Learning Task Research Articles

Modulating social learning-induced evaluation updating during human sleep

People often change their evaluations upon learning about their peers’ evaluations, i.e., social learning. Given sleep’s vital role in consolidating daytime experiences, sleep may facilitate social learning, thereby further changing people’s evaluations. Combining a social learning task and the sleep-based targeted memory reactivation technique, we asked whether social learning-induced evaluation updating can be modulated during sleep. After participants had indicated their initial evaluation of snacks, they learned about their peers’ evaluations while hearing the snacks’ spoken names. During the post-learning non-rapid-eye-movement sleep, we re-played half of the snack names (i.e., cued snack) to reactivate the associated peers’ evaluations. Upon waking up, we found that the social learning-induced evaluation updating further enlarged for both cued and uncued snacks. Examining sleep electroencephalogram (EEG) activity revealed that cue-elicited delta-theta EEG power and the overnight N2 sleep spindle density predicted post-sleep evaluation updating for cued but not for uncued snacks. These findings underscore the role of sleep-mediated memory reactivation and the associated neural activity in supporting social learning-induced evaluation updating.

Serotonergic input from the dorsal raphe nucleus shapes learning-associated odor responses in the olfactory bulb.

Neural activity in the olfactory bulb (OB) can represent odor information during different brain and behavioral states. For example, the odor responses of mitral/tufted (M/T) cells in the OB change during learning of odor-discrimination tasks and, at the network level, beta power increases and the high gamma (HG) power decreases during odor presentation in such tasks. However, the neural mechanisms underlying these observations remain poorly understood. Here, we investigate whether serotonergic modulation from the dorsal raphe nucleus (DRN) to the OB is involved in shaping activity during the learning process in a go/no-go task in mice. Fiber photometry was used to record the population activity of DRN serotonergic neurons during a go/no-go task. Invivo electrophysiology was used to record neural activity (single units and local field potentials) in the OB during the go/no-go task. Real-time place preference (RTPP) and intracranial light administration in a specific subarea (iClass) tests were used to assess the ability of mice to encoding reward information. Odor-evoked population activity in serotonergic neurons in the DRN was shaped during the learning process in a go/no-go task. In the OB, neural activity from oscillations to single cells showed complex, learning-associated changes and ability to encode information during an odor discrimination task. However, these properties were not observed after ablation of DRN serotonergic neurons. The activity of neural networks and single cells in the OB, and their ability to encode information about odor value, are shaped by serotonergic projections from the DRN.

Fusing crops representation into snippet via mutual learning for weakly supervised surveillance anomaly detection

AbstractIn recent years, the challenge of detecting anomalies in real‐world surveillance videos using weakly supervised data has emerged. Traditional methods, utilising multi‐instance learning (MIL) with video snippets, struggle with background noise and tend to overlook subtle anomalies. To tackle this, the authors propose a novel approach that crops snippets to create multiple instances with less noise, separately evaluates them and then fuses these evaluations for more precise anomaly detection. This method, however, leads to higher computational demands, especially during inference. Addressing this, our solution employs mutual learning to guide snippet feature training using these low‐noise crops. The authors integrate multiple instance learning (MIL) for the primary task with snippets as inputs and multiple‐multiple instance learning (MMIL) for an auxiliary task with crops during training. The authors’ approach ensures consistent multi‐instance results in both tasks and incorporates a temporal activation mutual learning module (TAML) for aligning temporal anomaly activations between snippets and crops, improving the overall quality of snippet representations. Additionally, a snippet feature discrimination enhancement module (SFDE) refines the snippet features further. Tested across various datasets, the authors’ method shows remarkable performance, notably achieving a frame‐level AUC of 85.78% on the UCF‐Crime dataset, while reducing computational costs.

Machine learning without a processor: Emergent learning in a nonlinear analog network

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN-an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Transcribing Medieval Manuscripts for Machine Learning

This article focuses on the transcription of medieval manuscripts. Whereas problems of transcription have long interested medievalists, few workable options in the era of printed editions were available besides normalisation. The automation of this process, known as handwritten text recognition (HTR), has made new kinds of digital text creation possible, but also has foregrounded the necessity of theorising transcription in our scholarly practices. We reflect here on different notions of transcription against the backdrop of changing text technologies. Moreover, drawing on our own research on medieval Latin Bibles, we present general guidelines for customizing transcription schemes, arguing that they must be designed with specific research questions and scholarly end use in mind. Since we are particularly interested in the scribal contribution to the production of codices, our transcription guidelines aim to capture abbreviations and orthographic variation between different textual witnesses for downstream machine learning tasks. In the final section of the article, we discuss a few examples of how the HTR-created transcriptions allow us to address new questions at scale in medieval manuscripts, such as textual variance across witnesses, the prediction of a change in scribal hands within a single manuscript as well as the profiling of individual and regional scribal characteristics.

