External Attention Research Articles

Hypervigilance strikes a balance between external and internal attention: behavioral and modeling evidence from the switching attention task.

Hypervigilance involves increased attentional scanning of the environment to facilitate the detection of possible threats. Accordingly, this state is mostly bound to external attention and as a corollary, it might be detrimental to internal attention and further affect attentional balance defined as the ability to switch dynamically between these two domains. In the current study, we aimed to address this question and induced hypervigilance in 49 healthy participants through the presentation of a task-unrelated aversive sound while they performed the switching attention task (SAT), which was previously devised to study attentional balance. The skin conductance response results, as well as subjective sound ratings, confirmed that the hypervigilance manipulation was successful. At the behavioral level, hypervigilance led to a more symmetrical balance between internal and external attention compared to the control and neutral conditions, where it was asymmetrical, replicating previous studies. Moreover, using a drift diffusion model, we found that hypervigilance reduced the drift rate for internal repetition trials, suggesting that hypervigilance possibly caused an impaired shielding of internal attention.

Combined query embroidery image retrieval based on enhanced CNN and blend transformer.

Embroidery images carry rich historical information and are an important form of embroidery art. In the field of combination query image retrieval, how to efficiently retrieve the embroidery image information required by users has become a current research challenge. In recent years, convolutional neural networks (CNNs) have achieved significant success in image feature extraction, but they tend to focus on local information, making it easy to ignore global context information when processing such textured embroidery images. Therefore, we propose a combination query retrieval method for embroidery images. First, we propose Blend-Transformer, which introduces Group External Attention (GEA). GEA can integrate feature information from three different dimensions, effectively capturing the local and global context information of embroidery images. Second, we propose Enhanced CNN, which introduces Shuffle Attention (SA), regrouping the reference image features extracted by CNN and reaggregating them by channel to enhance the richness of embroidery image feature information. Through experiments on the TCE-S and ICR2020 standard datasets, we verify the excellent performance of the proposed algorithm in embroidery image retrieval. Our method fills the gap in embroidery image retrieval research and provides a new perspective for the protection of embroidery art.

Influences of Attentional Focus on Across- and Within-Sentence Variability in Adults Who Do and Do Not Stutter.

Research has found an advantage to maintaining an external attentional focus while speaking as an increase in accuracy and a decrease in across-sentence variability has been found when producing oral-motor and speech tasks. What is not clear is how attention affects articulatory variability both across and within sentences, or how attention affects articulatory control in speakers who stutter. The purpose of this study was to investigate the effects of an internal versus external attention focus on articulatory variability at the sentence level. This study used linear (spatial-temporal index [STI]) and nonlinear (recurrence quantification analysis [RQA]) indices to measure lip aperture variability in 10 adults who stutter (AWS) and 15 adults who do not stutter (ANS) while they repeated sentences under an internal versus external attentional focus, virtual reality task (withVR.app; retrieved December 2023 from https://therapy.withvr.app). Four RQA measures were used to calculate within sentence variability including percent recurrence, percent determinism (%DET), stability (MAXLINE), and stationarity (TREND). Sentence duration measures were also obtained. AWS' movement durations were significantly longer than those of the ANS across conditions, and the AWS were more affected by the attentional focus shifts as their speech rate significantly increased when speaking with an external focus. AWS' speech patterns were also significantly more deterministic (%DET) and stable (MAXLINE) across attentional focus conditions compared to those of the ANS. Both groups showed an effect from attentional shifts as they exhibited less variability (i.e., more consistent) across sentences (STI) and less determinism (%DET) and stability (MAXLINE) within sentences when repeating sentences under an external attentional focus. STI values were not significantly different between the AWS and ANS for the internal or external attentional focus tasks. There were no significant main effects for group or condition for TREND; however, a main effect for sentence type was found. Results suggest that AWS use a more restrictive and less flexible approach to movement and that an external focus fosters more flexibility and thus responsiveness to external factors.

