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Deformable image registration (DIR) plays an important part in many clinical tasks, and deep learning has made significant progress in DIR over the past few years.To propose a fast multiscale unsupervised deformable image registration (referred to as FMIRNet) method for monomodal image registration.We designed a multiscale fusion module to estimate the large displacement field by combining and refining the deformation fields of three scales. The spatial attention mechanism was employed in our fusion module to weight the displacement field pixel by pixel. Except mean square error (MSE), we additionally added structural similarity (ssim) measure during the training phase to enhance the structural consistency between the deformed images and the fixed images.Our registration method was evaluated on EchoNet, CHAOS and SLIVER, and had indeed performance improvement in terms of SSIM, NCC and NMI scores. Furthermore, we integrated the FMIRNet into the segmentation network (FCN, UNet) to boost the segmentation task on a dataset with few manual annotations in our joint leaning frameworks. The experimental results indicated that the joint segmentation methods had performance improvement in terms of Dice, HD and ASSD scores.Our proposed FMIRNet is effective for large deformation estimation, and its registration capability is generalizable and robust in joint registration and segmentation frameworks to generate reliable labels for training segmentation tasks. [ABSTRACT FROM AUTHOR]

The flanker task (Eriksen & Eriksen, Perception & Psychophysics, 16(1), 143-149, 1974) has been highly influential and widely used in studies of visual attention. Its simplicity has made it popular to include it in experimental software packages and online platforms. The spacing flanker task (SFT), in which the distance between the target and flankers varies, is useful for studying the distribution of attention across space as well as inhibitory control. Use of the SFT requires that the viewing environment (e.g., stimulus size and viewing distance) be controlled, which is a challenge for online delivery. We implement and evaluate an online version of the SFT that includes two calibration pretests to provide the necessary control. Test–retest and split-half reliability of the online version was compared with a laboratory version on measures of inhibitory control and measures of the distribution of attention across space. Analyses show that the online SFT is comparable to laboratory testing on all measures. Results also identify two measures with good test–retest reliability that hold promise for studying performance in the SFT: the mean flanker effect (ICC = 0.745) and RTs on incongruent trials across distances (ICC = 0.65–0.71). [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The You Only Look Once (YOLO) series of object detection models is widely recognized for its efficiency and real-time performance, particularly under the challenging conditions of underwater environments, characterized by insufficient lighting and visual disturbances. By modifying the YOLOv9s model, this study aims to improve the accuracy and real-time capabilities of underwater object detection, resulting in the introduction of the YOLOv9s-UI detection model. The proposed model incorporates the Dual Dynamic Token Mixer (D-Mixer) module from TransXNet to improve feature extraction capabilities. Additionally, it integrates a feature fusion network design from the LocalMamba network, employing channel and spatial attention mechanisms. These attention modules effectively guide the feature fusion process, significantly enhancing detection accuracy while maintaining the model's compact size of only 9.3 M. Experimental evaluation on the UCPR2019 underwater object dataset shows that the YOLOv9s-UI model has higher accuracy and recall than the existing YOLOv9s model, as well as excellent real-time performance. This model significantly improves the ability of underwater target detection by introducing advanced feature extraction and attention mechanisms. The model meets portability requirements and provides a more efficient solution for underwater detection. [ABSTRACT FROM AUTHOR]

Tea disease detection is crucial for improving the agricultural circular economy. Deep learning-based methods have been widely applied to this task, and the main idea of these methods is to extract multiscale coarse features of diseases using the backbone network and fuse these features through the neck for accurate disease detection. This paper proposes a novel tea disease detection method that enhances feature expression of the backbone network and the feature fusion capability of the neck: (1) constructing an inverted residual self-attention module as a backbone plugin to capture the long-distance dependencies of disease spots on the leaves; and (2) developing a channel–spatial attention module with residual connection in the neck network to enhance the contextual semantic information of fused features in disease images and eliminate complex background noise. For the second step, the proposed channel–spatial attention module uses Residual Channel Attention (RCA) to enhance inter-channel interactions, facilitating discrimination between disease spots and normal leaf regions, and employs spatial attention (SA) to enhance essential areas of tea diseases. Experimental results demonstrate that the proposed method achieved accuracy and mAP scores of 92.9% and 94.6%, respectively. In particular, this method demonstrated improvements of 6.4% in accuracy and 6.2% in mAP compared to the SSD model. [ABSTRACT FROM AUTHOR]

