Learning Task Research Articles

Expressibility-induced concentration of quantum neural tangent kernels

Quantum tangent kernel methods provide an efficient approach to analyzing the performance of quantum machine learning models in the infinite-width limit, which is of crucial importance in designing appropriate circuit architectures for certain learning tasks. Recently, they have been adapted to describe the convergence rate of training errors in quantum neural networks in an analytical manner. Here, we study the connections between the expressibility and value concentration of quantum tangent kernel models. In particular, for global loss functions, we rigorously prove that high expressibility of both the global and local quantum encodings can lead to exponential concentration of quantum tangent kernel values to zero. Whereas for local loss functions, such issue of exponential concentration persists owing to the high expressibility, but can be partially mitigated. We further carry out extensive numerical simulations to support our analytical theories. Our discoveries unveil a fundamental feature of quantum neural tangent kernels, indicating that the issue of their concentration cannot be bypassed merely by transitioning to a local encoding scheme while maintaining high expressibility. This offers valuable insights for the design of wide quantum variational circuit models in practical applications.

Individual differences in parasympathetic functioning across social stressor tasks: Relations with child and parent anxiety

AbstractChildren with elevated behavioural inhibition (BI) show context‐inappropriate fear and dysregulated RSA across stressor tasks. However, few studies have examined dynamic RSA within tasks and relations to parent and child anxiety. Using piecewise growth modelling and multimethod baseline data from an intervention study of 151 3.5–5‐year‐old children and their parents, we examined relations between child social anxiety (SA), parent anxiety and their interaction in predicting children's RSA across social stressor tasks (e.g. learning about unfamiliar peers, Trier Social Stress). Within the sample, 49.63% of children were reported to be White, non‐Hispanic/Latine (n = 67), 22% multiracial (n = 31), 14.81% Asian/Pacific Islander (n = 20) and 12.59% Black/African‐American (n = 17). Furthermore, 64.44% of the parents were reported to be White, non‐Hispanic/Latine (n = 87), 20% Asian/Pacific Islander (n = 27), 13.33% Black/African‐American (n = 18) and 2.22% multiracial (n = 3). Children showed differentiated RSA reactivity and recovery within an anticipatory social learning task based on their level of clinically appraised SA. Relations between child SA and RSA across tasks was moderated by parent anxiety, specifically for dyads matched in anxiety. Findings provide support for the potential influence of both child and parent anxiety on children's parasympathetic nervous system (PNS) responses across specific self‐regulatory tasks.

Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner’s mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner’s cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann–Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI’s potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.

Children’s Expressed Emotions During Playful Learning Games

Studies on software tutoring systems for complex learning have shown that confusion has a beneficial relationship with the learning experience and student engagement (Arguel et al., 2017). Causing confusion can prevent boredom while signs of confusion can serve as a signal of genuine learning and as a predecessor for frustration. There is little to no research on the role of confusion in early childhood education and playful learning, as these studies primarily focus on high school and university students during complex learning tasks. Despite that, the field acknowledges that confusion may be caused by inconsistency between information and a student’s internal model referred to as cognitive disequilibrium known from the theory of cognitive development, which was originally theorized based on observational studies on young children (D’Mello and Graesser, 2012). Therefore, there is reason to expect that the virtues of confusion also apply to young children engaging in learning activities, such as playful learning. To investigate the role of confusion in playful learning, we conducted an observational study, in which the behavior and expressed emotions of young children were collected by familiar pedagogues, using a web app, while they engaged with playful learning games designed for kindergartens. The expressed emotions were analyzed using a likelihood metric to determine the likely transitions between emotions (D’Mello and Graesser, 2012). The preliminary results showed that during short play sessions, children express confusion, frustration, and boredom. Furthermore, the observed emotional transitions were matched with previously established models of affect dynamics during complex learning. We argue that games with a learning objective can benefit by purposely confusing the player and how the player’s confusion may be managed to improve the learning experience.

