressively generate the best feature selection decision sequence with autostop. Extensive experimental results show this generative perspective is effective and generic, without large discrete search space and expert-specific hyperparameters. The code is available at http://tinyurl.com/FSDSGL .

Generative learning for forecasting the dynamics of high-dimensional complex systems

We introduce generative models for accelerating simulations of high-dimensional systems through learning and evolving their effective dynamics. In the proposed Generative Learning of Effective Dynamics (G-LED), instances of high dimensional data are down sampled to a lower dimensional manifold that is evolved through an auto-regressive attention mechanism. In turn, Bayesian diffusion models, that map this low-dimensional manifold onto its corresponding high-dimensional space, operate on batches of physics correlated, time sequences of data and capture the statistics of the system dynamics. We demonstrate the capabilities and drawbacks of G-LED in simulations of several benchmark systems, including the Kuramoto-Sivashinsky (KS) equation, two-dimensional high Reynolds number flow over a backward-facing step, and simulations of three-dimensional turbulent channel flow. The results demonstrate that generative learning offers new frontiers for the accurate forecasting of the statistical properties of high-dimensional systems at a reduced computational cost.

General Machine Learning Approaches for Lithium-Ion Battery Capacity Fade Compared to Empirical Models

Today’s growing demand for lithium-ion batteries across various industrial sectors has introduced a new concern: battery aging. This issue necessitates the development of tools and models that can accurately predict battery aging. This study proposes a general framework for constructing battery aging models using machine learning techniques and compares these models with two existing empirical models, including a commercial one. To build the models, the databases produced by EVERLASTING and Bills et al. were utilized. The aim is to create universally applicable models that can address any battery-aging scenario. In this study, three types of models were developed: a vanilla neural network, a neural network inspired by extreme learning machines, and an encoder coupled with a neural network. The inputs for these models are derived from established knowledge in battery science, allowing the models to capture aging effects across different use cases. The models were trained on cells subjected to specific aging conditions and they were tested on other cells from the same database that experienced different aging conditions. The results obtained during the test for the vanilla neural network showed an RMSE of 1.3% on the Bills et al. test data and an RMSE of 2.7% on the EVERLASTING data, demonstrating similar or superior performance compared to the empirical models and proving the ability of the models to capture battery aging.

Implementing Vocabulary Journals through Digital Narrative Text for EFL Secondary Students in Learning General Vocabulary

Four fundamental language skills such as listening, speaking, reading, and writing are important skills for EFL secondary school students to develop it. Learning English requires fundamental knowledge of vocabulary since it influences these abilities greatly. However, vocabulary instruction is often overlooked in traditional language learning strategies. On the other hand, the use of digital narrative texts, such as videos, provides a modern approach to facilitating students in general vocabulary learning. This study explores the integration of vocabulary journals with digital narrative texts to enhance vocabulary acquisition among EFL secondary students specifically in junior high school students. This study used action research and involved 25 students from a junior high school in Karawang. Data collection methods included vocabulary journal logs, observations, interviews, and reflective journals. The findings revealed that vocabulary journals significantly assist students in understanding and retaining new vocabulary, enhancing overall language comprehension. Students expressed positive attitudes towards this method, highlighting its engaging and interactive learning strategy. This study concludes that combining vocabulary journals with digital narrative texts effectively supports vocabulary acquisition, making learning more enjoyable and impactful. This approach fosters deeper understanding and long-term retention of vocabulary, improving students' language skills and academic performance. The findings suggest that this innovative strategy could be beneficial in educational settings.

Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy.

Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the development of such models lies in the limited training data characterizing different conformational transitions. To address this issue, molecular dynamics simulations is combined with enhanced sampling methods to create a large-scale database. To this end, the study simulates the conformational changes of 2635 proteins featuring two known stable states, and collects the structural information along each transition pathway. Utilizing this database, a general deep learning model capable of predicting the transition pathway for a given protein is developed. The model exhibits general robustness across proteins with varying sequence lengths (ranging from 44 to 704 amino acids) and accommodates different types of conformational changes. Great agreement is shown between predictions and experimental data in several systems and successfully apply this model to identify a novel allosteric regulation in an important biological system, the human β-cardiac myosin. These results demonstrate the effectiveness of the model in revealing the nature of protein conformational changes.

Cerebellar non-invasive stimulation of social and emotional mentalizing: A meta-analysis

Abstract The present meta-analysis investigated the impact of non-invasive stimulation, using transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) targeting the posterior cerebellum, on social and emotional mentalizing about others. Prior research has convincingly shown that the posterior cerebellum supports social and emotional cognition. We identified 14 studies targeting the cerebellum with appropriate control conditions (i.e., sham, control site), which exclude general learning effects of the task or placebo effects. The studies included 29 task conditions where stimulation before or during a social or emotional task was applied on healthy samples. The results showed significant evidence that sustained anodal tDCS and TMS generally improved social and emotional performance after stimulation, in comparison with sham or control conditions, with a small effect size. In contrast, cathodal stimulation showed mixed facilitatory and inhibitory results. In addition, short TMS pulses, administered with the aim of interfering with ongoing social or emotional processes, induced a small but consistent inhibitory effect. Control tasks without social or emotional components also showed significant improvement after sustained anodal tDCS and TMS, suggesting that transcranial stimulation of the cerebellum may also improve other functions. This was not the case for short TMS pulses, which did not modulate non-social and non-emotional control tasks. Taken together, this meta-analysis shows that cerebellar neurostimulation confirms a causal role of the cerebellum in socio-emotional cognition, has a small but significant effect on improving socio-emotional skills, and may therefore have important clinical applications in pathologies where social and emotional cognition is impaired.