Convolutional neural networks have made significant progress in human Facial Expression Recognition (FER). However, they still face challenges in effectively focusing on and extracting facial features. Recent research has turned to attention mechanisms to address this issue, focusing primarily on local feature details rather than overall facial features. Building upon the classical Convolutional Block Attention Module (CBAM), this paper introduces a novel Parallel Hybrid Attention Model, termed PH-CBAM. This model employs split-channel attention to enhance the extraction of key features while maintaining a minimal parameter count. The proposed model enables the network to emphasize relevant details during expression classification. Heatmap analysis demonstrates that PH-CBAM effectively highlights key facial information. By employing a multimodal extraction approach in the initial image feature extraction phase, the network structure captures various facial features. The algorithm integrates a residual network and the MISH activation function to create a multi-feature extraction network, addressing issues such as gradient vanishing and negative gradient zero point in residual transmission. This enhances the retention of valuable information and facilitates information flow between key image details and target images. Evaluation on benchmark datasets FER2013, CK+, and Bigfer2013 yielded accuracies of 68.82%, 97.13%, and 72.31%, respectively. Comparison with mainstream network models on FER2013 and CK+ datasets demonstrates the efficiency of the PH-CBAM model, with comparable accuracy to current advanced models, showcasing its effectiveness in emotion detection. [ABSTRACT FROM AUTHOR]

Hyperspectral images can be widely used in many fields due to their high information richness. However, costly and complex imaging spectrometers limit its growth. Hyperspectral reconstruction aims to obtain the corresponding hyperspectral image from the multispectral image, to reduce the acquisition cost of hyperspectral images. At present, the related works mainly use methods based on deep learning, and they have achieved good results. However, how to fully extract the global spectral and spatial features of hyperspectral images is still not well solved. To address these issues, we propose an Attention and Transformer Complementary Fusion Network (ATCFNet), which is composed of three modules: Multi-angle Input Image Processing (MIIP), Deep Feature Extraction (DFE) and Hyperspectral Reconstruction (HR) modules. Within the DFE module, an improved Transformer module and a novel Multi-scale Spatial Attention (MSA) module are proposed to extract the global spectral relationship and the spatial features of the hyperspectral images, respectively. Moreover, the MIIP module is proposed to extract the features of input multispectral images more effectively and comprehensively. To verify these, we compare the proposed ATCFNet with other excellent reconstruction methods on three hyperspectral image datasets. The results show that our method achieves the best results in these datasets. Code is publicly available at . [ABSTRACT FROM AUTHOR]

This article investigates the decisional and attentional drivers of the attentional repulsion effect (ARE) using the diffusion decision model (DDM). The ARE is a phenomenon in which a subjective expansion of space is experienced outside the focus of attention. It is thought to occur due to changes in the functioning of visual cell receptive fields. The DDM is a model of the decision-making process that assumes responses are selected by sequentially sampling an encoded representation of a stimulus until sufficient evidence has been accumulated favoring one response alternative over the other. The model decomposes observed choice and response times into different latent variables corresponding to the rate of evidence accumulation, response caution, response bias, and the time course of stimulus encoding and response execution. In this article, we interpret changes in the rate of evidence accumulation as primarily reflecting perceptual-driven changes in stimulus representation. We interpret changes in response bias as primarily reflecting decision-level changes. We utilize the DDM's ability to estimate these variables independently to explore how they are each affected by cueing manipulations to clarify whether the ARE emerges due to attentional or decisional drivers, or some combination of the two. The results of this study could shed light on the mechanisms underlying the ARE, and has implications in our understanding of spatial attention. [ABSTRACT FROM AUTHOR]

Challenges exist in the power generation efficiency and safety of marine current turbines (MCTs), as the MCT blades are often attached by foreign objects when operating underwater. It is essential for the stable operation of an MCT to recognize attachments timely and accurately. However, underwater imaging suffers from blurry edges due to light attenuation and scattering. It is challenging for accurate recognition through underwater images since blurry edges result in unclear edge features. To alleviate this problem, LAW-IFF Net is proposed in this paper, which mainly contains two parts. Firstly, this paper proposes to transform the local averages of feature maps into weight matrices, namely the locally average weighting (LAW) mechanism. It is intended to alleviate the edge gradient reduction caused by blurry edges. Secondly, the proposed improved feature fusion (IFF) mechanism aims to overcome the deviation caused by the feature fusion of different attention branches based on spatial attention. At the same time, the lightweight networks are combined with the proposed method to improve the computation speed and ensure the timeliness of recognition. Experimental results on the MCT dataset show the superiority of the proposed method in terms of accuracy and speed of attachment recognition in images with blurry edges. The experimental results on publicly available datasets show the applicability of the proposed method to other underwater images. [ABSTRACT FROM AUTHOR]