EXPLORING LEARNING OPPORTUNITIES FOR PRIMARY TEACHERS: THE CASE OF KNOWLEDGE FOR TEACHING EARLY ALGEBRA

Understanding how to constitute and develop opportunities for primary teachers teach early algebra to younger children is still an important research gap. In this paper, we bring results of a research program developed in Brazil over the past five years. We aim to discuss how professional learning opportunities emerged when teachers collectively planned, discussed, and analyzed lessons involving different meanings of the equality symbol and the development of functional thinking. Developed from the perspective of a qualitative-interpretative research, data analyzed consists of curriculum documents, protocols for the resolution of formative tasks, audios and videos collected during teacher education processes for in-service teachers. The results highlight that professional learning tasks, combined with the actions of teacher educators during collective discussions, favored in-service teachers to differentiate and understand the students’ reasoning. Some implications for teacher education as well as the professional development of primary teachers are discussed, especially regarding early algebra thinking, because teachers do not normally have the opportunity to study these contents on their own experiences in school.

6631 A Week With Diabetes: Using Real-Time Continuous Glucose Monitoring In A Simulation Activity With Residents And Fellows

Abstract Disclosure: T. Gallagher: None. C. Donelly: None. R.E. Amori: None. Background: Continuous glucose monitor (CGM) use in managing diabetes mellitus is increasing rapidly. Fellows and residents often prescribe these devices, but most do not themselves have diabetes mellitus and do not use these devices personally. A real-time simulation activity can help physicians in training learn about using the technology in diabetes management and the patient experience living with diabetes mellitus. Objective: Assessing learner’s experiences using a one week real-time CGM simulation. Methods: A real-time simulation using CGM and elements of role playing can enhance knowledge of CGM use and overall diabetes care. Voluntary participants included endocrinology fellows, internal and family medicine residents, and one health professions student. One common structured learning task per day was assigned to all participants over a single seven day period of use in academic year 2022-23. Assessment of each task occurred via personal observations and reflections. Data was collected in an anonymous form and in a group debrief. Results: Eight of 10 participants completed the deidentified post simulation survey and 8/10 participated in the live debrief. Participants reported ease of CGM placement and use. They reported improved insight into the impacts of diabetes on daily activities and increased awareness of diet and exercise impact on glucose levels. Insulin dose calculations were largely correct but errors were seen. All felt the activity was beneficial to their learning. Conclusions: Real-time CGM simulation improves student, resident, and fellow knowledge of the technology and allows the learner to gain insight into a patient’s diabetes self-management. Presentation: 6/3/2024

Astrocytes contribute to the functional differentiation of the hippocampal longitudinal axis during reward and aversion processing in the adult male rat

This study aims to investigate whether glial cells, in particular putative astrocytes, contribute to functional distinctions between the dorsal (DH), intermediate (IH), and ventral (VH) hippocampus. To evaluate this, we performed three different behavioral tasks (i.e., Morris water maze; MWM, Passive avoidance; PA, T-maze place preference; TPP) to determine whether the DH, IH, and VH are necessary for each task. Sensitivity of behavioral tasks was confirmed using lidocaine (2 %, 1 μl) reversible inactivation. Subsequently, we examined the effects of silencing astrocytes, using fluorocitrate (FC, 1 mM/1 μl), into the DH, IH, and VH on these tasks. The effects of drugs were examined separately. We observed that injection of FC into the DH resulted in a significant impairment in MWM performance. In contrast, while FC injections into the IH or VH did not prevent platform localization during the acquisition phase, rats showed difficulty recalling the target zone during the retrieval phase. In the PA test, FC injection into the VH impaired task learning and memory. During the acquisition phase, FC injection into the DH or IH did not differ from the control in the number of shocks; however, during retrieval, there was a significant decrease in the latency before entering the dark chamber. The TPP test performance was impaired by FC injection in the IH. In sum, we show that glial cells, especially astrocytes in specific functional regions of the hippocampus, play distinct roles in processing aversive and rewarding experiences and contribute to the functional organization of the hippocampal longitudinal axis.