Rolling Bearing Fault Diagnosis Model Based on External Attention Integrated Convolutional Neural Network under Imbalanced Data Conditions

Abstract Rolling bearings are essential components in numerous mechanical systems, and their failure can result in considerable downtime and expensive repairs. Therefore, accurate and timely fault diagnosis is vital for effective predictive maintenance and overall reliability. Traditional diagnostic methods often struggle with complex and non-stationary signals, compounded by issues of data imbalance in 
 realworld scenarios. A method for diagnosing rolling bearing faults has been developed in this paper utilizing External Attention (EA), Convolutional Neural Networks (CNN), and Continuous Wavelet Transform (CWT), specifically addressing the challenge of imbalanced sample data. This approach offers significant advantages, including a reduction in complexity by eliminating the need for data augmentation and leveraging external attention for enhanced feature extraction from samples. Compared to other attention mechanisms, this method demonstrates outstanding performance on both training and testing sets with imbalanced samples, exhibiting minimal overfitting tendencies. The proposed CWT-EACNN method effectively addresses the challenge of imbalanced sample data in rolling bearing fault diagnosis, demonstrating exceptional performance and reduced complexity.

Efficient Semantic Segmentation for Large-Scale Agricultural Nursery Managements via Point Cloud-Based Neural Network

Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization of point cloud data, offering innovative technical support for modern horticulture in nursery environments, particularly in the area of plant cultivation. Semantic segmentation results aid in obtaining tree components, like canopies and trunks, and detailed data on tree growth environments. However, obtaining precise semantic segmentation results from large-scale areas can be challenging due to the vast number of points involved. Therefore, this paper introduces an improved model aimed at achieving superior performance for large-scale points. The model incorporates direction angles between points to improve local feature extraction and ensure rotational invariance. It also uses geometric and relative distance information for better adjustment of different neighboring point features. An external attention module extracts global spatial features, and an upsampling feature adjustment strategy integrates features from the encoder and decoder. A specialized dataset was created from real nursery environments for experiments. Results show that the improved model surpasses several point-based models, achieving a Mean Intersection over Union (mIoU) of 87.18%. This enhances the precision of nursery environment analysis and supports the advancement of autonomous nursery managements.

A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution.

In the field of intelligent fault diagnosis, particularly concerning rotating machinery, convolutional neural networks (CNNs) face significant challenges when applied to real industrial vibration data. These data are not only contaminated by various types of noise but also exhibit fault features that vary across different scales. Consequently, the effective suppression of extraneous noise and accurate extraction of multi-scale fault features are crucial issues. To address these challenges, this study proposes a novel deep neural network framework, termed the Multidimensional Fusion Residual Attention Network (MFRANet), for gearbox fault diagnosis. The MFRANet employs a multi-scale deep separable convolution module to thoroughly investigate the fundamental characteristics of the original vibration signals in both the time and time-frequency domains. To enhance the detailed analysis of diagnostic data and mitigate the risks of overfitting and noise interference, an efficient residual channel attention module is incorporated to weight and denoise the feature maps. Additionally, an external attention module is introduced to create implicit connections between the denoised multi-scale feature maps and to highlight potential correlations within the sample data, thereby improving the accuracy of fault diagnosis. Experimental evaluations on a gearbox fault dataset demonstrate that the proposed method surpasses several benchmark and state-of-the-art techniques in terms of diagnostic performance, exhibiting robust noise resilience across various noise levels. This indicates enhanced reliability and accuracy in gearbox fault diagnosis, providing an innovative and efficient solution for fault diagnosis in rotating machinery. The study underscores the contributions of artificial intelligence through the innovative structure of the method and the integration of advanced deep learning modules, while its engineering application is evidenced by addressing practical challenges in rotating machinery fault diagnosis. This work meets the urgent need for reliable diagnostic methods in industrial environments.

The Effect of Attention Focus Instructions on Strength and Balance in Subjects With Generalized Joint Hypermobility

ABSTRACT This study aims to examine the effects of different attention focuses on muscle strength and balance performance in individuals with Generalized Joint Hypermobility (GJH). This randomized crossover trial included 32 individuals with GJH whose Beighton score was greater than 5. Subjects performed each task under external attentional focus, internal attentional focus, and neutral attentional focus condition. Knee extensor muscle strength was measured using the Isokinetic Dynamometer. Postural stability was evaluated using the Biodex Balance System, while dynamic balance was assessed using the Y Balance Test. The main effects of attentional focus on the outcomes were analyzed using repeated measures ANOVA and post-hoc corrections with a 95% confidence interval. Subjects produced significantly higher quadriceps peak torque during external focus instruction and internal focus instruction compared to neutral condition (p = .006). Postural stability performance were found to be better during external attention focus compared to the internal focus of attention and the neutral group (p = .008). In addition, an increase in Y balance composite score was observed during external condition compared to internal condition and neutral condition (p < .001). Whether internal or external, the use of attentional focus may be beneficial for optimal force production during training in individuals with GJH. External attention focus enabled better postural stability and dynamic balance performances.