Forecasting energy demand is critical to ensure the steady operation of the power system. However, present approaches to estimating power load are still unsatisfactory in terms of accuracy, precision, and efficiency. In this paper, we propose a novel method, named ELFNet, for estimating short-term electricity consumption, based on the deep convolutional neural network model with a double-attention mechanism. The Gramian Angular Field method is utilized to convert electrical load time series into 2D image data for input into the proposed model. The prediction accuracy is greatly improved through the use of a convolutional neural network to extract the intrinsic characteristics from the input data, along with channel attention and spatial attention modules, to enhance the crucial features and suppress the irrelevant ones. The present ELFNet method is compared to several classic deep learning networks across different prediction horizons using publicly available data on real power demands from the Belgian grid firm Elia. The results show that the suggested approach is competitive and effective for short-term power load forecasting. [ABSTRACT FROM AUTHOR]

Fourier Ptychographic Microscopy (FPM) is a microscopy imaging technique based on optical principles. It employs Fourier optics to separate and combine different optical information from a sample. However, noise introduced during the imaging process often results in poor resolution of the reconstructed image. This article has designed an approach based on a residual local mixture network to improve the quality of Fourier ptychographic reconstruction images. By incorporating channel attention and spatial attention into the FPM reconstruction process, the network enhances the efficiency of the network reconstruction and reduces the reconstruction time. Additionally, the introduction of the Gaussian diffusion model further reduces coherent artifacts and improves image reconstruction quality. Comparative experimental results indicate that this network achieves better reconstruction quality, and outperforming existing methods in both subjective observation and objective quantitative evaluation. [ABSTRACT FROM AUTHOR]

The vigilance decrement, a temporal decline in detection performance, has been observed across multiple sensory modalities. Spatial uncertainty about the location of task-relevant stimuli has been demonstrated to increase the demands of vigilance and increase the severity of the vigilance decrement when attending to visual displays. The current study investigated whether spatial uncertainty also increases the severity of the vigilance decrement and task demands when an auditory display is used. Individuals monitored an auditory display to detect critical signals that were shorter in duration than non-target stimuli. These auditory stimuli were presented in either a consistent, predictable pattern that alternated sound presentation from left to right (spatial certainty) or an inconsistent, unpredictable pattern that randomly presented sounds from the left or right (spatial uncertainty). Cerebral blood flow velocity (CBFV) was measured to assess the neurophysiological demands of the task. A decline in performance and CBFV was observed in both the spatially certain and spatially uncertain conditions, suggesting that spatial auditory vigilance tasks are demanding and can result in a vigilance decrement. Spatial uncertainty resulted in a more severe vigilance decrement in correct detections compared to spatial certainty. Reduced right-hemispheric CBFV was also observed during spatial uncertainty compared to spatial certainty. Together, these results suggest that auditory spatial uncertainty hindered performance and required greater attentional demands compared to spatial certainty. These results concur with previous research showing the negative impact of spatial uncertainty in visual vigilance tasks, but the current results contrast recent research showing no effect of spatial uncertainty on tactile vigilance. [ABSTRACT FROM AUTHOR]

The presence of side-lobe noise degrades the image quality and adversely affects the performance of inverse synthetic aperture radar (ISAR) image understanding applications, such as automatic target recognition (ATR), target detection, etc. However, methods reliant on data processing, such as windowing, inevitably encounter resolution reduction, and current deep learning approaches under-appreciate the sparsity inherent in ISAR images. Taking the above analysis into consideration, a convolutional neural network-based process for ISAR side-lobe noise training is proposed in this paper. The proposed processing, based on the ISAR images sparsity characteristic analysis, undergoes enhancements in three core ideas, dataset construction, prior network design, and loss function improvements. In the realm of dataset construction, we introduce a bin-by-bin fast clean algorithm and accelerate the processing speed significantly on the basis of image complete information. Subsequently, a spatial attention layer is incorporated into the prior network designed to augment the network's focus on the crucial regions of ISAR images. In addition, a loss function featuring a weighting factor is devised to ensure the precise recovery of the strong scattering point. Simulation experiments demonstrate that the proposed process achieves significant improvements in both quantitative and qualitative results over the classical denoise methods. [ABSTRACT FROM AUTHOR]

The zebrafish visual system has become a paradigmatic preparation for behavioral and systems neuroscience. Around 40 types of retinal ganglion cells (RGCs) serve as matched filters for stimulus features, including light, optic flow, prey, and objects on a collision course. RGCs distribute their signals via axon collaterals to 12 retinorecipient areas in forebrain and midbrain. The major visuomotor hub, the optic tectum, harbors nine RGC input layers that combine information on multiple features. The retinotopic map in the tectum is locally adapted to visual scene statistics and visual subfield–specific behavioral demands. Tectal projections to premotor centers are topographically organized according to behavioral commands. The known connectivity in more than 20 processing streams allows us to dissect the cellular basis of elementary perceptual and cognitive functions. Visually evoked responses, such as prey capture or loom avoidance, are controlled by dedicated multistation pathways that—at least in the larva—resemble labeled lines. This architecture serves the neuronal code's purpose of driving adaptive behavior. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) with gaming abilities have been widely applied in image generation. However, gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes. Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation. In this paper, we propose an enhanced GAN via improving a generator for image generation (EIGGAN). EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness of the generated images. Taking into relation the context account, parallel residual operations are fused into a generation network to extract more structural information from the different layers. Finally, a mixed loss function in a GAN is exploited to make a tradeoff between speed and accuracy to generate more realistic images. Experimental results show that the proposed method is superior to popular methods, i.e. Wasserstein GAN with gradient penalty (WGAN-GP) in terms of many indexes, i.e. Frechet Inception Distance, Learned Perceptual Image Patch Similarity, Multi-Scale Structural Similarity Index Measure, Kernel Inception Distance, Number of Statistically-Different Bins, Inception Score and some visual images for image generation. [ABSTRACT FROM AUTHOR]