Reinforcement Learning with Decoupled State Representation for Robot Manipulations

Deep reinforcement learning has significantly advanced robot manipulations by providing an alternative solution for designing control strategies using raw images as direct inputs. While images offer additional environmental information, the end-to-end policy training manner (from image to action) requires simultaneous representation and task learning by the agent. This often necessitates a substantial number of interaction samples to achieve satisfactory policy performance. Previous works has attempted to address this challenge by learning a visual representation model that encodes the entire image into a low-dimensional vector before the policy training. However, since this vector contains both robot and object information, it inevitably introduces coupling within the state, which can mislead the policy training process. In this study, a novel method called Reinforcement Learning with Decoupled State Representation is proposed to effectively decouple robot and object information within the state representation. Experimental results demonstrate that the proposed method exhibits faster learning speed and achieves superior performance compared to previous methods across various robot manipulation tasks. Moreover, with only 3096 offline images, the proposed method successfully applies to real-world robot pushing tasks, which demonstrates its high practicability.

Research on underwater target recognition based on auditory EEG signal features and deep learning

Target recognition is a key technical link in hydroacoustic detection, which has a broad application prospect in the fields of marine safety and resource exploration. In recent years, artificial involvement of underwater target recognition methods based on analysis of line spectra, auditory spectra and other characteristics are broadly utilized in actual engineering applications, with their unique characteristics. Meanwhile, the learning tasks of human-computer interaction have been widely used, for machine learning and deep learning are also developing rapidly. Therefore, in this paper, auditory EEG signals under the excitation of underwater targets' signals are used to carry out recognition research by combining human brain perception, artificial analysis, SVM and ResNet with atrous convolution. The results show that the recognition rate of four types of ship radiated noise can reach 94.35% using the ResNet with atrous convolution, which can effectively classify and recognize underwater targets.

IMPLEMENTATION OF THE CRT APPROACH ASSISTED BY WORDWALL MEDIA IN IMPROVING THE POETRY WRITING SKILLS OF CLASS XE STUDENTS OF SMA NEGERI 5 KUPANG

Abstract The aim of this research is to improve students' ability to write in class using a Culturally Responsive Teaching (CRT) approach with the help of Wordwall media. The CRT approach is a learning method used to appreciate and respect the diversity of students. Wherever, this approach will increase the effectiveness of writing poetry in the learning process. The method used in this research is called Classroom Action Research (PTK), which was carried out over two semesters of lectures. This research consists of four stages: preparation, action, evaluation, and reflection. The object of the research was 36 students in class X E of SMA Negeri 5 Kupang. The data collection methods used are observation and checking learning tasks. Qualitative descriptive analysis was used in collecting research data. The results of this research were obtained by applying the Culturally Responsive Teaching (CRT) approach. Keywords: Culturally Responsive Teaching (CRT), Writing poetry, and Wordwall.

A category incremental continuous learning model for imbalance arrhythmia detection

Abstract The development of efficient arrhythmia detection systems is crucial for physiological measurements and computer-aided diagnosis. Existing systems rely mainly on offline learning methods and lack the ability to assimilate new data or recognize emerging categories. To address these challenges, this study introduces an arrhythmia detection model that is resistant to data imbalance and has continuous learning capabilities, specifically for incremental learning of new ECG data categories. The system incorporates constraints derived from the new class data and implements a dynamic mechanism for updating connection weights, facilitating the incremental continual learning of classes. Confronted with the problem of models forgetting the original data and overfitting with the added data in continuous learning, we introduce a data balancing method by regularization to balance the model's memory and learning of the two types of data. Facing the data imbalance problem in continuous learning, we introduce a posteriori probability weighting strategy. This strategy assigns greater importance to high-value samples based on the model's posterior residual kernel density estimates. Comprehensive testing of the model using various datasets from the MIT-BIH database indicates superior performance in incremental learning tasks. The results reveal that the model not only excels in class incremental learning but also ensures effective balancing across different data classes.