A Multichannel Long-Term External Attention Network for Aeroengine Remaining Useful Life Prediction

A Multichannel Long-Term External Attention Network for Aeroengine Remaining Useful Life Prediction

Mental effort during mindless reading? Pupil fluctuations indicate internal processing during levels of inattention.

Mind wandering, an experience characterized by a reduced external focus of attention and an increased internal focus, has seen significant theoretical advancement in understanding its underlying cognitive processes. The levels-of-inattention hypothesis posits that in mind wandering, external attention is reduced in a graded fashion, reflecting different levels of weak versus deep attentional decoupling. However, it has remained unclear whether internal processing during mind wandering, and mindless reading in particular, requires effort and, if so, whether it is graded or distinct. To address this, we analyzed pupil size as a measure of cognitive load in the sustained-attention-to-stimulus task during text reading. We examined whether decoupled external attention is linked to an overall reduction in workload and whether internal focus of attention is graded or represents a distinct cognitive process. Overall, overlooking errors in the text was associated with a small pupil size, indicating reduced effortful processing. However, this effect varied with error type: overlooking high- or medium-level errors (weak decoupling) resulted in reduced pupil size, while overlooking low-level errors (deep decoupling) had no effect on pupil size. Moreover, detecting an error (at any processing level) elicited a task-evoked pupillary response, which was absent when it was overlooked. These findings suggest that weak decoupling reduces internal resource-demanding processing and are in line with the hypothesis that large pupils during deep decoupling may be associated with distinct states of effortful internal processing. They further support both the levels-of-inattention hypothesis and the notion that internal focus is a distinct mode of deeply decoupled processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Asym-UNet: An asymmetric U-shape Network for breast lesions ultrasound images segmentation

Precise segmentation of breast ultrasound images is essential for early breast cancer screening. However, the segmentation process is challenging due to the diverse morphology of tumors, blurred boundaries, and the similar grey intensity distribution between lesions and normal tissues. To overcome these obstacles, we propose the Asym-UNet, an asymmetric U-shaped network tailored for segmenting breast lesions with high accuracy and reliability. This network features a Multi-branch Residual Encoder (MRE), an External Attention Module (EAM), and a Boundary Detection Module (BDM). The MRE is designed to capture distinctive representations of breast lesions, facilitating accurate subsequent segmentation. The EAM directs the network to focus on the lesion areas while filtering out irrelevant information, thereby enhancing diagnostic precision. Furthermore, the BDM, incorporated to refine the boundaries of predicted masks, provides additional supervision and guidance through a multi-level prediction layer. We conducted evaluations of the Asym-UNet against thirteen other segmentation methods using two publicly available datasets. The results indicate that our network achieves state-of-the-art performance in breast tumor segmentation.

Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal–external attention switching

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

Buffer or substitute? Corporate financialization and leverage manipulation

This study examines the impact of financialization on leverage manipulation for nonfinancial companies in China from 2011 to 2021. Using the XLT-LEVM measurement method, we find that corporate financialization can inhibit leverage manipulation. Mechanism analysis suggests that corporate financialization deters leverage manipulation by crowding out debt financing in a substantive way. According to heterogeneity analysis, the inhibitory effect is less pronounced in firms with short-term solvency pressure and intensive external attention. Our findings extend the literature on corporate financialization and demonstrate its role in mitigating leverage manipulation, with implications for both financial practice and public policy.

When wavelet decomposition meets external attention: a lightweight cloud server load prediction model

Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud’s cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods.

Does artificial intelligence deter greenwashing?

Employing 6,940 observations of 1,205 Chinese-listed firms from 2012 to 2022, we provide robust evidence that Artificial Intelligence (hereafter AI) inhibits greenwashing. We further find that AI achieves this effect by mitigating agency problems, easing financing constraints, and increasing external attention. In addition, the positive impact of AI in curbing greenwashing is more notable in politically unaffiliated firms, those with fewer female directors, or those with weaker equity incentives. Our findings highlight AI's crucial role in combating greenwashing and maintaining capital market order.

Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

Effective identification and regulation of water quality impact factors is essential for water resource management and environmental protection. However, the complex coupling of water quality systems poses a significant challenge to this task. This study proposes coherent model for water quality prediction, classification and regulation based on interpretable machine learning. The decomposition-reconstruction module is used to transform non-stationary water quality series into stationary series while effectively reducing the feature dimensions. Spatiotemporal multi-source data is introduced by using the Maximum Information Coefficient (MIC) for feature selection. The Temporal Convolutional Network (TCN) is used to extract the temporal features of different variables, followed by the introduction of External Attention mechanism (EA) to construct the relationship between these features. Finally, the target water quality sequence is simulated using Gated Recurrent Unit (GRU). The proposed model was applied to Poyang Lake in China to predict six water quality indicators: ammonia nitrogen (NH3-N), dissolved oxygen (DO), pH, total nitrogen (TN), total phosphorus (TP), water temperature (WT). The water quality was then classified based on the prediction results using the XGBoost algorithm. The findings indicate that the proposed model's Nash-Sutcliff Efficiency (NSE) value ranges from 0.88 to 0.99, surpassing that of the benchmark model, and demonstrates strong interval prediction performance. The results highlight the superior performance of the XGBoost algorithm (with an accuracy of 0.89) in addressing water quality classification issues, particularly in cases of category imbalance. Subsequently, interpretability analysis using the SHapley Additive exPlanation (SHAP) method revealed that the model is capable of learning relationships between different variables and there exists a possibility of learning the physical laws. Ultimately, this study proposes a water quality regulation mechanism that improves TN and DO levels by stepwise changing the magnitude of water temperature, which significantly improves in the case of data limitations. In conclusion, this study presents an overall framework for integrating water quality prediction, classification and improvement for the first time, forming a complete set of water quality early warning and improvement management strategies. This framework provides new ideas and ways for lake water quality management.

Conscious but not thinking-Mind-blanks during visuomotor tracking: An fMRI study of endogenous attention lapses.

Attention lapses (ALs) are complete lapses of responsiveness in which performance is briefly but completely disrupted and during which, as opposed to microsleeps, the eyes remain open. Although the phenomenon of ALs has been investigated by behavioural and physiological means, the underlying cause of an AL has largely remained elusive. This study aimed to investigate the underlying physiological substrates of behaviourally identified endogenous ALs during a continuous visuomotor task, primarily to answer the question: Were the ALs during this task due to extreme mind-wandering or mind-blanks? The data from two studies were combined, resulting in data from 40 healthy non-sleep-deprived subjects (20M/20F; mean age 27.1 years, 20-45). Only 17 of the 40 subjects were used in the analysis due to a need for a minimum of two ALs per subject. Subjects performed a random 2-D continuous visuomotor tracking task for 50 and 20 min in Studies 1 and 2, respectively. Tracking performance, eye-video, and functional magnetic resonance imaging (fMRI) were recorded simultaneously. A human expert visually inspected the tracking performance and eye-video recordings to identify and categorise lapses of responsiveness as microsleeps or ALs. Changes in neural activity during 85 ALs (17 subjects) relative to responsive tracking were estimated by whole-brain voxel-wise fMRI and by haemodynamic response (HR) analysis in regions of interest (ROIs) from seven key networks to reveal the neural signature of ALs. Changes in functional connectivity (FC) within and between the key ROIs were also estimated. Networks explored were the default mode network, dorsal attention network, frontoparietal network, sensorimotor network, salience network, visual network, and working memory network. Voxel-wise analysis revealed a significant increase in blood-oxygen-level-dependent activity in the overlapping dorsal anterior cingulate cortex and supplementary motor area region but no significant decreases in activity; the increased activity is considered to represent a recovery-of-responsiveness process following an AL. This increased activity was also seen in the HR of the corresponding ROI. Importantly, HR analysis revealed no trend of increased activity in the posterior cingulate of the default mode network, which has been repeatedly demonstrated to be a strong biomarker of mind-wandering. FC analysis showed decoupling of external attention, which supports the involuntary nature of ALs, in addition to the neural recovery processes. Other findings were a decrease in HR in the frontoparietal network before the onset of ALs, and a decrease in FC between default mode network and working memory network. These findings converge to our conclusion that the ALs observed during our task were involuntary mind-blanks. This is further supported behaviourally by the short duration of the ALs (mean 1.7 s), which is considered too brief to be instances of extreme mind-wandering. This is the first study to demonstrate that at least the majority of complete losses of responsiveness on a continuous visuomotor task are, if not due to microsleeps, due to involuntary mind-blanks.