Abstract Attention is often viewed as a mental spotlight, which can be scaled like a zoom lens at specific spatial locations and features a center-surround gradient. Here, we demonstrate a neural signature of attention spotlight in signal transmission along the visual hierarchy. fMRI background connectivity analysis was performed between retinotopic V1 and downstream areas to characterize the spatial distribution of inter-areal interaction under two attentional states. We found that, compared to diffused attention, focal attention sharpened the spatial gradient in the strength of the background connectivity. Dynamic causal modeling analysis further revealed the effect of attention in both the feedback and feedforward connectivity between V1 and extrastriate cortex. In a context which induced a strong effect of crowding, the effect of attention in the background connectivity profile diminished. Our findings reveal a context-dependent attention prioritization in information transmission via modulating the recurrent processing across the early stages in human visual cortex.

Abstract When lying inside a MRI scanner and even in the absence of any motion, the static magnetic field of MRI scanners induces a magneto-hydrodynamic stimulation of subjects’ vestibular organ (MVS). MVS thereby not only causes a horizontal vestibular nystagmus but also induces a horizontal bias in spatial attention. In this study, we aimed to determine the time course of MVS-induced biases in both VOR and spatial attention inside a 3 T MRI-scanner as well as their respective aftereffects after participants left the scanner. Eye movements and overt spatial attention in a visual search task were assessed in healthy volunteers before, during, and after a one-hour MVS period. All participants exhibited a VOR inside the scanner, which declined over time but never vanished completely. Importantly, there was also an MVS-induced horizontal bias in spatial attention and exploration, which persisted throughout the entire hour within the scanner. Upon exiting the scanner, we observed aftereffects in the opposite direction manifested in both the VOR and in spatial attention, which were statistically no longer detectable after 7 min. Sustained MVS effects on spatial attention have important implications for the design and interpretation of fMRI-studies and for the development of therapeutic interventions counteracting spatial neglect.

Abstract Early detection of Alzheimer's disease (AD) is critical due to its rising prevalence. AI-aided AD diagnosis has grown for decades. Most of these systems use deep learning using CNN. However, a few concerns must be addressed to identify AD: a. there is a lack of attention paid to spatial features; b. there is a lack of scale-invariant feature modelling; and c. the convolutional spatial attention block (C-SAB) mechanism is available in the literature, but it exploits limited feature sets from its input features to obtain a spatial attention map, which needs to be enhanced. The suggested model addresses these issues in two ways: through a backbone of multilayers of depth-separable CNN. Firstly, we propose an improved spatial convolution attention block (I-SAB) to generate an enhanced spatial attention map for the multilayer features of the backbone. The I-SAB, a modified version of the C-SAB, generates a spatial attention map by combining multiple cues from input feature maps. Such a map is forwarded to a multilayer of depth-separable CNN for further feature extraction and employs a skip connection to produce an enhanced spatial attention map. Second, we combine multilayer spatial attention features to make scale-invariant spatial attention features that can fix scale issues in MRI images. We demonstrate extensive experimentation and ablation studies using two open-source datasets, OASIS and AD-Dataset. The recommended model outperforms existing best practices with 99.75% and 96.20% accuracy on OASIS and AD-Dataset. This paper also performed a domain adaptation test on the OASIS dataset, which obtained 83.25% accuracy.

Aiming at the problem of poor reconstruction due to the blurring of feature targets in remote sensing images and the influence of background noise, in this paper, a super-resolution reconstruction algorithm for remote sensing images incorporating two-branch semantic enhanced perception is proposed. Firstly, a global-local spatial attention module is designed to enhance the semantic representation of features at different scales of spatial global-local, and at the same time strengthen the discriminative ability of the network for effective feature groups. Secondly, a channel grouping-aggregation attention module is proposed to enhance the model’s discriminative ability for ground objects features by designing feature grouping-aggregation and channel attention modules, and strengthen the model’s ability to focus on effective feature channels. Experiments show that on the UC Merced dataset, the PSNR reaches 34.397 dB, 29.920 dB and 28.128 dB respectively at the ×2/×3/×4 multiplier, and the structural similarity reaches 0.931, 0.834 and 0.791 at the ×2/×3/×4 multiplier. On the AID dataset, the PSNR reaches 32.524 dB, 29.317 dB and 27.522 dB respectively at the×2/×3/×4 multiplier, and the structural similarity reaches 0.895, 0.829 and 0.721 at the ×2/×3/×4 multiplier. Compared with other mainstream algorithms, both indices are improved, and the edge and regional details of reconstructed images are better, effectively overcoming the problems of fuzzy feature information of ground objects and background noise, which lead to poor reconstruction effect of remote sensing images.