Penerapan Manajemen Balance Scorecard (BSC): Studi Kasus di MIS Miftahul Ulum Kec. Serpong Utara Kota Tangerang Selatan

The implementation of Balanced Scorecard (BSC) management at MIS Miftahul Ulum, Serpong Utara District, South Tangerang City is still facing some obstacles. The lack of understanding and accurate data, structured systems, resources, and accountability culture are the main barriers. This has resulted in the suboptimal achievement of strategic goals, such as learning, internal process, customer, and financial indicators that are not yet maximized. The purpose of this study is to determine the extent to which the implementation of Balanced Scorecard (BSC) management at MIS Miftahul Ulum, reviewed from four perspectives, namely: financial, customer, internal process, and learning and growth. This research is qualitative, with data collection methods including interviews, observations, and documentation. Data validity is ensured through triangulation, and data analysis techniques include data collection, data reduction, data presentation, and conclusion drawing. The research shows that the quality of education at MIS Miftahul Ulum, Serpong Utara District, South Tangerang City has improved compared to previously. However, some adjustments need to be made in the implementation of Balanced Scorecard (BSC). Conclusion: MIS Miftahul Ulum focuses on diversifying funding sources, increasing the efficiency of fund management, and increasing income from voluntary donations. Customer satisfaction and making MIS Miftahul Ulum the main school of choice for students and parents. Progress in its internal processes by implementing a quality curriculum, an effective teaching and learning process, and timely completion of teaching and learning tasks, and a quality curriculum provides a strong foundation for students to learn and grow. Effectiveness of the teaching and learning process: An effective teaching and learning process allows students to reach their full learning potential. Timely completion of teaching and learning tasks: Timely completion of teaching and learning tasks ensures that students have enough time to learn and understand the subject matter.

Few-Shot Anomaly Detection via Category-Agnostic Registration Learning.

Most existing anomaly detection (AD) methods require a dedicated model for each category. Such a paradigm, despite its promising results, is computationally expensive and inefficient, thereby failing to meet the requirements for real-world applications. Inspired by how humans detect anomalies, by comparing a query image to known normal ones, this article proposes a novel few-shot AD (FSAD) framework. Using a training set of normal images from various categories, registration, aiming to align normal images of the same categories, is leveraged as the proxy task for self-supervised category-agnostic representation learning. At test time, an image and its corresponding support set, consisting of a few normal images from the same category, are supplied, and anomalies are identified by comparing the registered features of the test image to its corresponding support image features. Such a setup enables the model to generalize to novel test categories. It is, to our best knowledge, the first FSAD method that requires no model fine-tuning for novel categories: enabling a single model to be applied to all categories. Extensive experiments demonstrate the effectiveness of the proposed method. Particularly, it improves the current state-of-the-art (SOTA) for FSAD by 11.3% and 8.3% on the MVTec and MPDD benchmarks, respectively. The source code is available at https://github.com/Haoyan-Guan/CAReg.

Knowledge enhanced multi-task learning for simultaneous optimization of human parsing and pose estimation

Human parsing and pose estimation are both closely related to the human body structure, and it is advantageous to deal with both together in order to assist each other’s learning. Nevertheless, the two tasks have their own unique characteristics, and it is therefore difficult to efficiently and simultaneously obtain a good performance for both tasks in one single multi-task learning (MTL) network. This paper proposes a new MTL network (named “KDNet”) to enhance the communication between human parsing and pose estimation through knowledge distillation so as to improve the simultaneous learning of both tasks. In the proposed KDNet, a consistent representation module is used to enhance the related information sharing between the tasks, and a single-task model-based loss weighting method is developed to balance the loss levels. The proposed KDNet has been verified across three benchmark datasets, and in comparison to other state-of-the-art methods it achieves substantial performance gains without increasing model parameters during inference. The codes are shared at https://github.com/Yhdian/KDNet.git.