Impact of environmental, social, and governance rating disagreement on real earnings management in Chinese listed companies

This study examines the impact of environmental, social, and governance (ESG) rating disagreement on real earnings management in Chinese companies. Using ESG ratings from Huazheng, Wind, SynTao Green Finance, and Bloomberg, we find that increased disagreements in ESG rating lead to higher real earnings management in the current period and over the next 1 to 2 years. This effect is driven by external attention, compensation incentives, and reputational pressure. Furthermore, companies with a financial management background and younger management show a stronger correlation. Specifically, ESG rating disagreement has a significant impact on the manipulation of discretionary expenses and production costs but not on operating cash flows. Additionally, high ESG-rated companies tend to manage their earnings through discretionary expenses when rating disagreements arise. Overall, this study reveals the potential incentive mechanisms for such companies, providing theoretical support for understanding the factors influencing real earnings management. It also suggests that regulators and investors should fully consider the impact of ESG rating disagreement when assessing company performance.

Online network traffic classification based on external attention and convolution by IP packet header

Network traffic classification is an important part of network monitoring and network management. Three traditional methods for network traffic classification are flow-based, session-based, and packet-based, while flow-based and session-based methods cannot meet the real-time requirements and existing packet-based methods will violate user’s privacy. To solve the above problems, we propose a network traffic classification method only by the IP packet header, which satisfies the requirements of both the user’s privacy protection and online classification performances. Through statistical analyses, we find that IP packet header information is effective on the network traffic classification tasks and this conclusion is also demonstrated by experiments. Furthermore, we propose a novel external attention and convolution mixed (ECM) model for online network traffic classification. This model adopts both low-computational complexity external attention and convolution to respectively extract the byte-level and packet-level characteristics for traffic classification. Therefore, it can achieve high classification accuracy and low time consumption. The experiments show that ECM can reach over 96% classification accuracy on four datasets and the classification time is 0.36 ms per packet which can meet the real-time requirements. The code is available at https://github.com/CNZZQ1030/ECM-for-Network-Traffic-Classification.

Transcranial direct current stimulation over medial prefrontal cortex reduced alpha power and functional connectivity during somatic but not semantic self-referential processing

Past self-report and cognitive-behavioural studies of the effects of transcranial direct current stimulation (tDCS) targeting the medial prefrontal cortex (mPFC) on semantic self-referential processing (SRP) have yielded mixed results. Meanwhile, electroencephalography (EEG) studies show that alpha oscillation (8–12 Hz) may be involved during both semantic and somatic SRP, although the effect of tDCS on alpha-EEG during SRP remains unknown. The current study assessed the EEG and subjective effects of 2 mA tDCS over the mPFC while participants were SRP either on semantic (life roles, e.g., “friend”) or somatic (outer body, e.g., “arms”) self-referential stimuli compared to resting state and an external attention memory task in 52 young adults. Results showed that whereas mPFC-tDCS did not yield significant changes in participants’ mood or experienced attention or pleasantness levels during the SRP task, EEG source analysis indicated, compared to sham stimulation, that tDCS reduced alpha power during somatic but not semantic SRP in the posterior cingulate cortex (PCC), and the frontal, parietal, temporal, and somatosensory cortex, and reduced the functional connectivity between the left inferior parietal lobule and the ventral PCC, but only when mPFC-tDCS was applied at the second while not the first experimental session. Our results suggest that while mPFC-tDCS may be insufficient to alter immediate subjective experience during SRP, mPFC-tDCS may modulate the power and functional connectivity of the brain’s alpha oscillations during somatic SRP. Future research directions are discussed.

Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism

Background:Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of cardiovascular diseases. In order to achieve cardiologist-level performance, deep neural networks have been widely utilized to extract abstract feature representations. However, the mechanical stacking of numerous computationally intensive operations makes traditional deep neural networks suffer from inadequate learning, poor interpretability, and high complexity. Method:To address these limitations, a clinical knowledge-based ECG abnormalities detection model using dual-view CNN-Transformer and external attention mechanism is proposed by mimicking the diagnosis of the clinicians. Considering the clinical knowledge that both the detailed waveform changes within a single heartbeat and the global changes throughout the entire recording have complementary roles in abnormalities detection, we presented a dual-view CNN-Transformer to extract and fuse spatial–temporal features from different views. In addition, the locations of the ECG where abnormalities occur provide more information than other areas. Therefore, two external attention mechanisms are designed and added to the corresponding views to help the network learn efficiently. Results:Experiment results on the 9-class dataset show that the proposed model achieves an average F1-score of 0.854±0.01 with a higher interpretability and a lower complexity, outperforming the state-of-the-art model. Conclusions:Combining all these excellent features, this study provides a credible solution for automatic ECG abnormalities detection.