Monika Halicka,1– 3 Olivia Rose Cousins,3 Antonia F Ten Brink,2– 4 Axel D Vittersø,2,3,5 Michael J Proulx,3 Janet H Bultitude2,3 1Institute of Neuroscience, Universite catholique de Louvain, Brussels, Belgium; 2Centre for Pain Research, University of Bath, Bath, UK; 3Department of Psychology, University of Bath, Bath, UK; 4Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands; 5Division of Mental Health Services, Akershus University Hospital, Lørenskog, NorwayCorrespondence: Monika Halicka, Institute of Neuroscience (IoNS), Université catholique de Louvain, Avenue Mounier 53, B1.53.04, 1200 Woluwe-Saint-Lambert, Brussels, Belgium, Email mon.halicka@gmail.comPurpose: Alterations in spatial attention have been reported in people with chronic pain and may be relevant to understanding its cortical mechanisms and developing novel treatments. There is conflicting evidence as to whether people with Complex Regional Pain Syndrome (CRPS) have reduced visuospatial attention to their affected limb and/or its surrounding space, with some evidence that these deficits may be greater in personal (bodily) space. We aimed to test the competing hypotheses of whether the visuospatial attentional bias is specific to the personal space of the affected limb or generalizes to the personal space of other parts of the affected side of the body.Patients and Methods: Using visual Temporal Order Judgement tasks, we measured spatial attention in the personal space of the hands and feet of patients with upper (n=14) or lower (n=14) limb CRPS and pain-free controls (n=17). Participants judged the order of two light flashes presented at different temporal offsets on each of their hands or feet. Slower processing of the flash on one side relative to the other reflects reduced attention to that side of space.Results: Controls prioritized stimuli on the non-dominant (left) relative to dominant side, consistent with the well-documented normal leftward bias of attention (ie “pseudoneglect”). Regardless of the location (upper or lower limb) of the pain or visual stimuli, people with CRPS showed no such asymmetry, representing reduced attention to the affected side (compared to the greater attention of controls to their non-dominant side). More severe CRPS symptoms were associated with a greater tendency to deprioritize stimuli on the affected side.Conclusion: Our findings suggest that relative visuospatial bias in CRPS is generalized to the personal space of the affected side of the body, rather than being specific to the personal space of the CRPS-affected limb.Keywords: complex regional pain syndrome, spatial attention, personal space, pseudoneglect, temporal order judgement, chronic pain

Abstract As one of the three major outdoor components of the railroad signal system, the track circuit plays an important role in ensuring the safety and efficiency of train operation. Therefore, when a fault occurs, the cause of the fault needs to be found quickly and accurately and dealt with in a timely manner to avoid affecting the efficiency of train operation and the occurrence of safety accidents. This article proposes a fault diagnosis method based on multi-scale attention network, which uses Gramian Angular Field (GAF) to transform one-dimensional time series into two-dimensional images, making full use of the advantages of convolutional networks in processing image data. A new feature fusion training structure is designed to effectively train the model, fully extract features at different scales, and fusing spatial feature information through spatial attention mechanisms. Finally, experiments are conducted using real track circuit fault datasets, and the accuracy of fault diagnosis reaches 99.36%, and our model demonstrates better performance compared to classical and state-of-the-art models. And the ablation experiments verified that each module in the designed model plays a key role.

Reading direction affects cognitive processes such as spatial processing and attention. Testing Farsi right-to-left readers, we investigated the effect of reading direction on the involved spatial and attentional processes in a verbal working memory task. Previous research in left-to-right readers has shown that serial order in verbal WM is encoded spatially from left to right and that mechanisms of spatial attention operate on these mental representations. First, we confirmed that the spatial representation of serial information in WM follows the right-to-left reading direction of Farsi. Second, we demonstrated the influence of reading direction on the distribution of spatial attention over the reading-direction-contingent mental representation of serial information in WM. Behavioural RTs and neural markers of spatial attention shifts (EDAN and ADAN ERPs and lateralised posterior alpha activity) confirm the right-to-left representation of serial order in WM and the involvement of spatial attention in retrieving items from this mnemonic space. [ABSTRACT FROM AUTHOR]