A critical discourse analysis of cultural representation in language learning tasks: The case of an English language textbook used in Indonesian secondary schools

ABSTRACT The present study aims to examine the extent to which language learning tasks promote intercultural awareness. Drawing on Kachru’s (1985) circles framework, the analysis of 15 chapters and 107 tasks shows that more than fifty percent of the analyzed tasks do not belong to any of Kachru’s circles. In terms of Baker’s (2011) ICA levels, the majority of the tasks demonstrate the basic level (Level 1); none belongs to the advanced level (Level 2); and only fourteen percent exhibit the top level of intercultural awareness (Level 3). These findings suggest the need to incorporate more intercultural dimensions into language learning tasks to enhance students’ intercultural awareness.

Exponential Learning Advantages with Conjugate States and Minimal Quantum Memory

The ability of quantum computers to directly manipulate and analyze quantum states stored in quantum memory allows them to learn about aspects of our physical world that would otherwise be invisible given a modest number of measurements. Here we investigate a new learning resource, which could be available to quantum computers in the future—measurements on the unknown state accompanied by its complex conjugate ρ⊗ρ∗. For a certain shadow tomography task, we surprisingly find that measurements on only copies of ρ⊗ρ∗ can be exponentially more powerful than measurements on ρ⊗K, even for large K. This expands the class of provable exponential advantages using only a constant overhead quantum memory, or minimal quantum memory, and we provide a number of examples where the state ρ∗ is naturally available in both computational and physical applications. In addition, we precisely quantify the power of classical shadows on single copies under a generalized Clifford ensemble and give a class of quantities that can be efficiently learned. The learning task we study in both the single copy and quantum memory settings is physically natural and corresponds to real-space observables with a limit of bosonic modes, where it achieves an exponential improvement in detecting certain signals under a noisy background. We quantify a new and powerful resource in quantum learning, and we believe the advantage may find applications in improving quantum simulation, learning from quantum sensors, and uncovering new physical phenomena. Published by the American Physical Society 2024

Phoenixes at LLMs4OL 2024 Tasks A, B, and C: Retrieval Augmented Generation for Ontology Learning

Large language models (LLMs) showed great capabilities in ontology learning (OL) where they automatically extract knowledge from text. In this paper, we proposed a Retrieval Augmented Generation (RAG) formulation for three different tasks of ontology learning defined in the LLMs4OL Challenge at ISWC 2024. For task A - term typing - we considered terms as a query and encoded the query through the Query Encoder model for searching through knowledge base embedding of types embeddings obtained through Context Encoder. Next, using Zero-Shot Prompt template we asked LLM to determine what types are appropriate for a given term within the term typing task. Similarly, for Task B, we calculated the similarity matrix using an encoder-based transformer model, and by applying the similarity threshold we considered only similar pairs to query LLM to identify whatever pairs have the "is-a" relation between a given type and in a case of having the relationships which one is "parent" and which one is "child". In final, for Task C -- non-taxonomic relationship extraction -- we combined both approaches for Task A and B, where first using Task B formulation, child-parents are identified then using Task A, we assigned them an appropriate relationship. For the LLMs4OL challenge, we experimented with the proposed framework over 5 subtasks of Task A, all subtasks of Task B, and one subtask of Task C using Mistral-7B LLM.

From shallows to depths: unveiling hybrid synthetic data modeling for enhanced learning with privacy considerations in naturally imbalanced datasets