Image authentication method based on Fourier zero-frequency replacement and single-pixel self-calibration imaging by diffractive deep neural network

The diffractive deep neural network is a novel network model that applies the principles of diffraction to neural networks, enabling machine learning tasks to be performed through optical principles. In this paper, a fully optical authentication model is developed using the diffractive deep neural network. The model utilizes terahertz light for propagation and combines it with a self-calibration single-pixel imaging model to construct a comprehensive optical authentication system with faster authentication speed. The proposed system filters the authentication images, establishes an optical connection with the Fourier zero-frequency response of the illumination pattern, and introduces the signal-to-noise ratio as a criterion for batch image authentication. Computer simulations demonstrate the fast speed and strong automation performance of the proposed optical authentication system, suggesting broad prospects for the combined application of diffractive deep neural networks and optical systems.

Perirhinal cortex learns a predictive map of the task environment

Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chronic two-photon calcium imaging, population analysis, and computational modeling revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that can progressively be decoded earlier in the trial as training advances and that generalize as animals learn new contingencies. Stimulus-outcome associations are linked to prospective network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and systemic pharmacological perturbation. We propose that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.

Hippocampus and striatum show distinct contributions to longitudinal changes in value-based learning in middle childhood

The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children’s learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.

Systemic pharmacological suppression of neural activity reverses learning impairment in a mouse model of Fragile X syndrome

The enhancement of associative synaptic plasticity often results in impaired rather than enhanced learning. Previously, we proposed that such learning impairments can result from saturation of the plasticity mechanism (Nguyen-Vu et al., 2017), or, more generally, from a history-dependent change in the threshold for plasticity. This hypothesis was based on experimental results from mice lacking two class I major histocompatibility molecules, MHCI H2-Kb and H2-Db (MHCI KbDb−/−), which have enhanced associative long-term depression at the parallel fiber-Purkinje cell synapses in the cerebellum (PF-Purkinje cell LTD). Here, we extend this work by testing predictions of the threshold metaplasticity hypothesis in a second mouse line with enhanced PF-Purkinje cell LTD, the Fmr1 knockout mouse model of Fragile X syndrome (FXS). Mice lacking Fmr1 gene expression in cerebellar Purkinje cells (L7-Fmr1 KO) were selectively impaired on two oculomotor learning tasks in which PF-Purkinje cell LTD has been implicated, with no impairment on LTD-independent oculomotor learning tasks. Consistent with the threshold metaplasticity hypothesis, behavioral pre-training designed to reverse LTD at the PF-Purkinje cell synapses eliminated the oculomotor learning deficit in the L7-Fmr1 KO mice, as previously reported in MHCI KbDb−/−mice. In addition, diazepam treatment to suppress neural activity and thereby limit the induction of associative LTD during the pre-training period also eliminated the learning deficits in L7-Fmr1 KO mice. These results support the hypothesis that cerebellar LTD-dependent learning is governed by an experience-dependent sliding threshold for plasticity. An increased threshold for LTD in response to elevated neural activity would tend to oppose firing rate stability, but could serve to stabilize synaptic weights and recently acquired memories. The metaplasticity perspective could inform the development of new clinical approaches for addressing learning impairments in autism and other disorders of the nervous system.

The relationship between metacognitive monitoring, non-verbal intellectual ability, and memory performance in kindergarten children

When assessing their certainty, children are often poor at accurately monitoring their level of learning. The study examined the relationships between memory performance, intellectual ability, and metacognitive monitoring accuracy in kindergarten children. We also explored whether specific thresholds in memory performance and non-verbal intellectual ability influence metacognitive monitoring accuracy to identify group-specific patterns that might be masked by an overall linear analysis. We assessed the monitoring discrimination of 290 kindergarteners (Mage 6 years) using a paired-associates learning task. Results showed small correlations between task performance, intellectual ability, and metacognitive monitoring. Non-verbal intellectual ability provided explanatory value for monitoring accuracy beyond memory performance. We observed group-specific results consistent with the unskilled-and-unaware effect; children with the highest memory skills were more effective at discriminating between correct and incorrect answers than their peers with the lowest memory skills. However, kindergarteners with the highest non-verbal intellectual abilities did not demonstrate greater cognitive adaptability in novel tasks, as their monitoring accuracy was comparable to that of peers with average or lower intellectual abilities. Findings indicate that both task performance and non-verbal intellectual ability are relevant for monitoring accuracy, but the impact of non-verbal intellectual ability was less significant than anticipated. The modest correlation suggests that kindergarteners' non-verbal intellectual ability and metacognitive monitoring abilities operate relatively independently.

Hippocampus and striatum show distinct contributions to longitudinal changes in value-based learning in middle childhood.