Wild desert grasslands are characterized by diverse habitats, uneven plant distribution, similarities among plant class, and the presence of plant shadows. However, the existing models for detecting plant species in desert grasslands exhibit low precision, require a large number of parameters, and incur high computational cost, rendering them unsuitable for deployment in plant recognition scenarios within these environments. To address these challenges, this paper proposes a lightweight and fast plant species detection system, termed YOLOv8s-KDT, tailored for complex desert grassland environments. Firstly, the model introduces a dynamic convolutional KernelWarehouse method to reduce the dimensionality of convolutional kernels and increase their number, thus achieving a better balance between parameter efficiency and representation ability. Secondly, the model incorporates triplet attention into its feature extraction network, effectively capturing the relationship between channel and spatial position and enhancing the model's feature extraction capabilities. Finally, the introduction of a dynamic detection head tackles the issue related to target detection head and attention non-uniformity, thus improving the representation of the target detection head while reducing computational cost. The experimental results demonstrate that the upgraded YOLOv8s-KDT model can rapidly and effectively identify desert grassland plants. Compared to the original model, FLOPs decreased by 50.8%, accuracy improved by 4.5%, and mAP increased by 5.6%. Currently, the YOLOv8s-KDT model is deployed in the mobile plant identification APP of Ningxia desert grassland and the fixed-point ecological information observation platform. It facilitates the investigation of desert grassland vegetation distribution across the entire Ningxia region as well as long-term observation and tracking of plant ecological information in specific areas, such as Dashuikeng, Huangji Field, and Hongsibu in Ningxia. [ABSTRACT FROM AUTHOR]

Attention is often viewed as a mental spotlight, which can be scaled like a zoom lens at specific spatial locations and features a center-surround gradient. Here, we demonstrate a neural signature of attention spotlight in signal transmission along the visual hierarchy. fMRI background connectivity analysis was performed between retinotopic V1 and downstream areas to characterize the spatial distribution of inter-areal interaction under two attentional states. We found that, compared to diffused attention, focal attention sharpened the spatial gradient in the strength of the background connectivity. Dynamic causal modeling analysis further revealed the effect of attention in both the feedback and feedforward connectivity between V1 and extrastriate cortex. In a context which induced a strong effect of crowding, the effect of attention in the background connectivity profile diminished. Our findings reveal a context-dependent attention prioritization in information transmission via modulating the recurrent processing across the early stages in human visual cortex. [ABSTRACT FROM AUTHOR]

When lying inside a MRI scanner and even in the absence of any motion, the static magnetic field of MRI scanners induces a magneto-hydrodynamic stimulation of subjects' vestibular organ (MVS). MVS thereby not only causes a horizontal vestibular nystagmus but also induces a horizontal bias in spatial attention. In this study, we aimed to determine the time course of MVS-induced biases in both VOR and spatial attention inside a 3 T MRI-scanner as well as their respective aftereffects after participants left the scanner. Eye movements and overt spatial attention in a visual search task were assessed in healthy volunteers before, during, and after a one-hour MVS period. All participants exhibited a VOR inside the scanner, which declined over time but never vanished completely. Importantly, there was also an MVS-induced horizontal bias in spatial attention and exploration, which persisted throughout the entire hour within the scanner. Upon exiting the scanner, we observed aftereffects in the opposite direction manifested in both the VOR and in spatial attention, which were statistically no longer detectable after 7 min. Sustained MVS effects on spatial attention have important implications for the design and interpretation of fMRI-studies and for the development of therapeutic interventions counteracting spatial neglect. [ABSTRACT FROM AUTHOR]

Violence detection is a difficult task because it involves analyzing video clips from multiple security cameras, which are located in various places and operate continuously. When violent crimes occur, a system should be able to reliably detect them in real-time and immediately alert a surveillance team. Currently, researchers employ deep learning models to detect violent behavior. Notably, a large number of deep learning approaches are based on extracting spatio-temporal information from a video by exploiting either 3D Convolutional Neural Networks (CNNs) or multi-stream networks. Despite their success, these techniques require a lot of parameters than 2D CNNs and have high computational complexity. Therefore, we present a simple spatio-temporal attention mechanism combined with a 2D CNN for an effective violence detection system. We propose a Squeeze Temporal Attention block that allows a 2D CNN to learn spatiotemporal features in videos. This effective block uses squeeze and temporal attention modules to summarize a video stream into three channels. In addition, we introduce spatial attention and feature fusion modules to improve the performance of the proposed system. The spatial attention module, Entropy Spatial Module, utilizes an entropy filter and frame differences to focus on spatial regions of the video with more movement. The fusion module parallelizes two dense layers with a 2D CNN to effectively enhance the classifier's performance. As a result, our proposed model achieves improved performance results in terms of accuracy when compared to Long Short-Term Memory, multi-stream networks, and current 3D CNNs. [ABSTRACT FROM AUTHOR]