Data serves as the foundational element that drives model development and performance in machine learning and deep learning. The learning algorithms are as good as data on which they are trained on. Data constrained environments pose serious threat to the effectiveness of learning algorithms. Data limitations stem from a range of factors, including regulatory restrictions, privacy concerns, and the inherent scarcity of relevant data. This constrained availability of data often leads to class imbalance problem in the context of classification tasks. To address the challenges of limited data, the algorithmic generated data, also known as synthetic data, is gaining significant traction as a cost-effective, readily available, and secure alternative. Synthetic data generation techniques can be employed to enhance dataset size by augmenting data samples and to address class imbalance by increasing the number of minority class instances. These techniques generally fall into two main categories: distance-based shallow models and probability estimation-based deep generative models. Shallow interpolation-based models generate new data points within the local space between existing data points, while deep density estimation based models generate new data by learning the whole distribution of data. . In the context of smaller datasets, deep generative models often struggle to accurately estimate the probability distribution of whole data. To effectively represent the global data distribution, these deep models require initial starting data samples to guide their approximation. This paper examines the potential of integration of shallow and deep generative models in the data generation pipeline for effective synthetic data augmentation which furthers enhanced learning and generalization of downstream tasks. In this work, we present a hybrid approach of tabular data generation involving mixed type data attributes (continuous, discrete) and pay special attention to data imbalance and insufficient data problems. We introduce the Hybrid Data Balancing and Augmentation Approach for Mixed Tabular Data (HDBA-MTD), specifically designed to synthesize samples for underrepresented labels of output class and address issues of insufficient data instances. This approach enhances training data diversity, thereby paying special attention to the downstream classification and generalization performance. Experiments are carried out using benchmark datasets to assess the practicality of the presented hybrid model in real-world scenarios. This work has also attempted to quantify the privacy preservability for real data concerning ethical considerations and data security circumstances. The evaluation and analysis of these experiments show that the present hybrid model performs favorably compared to other current hybrid synthetic data generation methods.

Kernels of Motor Memory Formation: Temporal Generalization in Bimanual Adaptation.

In daily life, we coordinate both simultaneous and sequential bimanual movements to manipulate objects. Our ability to rapidly account for different object dynamics suggests there are neural mechanisms to quickly deal with them. Here we investigate how actions of one arm can serve as a contextual cue for the other arm, and facilitate adaptation. Specifically, we examine the temporal characteristics that underlie motor memory formation and recall, by testing the contextual effects of prior, simultaneous, and post contralateral arm movements in both male and female human participants. To do so, we measure their temporal generalization in three bimanual interference tasks. Importantly, the timing context of the learned action plays a pivotal role in the temporal generalization. While motor memories trained with post adaptation contextual movements generalize broadly, motor memories trained with prior contextual movements exhibit limited generalization, and motor memories trained with simultaneous contextual movements do not generalize to prior or post contextual timings. This highlights temporal tuning in sensorimotor plasticity: different training conditions yield substantially different temporal generalization characteristics. Since these generalizations extend far beyond any variability in training times, we suggest that the observed differences may stem from inherent differences in the use of prior, current and post-adaptation contextual information in the generation of natural behavior. This would imply differences in the underlying neural circuitry involved in learning and executing the corresponding coordinated bimanual movements.Significance Statement This study addresses a fundamental question in the field of sensorimotor neuroscience of how multiple movements are temporally linked within a single motor memory. We examine the temporal generalization of motor memory formation by varying the timing of contextual movements associated with a learned motor memory across a range of prior, current, and post adaptation movement times using a bimanual motor learning task. We observed distinct patterns of temporal generalization based on whether the contextual movements occurred prior to, simultaneously with, or after the adaptation movement. For the first time, our findings reveal that the timing of contextual movements is crucial in the formation and generalization of motor memories.

User Engagement with Interactive Educational Videos: Relations with Task Value, Cognitive Load, and Learning Satisfaction

Interactive educational videos have grown in popularity in the higher education setting over the last two decades. This exploratory study investigates the mediating and moderating effects of user engagement (UE) on the relationships between the antecedents of UE (i.e., intrinsic load, extraneous load, germane load, and task value) and learning satisfaction. A cross-sectional survey study was conducted in June 2023. We conveniently sampled 49 year-one undergraduate students, who completed a self-report questionnaire consisting of items pertaining to cognitive load, task value, user engagement, and learning satisfaction. Data was analysed using hierarchical linear regression models. Findings revealed that reward fully mediated the relationship between germane load and learning satisfaction. In addition, reward was also found to moderate the relation between (a) germane load and learning satisfaction, and (b) task value and learning satisfaction. These findings highlight the importance of creating rewarding experiences when designing interactive educational videos.