Characterizing English Preposing in PP constructions

The English Preposing in PP construction (PiPP; e.g., Happy though/as we were) is extremely rare but displays an intricate set of stable syntactic properties. How do people become proficient with this construction despite such limited evidence? It is tempting to posit innate learning mechanisms, but present-day large language models seem to learn to represent PiPPs as well, even though such models employ only very general learning mechanisms and experience very few instances of the construction during training. This suggests an alternative hypothesis on which knowledge of more frequent constructions helps shape knowledge of PiPPs. I seek to make this idea precise using model-theoretic syntax (MTS). In MTS, a grammar is essentially a set of constraints on forms. In this context, PiPPs can be seen as arising from a mix of construction-specific and general-purpose constraints, all of which seem inferable from general linguistic experience.

Almost pairwise independence and resilience to deep learning attacks

Almost pairwise independence (API) is a quantitative property of a class of functions that is desirable in many cryptographic applications. This property is satisfied by Learning with errors (LWE)-mappings and by special Substitution-Permutation Networks (SPN). API block ciphers are known to be resilient to differential and linear cryptanalysis attacks. Recently, security of protocols against neural network-based attacks became a major trend in cryptographic studies. Therefore, it is relevant to study the hardness of learning a target function from an API class of functions by gradient-based methods. We propose a theoretical analysis based on the study of the variance of the gradient of a general machine learning objective with respect to a random choice of target function from a class. We prove an upper bound and verify that, indeed, such a variance is extremely small for API classes of functions. This implies the resilience of actual LWE-based primitives against deep learning attacks, and to some extent, the security of SPNs. The hardness of learning reveals itself in the form of the barren plateau phenomenon during the training process, or in other words, in a low information content of the gradient about the target function. Yet, we emphasize that our bounds hold for the case of a regular parameterization of a neural network and the gradient may become informative if a class is mildly pairwise independent and a parameterization is non-regular. We demonstrate our theory in experiments on the learnability of LWE mappings.

The Role of Generative Learning Strategy in Bridging the Gap between High and Low Achievers in Genetics Studies in Sefwi-Wiawso Municipality, Ghana

The study sought to investigate the use of the Generative Learning Strategy (GLS) in bridging the performance gap between high- and low-achieving students in genetics in Ghana's Western North region's capital, Sefwi-Wiawso Municipality. The study adopted an embedded research design involving the quasi-experimental pretest/posttest groups with a sample size of 106 SHS 3 Biology students. Two intact classes were randomly selected, treated as one group and exposed to the same treatment conditions. The Genetic Concepts Test and a semi-structured interview guide collected the quantitative and qualitative data, respectively. The GCT's internal consistency was 0.784, indicating a preferable internal consistency. The quantitative data was analyzed using the SPSS version 26, by employing descriptive and inferential statistics. The study found no significant performance difference between high and low achievers after using the GLS. The approach improved the lower achievers’ performance. The interview results revealed that Senior High School Biology students perceived that the Generative Learning Strategy improved the understanding, motivation, retention and self-directed learning of genetic concepts, thereby enhancing their learning outcomes. The study recommended that SHS Biology teachers employ the Generative Learning Strategy in teaching genetic concepts to bridge the performance gap between high and low achievers.

Acoustic conditions in innovative learning environments

New generation or innovative learning environments are being developed in many OECD coun-tries, including Australia and New Zealand. These spaces are often criticized as being 'noisy', dis-tracting, unattenable, and yet, there has been a link between deep learning outcomes and the new generation environments. Education spaces primarily revolve around speaking and listening activities. Although new space typologies have been, and continue to be, developed, the acoustic standards applied to them are often the same as those applied to traditional cellular classroom spaces. This paper presents the results of systematic acoustic testing undertaken within numerous physical spaces for traditional and new generation learning environments in Australia and New Zealand, to compare the differences in performance between traditional cellular classrooms and open plan spaces, and summarise any trends evident in the information. It also acknowledges the differences between traditional cellular spaces and new generation environments as determined from the results.

THE INFLUENCE OF A GENERATIVE LEARNING MODEL BASED ON WAVE MATERIAL COGNITIVE CONFLICT ON STUDENT LEARNING OUTCOMES AT SMAN 5 PAYAKUMBUH

Student learning outcomes in Physics subjects, especially in Wave material, are still low. This is caused by the learning model used in the Wave material not being being the process standards and characteristics of wave learning objectives. A generative learning model based on cognitive conflict is one approach to solving the aforementioned issue. The purpose of this study is to ascertain how student learning outcomes at SMAN 5 Payakumbuh are affected by the use of a generative learning model based on cognitive conflict in Wave content. The type of research is quasi-experimental research with a pretest-posttest Control Group Design. The participants in this study were all SMAN 5 Payakumbuh science class XI students. The information gathered reflects the attitudes, knowledge, and abilities of the pupils toward learning. Two averages are tested for equality as a data analysis technique. The study's findings demonstrate the notable distinctions between classrooms that employ the Problem-Based Learning model and those that employ the Generative learning model based on cognitive conflict. The average student learning outcome in classes that apply the Generative learning model is 83.14 and in classes that apply the Problem-Based Learning model is 77.26. The influence of the generative learning model based on cognitive conflict can be seen in the learning outcomes of students who have been analyzed and hypothesis tested. Based on the hypothesis test, the ttable is 2.006 and the tcount is 2.1. The condition for H0 to be rejected is if ttable < tcount. The tcount value is within the rejection of H0, so HI is accepted. Because all variables are controlled, except the learning model, it can be said that the application of the Generative learning model based on cognitive conflict in Wave material has a positive effect on student learning outcomes.