The ability to adapt to the environment is linked to the possibility of inhibiting inappropriate behaviours, and this ability can be enhanced by attention. Despite this premise, the scientific literature that assesses how attention can influence inhibition is still limited. This study contributes to this topic by evaluating whether spatial and moving attentional cueing can influence inhibitory control. We employed a task in which subjects viewed a vertical bar on the screen that, from a central position, moved either left or right where two circles were positioned. Subjects were asked to respond by pressing a key when the motion of the bar was interrupted close to the circle (go signal). In about 40% of the trials, following the go signal and after a variable delay, a visual target appeared in either one of the circles, requiring response inhibition (stop signal). In most of the trials the stop signal appeared on the same side as the go signal (valid condition), while in the others, it appeared on the opposite side (invalid condition). We found that spatial and moving cueing facilitates inhibitory control in the valid condition. This facilitation was observed especially for stop signals that appeared within 250ms of the presentation of the go signal, thus suggesting an involvement of exogenous attentional orienting. This work demonstrates that spatial and moving cueing can influence inhibitory control, providing a contribution to the investigation of the relationship between spatial attention and inhibitory control. [ABSTRACT FROM AUTHOR]

Healthy individuals typically show more attention to the left than to the right (known as pseudoneglect), and to the upper than to the lower visual field (known as altitudinal pseudoneglect). These biases are thought to reflect asymmetries in neural processes. Attention biases have been used to investigate how these neural asymmetries change with age. However, inconsistent results have been reported regarding the presence and direction of age-related effects on horizontal and vertical attention biases. The observed inconsistencies may be due to insensitive measures and small sample sizes, that usually only feature extreme age groups. We investigated whether spatial attention biases, as indexed by gaze position during free viewing of a single image, are influenced by age. We analysed free-viewing data from 4,243 participants aged 5–65 years and found that attention biases shifted to the right and superior directions with increasing age. These findings are consistent with the idea of developing cerebral asymmetries with age and support the hypothesis of the origin of the leftward bias. Age modulations were found only for the first seven fixations, corresponding to the time window in which an absolute leftward bias in free viewing was previously observed. We interpret this as evidence that the horizontal and vertical attention biases are primarily present when orienting attention to a novel stimulus – and that age modulations of attention orienting are not global modulations of spatial attention. Taken together, our results suggest that attention orienting may be modulated by age and that cortical asymmetries may change with age. [ABSTRACT FROM AUTHOR]

Existing deep learning-based change detection networks encounter challenges related to the temporal dependency inherent in dual-temporal images. In this study, a weight-shared dual-difference change detection network (DDCDNet) model is proposed based on feature extraction networks. The model employs feature discrimination modules fused with spatial and channel attention mechanisms at different hierarchical levels of the backbone network. In the encoding phase, the tiny version of the Swin Transformer is utilized as the backbone network, with a weightsharing strategy applied to extract feature information from bitemporal remote sensing images. The proposed model in this paper is experimented on the LEVIR-CDþand DSIFN datasets, achieving F1-scores (F1) of 87.71% and 85.79%, recalls of 83.87% and 81.17%, and IoU (Intersection over Union) scores of 78.11% and 75.12%, respectively. These results indicate that the proposed model significantly outperforms other comparative models, demonstrating a better capability of identifying temporal changes in buildings, excellent generalization capability. [ABSTRACT FROM AUTHOR]

Shield tunnel lining is prone to water leakage, which may further bring about corrosion and structural damage to the walls, potentially leading to dangerous accidents. To avoid tedious and inefficient manual inspection, many projects use artificial intelligence (AI) to detect cracks and water leakage. A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this paper. Our proposal includes a ConvNeXt-S backbone, deconvolutional-feature pyramid network (D-FPN), spatial attention module (SPAM). and a detection head. It can extract representative features of leaking areas to aid inspection processes. To further improve the model's robustness, we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training samples. Validation experiments are performed, achieving the average precision (AP) score of 56.8%, which outperforms previous work by a margin of 5.7%. Visualization illustrations also support our method's practical effectiveness. [ABSTRACT FROM AUTHOR]

The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. Such capabilities hold significant potential for advancing rehabilitation and the development of assistive technologies. In recent years, deep learning has received considerable attention in the MI-BCI field due to its powerful feature extraction and classification capabilities. However, two factors significantly impact the performance of deep-learning models. The size of the EEG datasets influences how effectively these models can learn. Similarly, the ability of classification models to extract features directly affects their accuracy in recognizing patterns. In this paper, we propose a Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network (MST-DGCN) to address these issues. In the data-preprocessing stage, we employ two strategies, data augmentation and transfer learning, to alleviate the problem of an insufficient data volume in deep learning. By using multi-scale convolution, spatial attention mechanisms, and dynamic graph neural networks, our model effectively extracts discriminative features. The MST-DGCN mainly consists of three parts: the multi-scale spatio-temporal module, which extracts multi-scale information and refines spatial attention; the dynamic graph convolution module, which extracts key connectivity information; and the classification module. We conduct experiments on real EEG datasets and achieve an accuracy of 77.89% and a Kappa value of 0.7052, demonstrating the effectiveness of the MST-DGCN in MI-BCI tasks. Our research provides new ideas and methods for the further development of MI-BCI systems. [ABSTRACT FROM AUTHOR]