The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children's learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.

Multitarget adaptive virtual fixture based on task learning for hydraulic manipulator

AbstractHeavy‐duty construction tasks implemented by hydraulic manipulators are highly challenging due to unstructured hazardous environments. Considering many tasks have quasirepetitive features (such as cyclic material handling or excavation), a multitarget adaptive virtual fixture (MAVF) method by teleoperation‐based learning from demonstration is proposed to improve task efficiency and safety, by generating an online variable assistance force on the master. First, the demonstration trajectory of picking scattered materials is learned to extract its distribution and the nominal trajectory is generated. Then, the MAVF is established and adjusted online by a defined nonlinear variable stiffness and position deviation from the nominal trajectory. An energy tank is introduced to regulate the stiffness so that passivity and stability can be ensured. Taking the operation mode without virtual fixture (VF) assistance and with traditional weighted adaptation VF as comparisons, two groups of tests with and without time delay were carried out to validate the proposed method.

CentroIDA: Cross-domain class discrepancy minimization based on accumulative class-centroids for Imbalanced Domain Adaptation

Unsupervised Domain Adaptation (UDA) is a critical task in transfer learning, which seeks to apply the knowledge acquired in one domain (the source domain) to a different but related domain (the target domain). In this context, the two domains exhibit Out-Of-Distribution(OOD), and the label information of the target domain is unavailable. Although numerous UDA methods have made significant strides in mitigating the discrepancies in feature distributions across domains, they often neglect a more prevalent real-world scenario. This scenario, referred to as Imbalanced Domain Adaptation (IDA) task, is characterized by the coexistence of differences in both feature and label distributions. Our empirical analysis suggests that overlooking the disparities in label distributions can severely compromise the performance of the model, thereby limiting the generalizability of domain adaptation models. Consequently, our objective is to introduce a model capable of concurrently addressing differences in both feature and label distributions, thereby fostering a broader application of domain adaptation methodologies. In this paper, we propose a cross-domain class discrepancy minimization method based on accumulative class-centroids for IDA, termed as centroIDA. The strategies of accumulative class-centroids alignment and class-wise feature alignment pay heed to the relationship between cross-domain intra-class features and inter-class features, thereby facilitating conditional feature alignment to overcome both feature and label distribution differences simultaneously. Our experimental evaluation on OfficeHome and DomainNet datasets reveals that, in comparison to TIToK (a novel method also designed to tackle the IDA problem), our method enhances the average accuracy by 5.6% and 2.7%, respectively. Furthermore, as the label shift intensifies, our method consistently outperforms other methods, demonstrating a robust resistance to label shift.

Weakly Supervised AUC Optimization: A Unified Partial AUC Approach.

Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to as weak supervision. In this work, we present WSAUC, a unified framework for weakly supervised AUC optimization problems, which covers noisy label learning, positive-unlabeled learning, multi-instance learning, and semi-supervised learning scenarios. Within the WSAUC framework, we first frame the AUC optimization problems in various weakly supervised scenarios as a common formulation of minimizing the AUC risk on contaminated sets, and demonstrate that the empirical risk minimization problems are consistent with the true AUC. Then, we introduce a new type of partial AUC, specifically, the reversed partial AUC (rpAUC), which serves as a robust training objective for AUC maximization in the presence of contaminated labels. WSAUC offers a universal solution for AUC optimization in various weakly supervised scenarios by maximizing the empirical rpAUC. Theoretical and experimental results under multiple settings support the effectiveness of WSAUC on a range of weakly supervised AUC optimization tasks.

Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Vulnerability to semantic and phonological interference in normal aging and amnestic mild cognitive impairment (aMCI).

To determine whether the increased vulnerability to semantic interference previously observed in amnestic mild cognitive impairment (aMCI) is specifically associated with semantic material or if it also affects other types of material, suggesting generalized executive and inhibitory impairment. Seventy-two participants divided into two groups (33 aMCI, 39 normal control [NC]) matched for age and education were included. They completed a comprehensive neuropsychological examination, the French version of the Loewenstein Acevedo Scale for Semantic Interference and Learning (LASSI-L; semantic interference test), and a homologous experimental phonological test, the phonological interference and learning test. Independent sample t tests, mixed analysis of variance (ANOVA), and analysis of covariance (ANCOVA) on memory and interference scores were conducted to compare memory and interference in both conditions for both groups. For memory scores, results revealed significant main effects of group (NC > aMCI) and condition (semantic > phonological) and significant interactions (poorer performance in the semantic condition for aMCI). aMCI committed more phonological false recognition errors, were disproportionately more vulnerable to retroactive semantic interference, and showed a higher percentage of intrusion errors associated with proactive semantic interference than NC. To our knowledge, this is the first study to compare vulnerability to interference in aMCI and normal aging with two similarly designed semantic and phonological word list learning tasks. Taken together, our results suggest that aMCI present with broad difficulties in source memory and inhibition, but that impaired deep semantic processing results in additional semantic intrusion errors during proactive interference and impacts their ability to show good recall after an interference list (greater semantic retroactive interference). Results are discussed according to the level-of-processing and activation/monitoring theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hilbert Curve Projection Distance for Distribution Comparison.

Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbert curve projection (HCP) distance, to measure the distance between two probability distributions with low complexity. In particular, we first project two high-dimensional probability distributions using Hilbert curve to obtain a coupling between them, and then calculate the transport distance between these two distributions in the original space, according to the coupling. We show that HCP distance is a proper metric and is well-defined for probability measures with bounded supports. Furthermore, we demonstrate that the modified empirical HCP distance with the Lp cost in the d-dimensional space converges to its population counterpart at a rate of no more than O(n-1/2max{d,p}). To suppress the curse-of-dimensionality, we also develop two variants of the HCP distance using (learnable) subspace projections. Experiments on both synthetic and real-world data show that our HCP distance works as an effective surrogate of the Wasserstein distance with low complexity and overcomes the drawbacks of the sliced Wasserstein distance.

Quantum machine learning with Qiskit: Evaluating regression accuracy and noise impact

AbstractQuantum machine learning (QML) can be employed in solving complicated machine learning tasks although the performance in examining the regression processes is only barely understood. Knowledge gaps are intended to be closed by studying modelling performance of QML in regression tasks, with emphasis being dedicated to scaling up and ability to resist noise. The regression part offers the following functions that include straight line and complex operations. Furthermore, the authors employ quantum neural networks generated using Qiskit to perform experiments. The results demonstrate that QML has a remarkable level of accuracy in basic regressions, reaching a maximum of 97%. Nevertheless, there are difficulties in representing intricate functions, such as 5 × cos(x), which results in a noticeable decline in performance. The work deals with the influence of noise and IERs from imperfect hardware on the efficiency of QML algorithms providing insight into the core obstacles. The result of a detailed examination of the results that have tested the powers and limits of QML in the development of regression applications is represented. The future direction of research and development will be defined by the results obtained in it.

Deep Learning Approaches for Ear Biometrics: A Novel Approach

This paper presents a novel deep learning model for ear biometrics, achieving state-of-the-art performance through the integration of transfer learning and data augmentation. Ear biometrics has garnered significant interest due to the ear's unique and stable characteristics, making it a viable modality for biometric identification. Traditional methods often falter under variations in lighting, pose, and occlusion, but deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown exceptional capability in overcoming these challenges by learning robust and discriminative features. Our comprehensive review explores the application of CNNs, Recurrent Neural Networks (RNNs), transfer learning, attention mechanisms, and multimodal fusion in ear recognition, verification, and identification tasks. Our model demonstrates remarkable recognition and verification accuracy, underscoring the potential of ear images as a reliable biometric modality. Experimental results show a recognition rate of 99.2% and an equal error rate (EER) of 0.8%, highlighting the effectiveness of our approach in real-world scenarios. Future work will involve expanding the dataset, exploring alternative deep learning architectures, and enhancing the robustness of ear biometric systems against security threats. Key Words: Ear Biometrics, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, Attention Mechanisms, Multimodal Fusion.

Distance Learning through the Grand Egyptian Museum: Leveraging Modern Technology to Teach about Ancient Egypt among Schools and National Museums

By considering the increasing emphasis on lifelong learning and the ongoing significant debates over the value and utility of digital technologies in accelerating long-established learning tasks, the paper suggests new approaches for the national museums of Egypt to incorporate distance learning and museum-based online programs as a means to improve their traditional educational programs. This would significantly help school students better understand the ancient history of Egypt, “Pharaonic Egypt”, which is an essential part of their formal school curriculum. Moreover, it provides much-needed support to museums with limited or insufficient educational programs to promote more meaningful learning experiences. The proposed approach can be effectively implemented through the Grand Egyptian Museum (GEM), the world’s largest archaeological museum complex dedicated to the culture of ancient Egyptian Pharaonic Culture. The study discusses the GEM’s educational and digital infrastructure capabilities in creating a robust online learning platform that provides access to rich educational content and digital resources across Egypt’s various geographic regions, enhancing museum education concerning pharaonic Egypt in line with the state’s cultural policy to strengthen national identity among future generations. By analyzing both qualitative and quantitative data, the study investigates the potential benefits of integrating online digital technologies into the pedagogical and educational practices of Egypt’s national museums. It concludes by providing practical recommendations for future research and actions encouraging online learning among Egyptian museums, aligned with recent national efforts toward Egypt’s digital transformation.