Infrared small target detection (IRSTD) is crucial for applications in security surveillance, unmanned aerial vehicle identification, military reconnaissance, and other fields. However, small targets often suffer from resolution limitations, background complexity, etc., in infrared images, which poses a great challenge to IRSTD, especially due to the noise interference and the presence of tiny, low-luminance targets. In this paper, we propose a novel dual enhancement network (DENet) to suppress background noise and enhance dim small targets. Specifically, to address the problem of complex backgrounds in infrared images, we have designed the residual sparse enhancement (RSE) module, which sparsely propagates a number of representative pixels between any adjacent feature pyramid layers instead of a simple summation. To handle the problem of infrared targets being extremely dim and small, we have developed a spatial attention enhancement (SAE) module to adaptively enhance and highlight the features of dim small targets. In addition, we evaluated the effectiveness of the modules in the DENet model through ablation experiments. Extensive experiments on three public infrared datasets demonstrated that our approach can greatly enhance dim small targets, where the average values of intersection over union ( I o U ), probability of detection ( P d ), and false alarm rate ( F a ) reached up to 77.33%, 97.30%, and 9.299%, demonstrating a performance superior to the state-of-the-art IRSTD method. [ABSTRACT FROM AUTHOR]

The human eye scans visual information through scan paths, series of fixations. Analogous to these scan paths during the process of actual "seeing," we investigated whether similar scan paths are also observed while subjects are "rehearsing" stimuli in visuospatial working memory. Participants performed a continuous recall task in which they rehearsed the precise location and color of three serially presented discs during a retention interval, and later reproduced either the precise location or the color of a single probed item. In two experiments, we varied the direction along which the items were presented and investigated whether scan paths during rehearsal followed the pattern of stimulus presentation during encoding (left-to-right in Experiment 1; left-to-right/right-to-left in Experiment 2). In both experiments, we confirmed that the eyes follow similar scan paths during encoding and rehearsal. Specifically, we observed that during rehearsal participants refixated the memorized locations they saw during encoding. Most interestingly, the precision with which these locations were refixated was associated with smaller recall errors. Assuming that eye position reflects the focus of attention, our findings suggest a functional contribution of spatial attention shifts to working memory and are in line with the hypothesis that maintenance of information in visuospatial working memory is supported by attention-based rehearsal. [ABSTRACT FROM AUTHOR]

In the search for mechanisms that contribute to dyslexia, the term "attention" has been invoked to explain performance in a variety of tasks, creating confusion since all tasks do, indeed, demand "attention." Many studies lack an experimental manipulation of attention that would be necessary to determine its influence on task performance. Nonetheless, an emerging view is that children with dyslexia have an impairment in the exogenous (automatic/reflexive) orienting of spatial attention. Here we investigated the link between exogenous attention and reading ability by presenting exogenous spatial cues in the multi‐letter processing task—a task relevant for reading. The task was gamified and administered online to a large sample of children (N = 187) between 6 and 17 years. Children with dyslexia performed worse overall at rapidly recognizing and reporting strings of letters. However, we found no evidence for a difference in the utilization of exogenous spatial cues, resolving two decades of ambiguity in the field. Previous studies that claimed otherwise may have failed to distinguish attention effects from overall task performance or found spurious group differences in small samples. Research Highlights: We manipulated exogenous visual spatial attention using pre‐cues in a task that is relevant for reading and we see robust task effects of exogenous attention.We found no evidence for a deficit in utilizing exogenous spatial pre‐cues in children with dyslexia.However, children with dyslexia showed reduced recognition ability for all letter positions.Children with dyslexia were just as likely to make letter transposition errors as typical readers. [ABSTRACT FROM AUTHOR]

Object detection plays a crucial role in autonomous driving assistance systems. It requires high accuracy for prediction, a small size for deployment on mobile devices, and real-time inference speed to ensure safety. In this paper, we present a compact and efficient algorithm called YOLOX with United Attention Head (UAH-YOLOX) for detection in autonomous driving scenarios. By replacing the backbone network with GhostNet for feature extraction, the model reduces the number of parameters and computational complexity. By adding a united attention head before the YOLO head, the model effectively detects the scale, position, and contour features of targets. In particular, an attention module called Spatial Self-Attention is designed to extract spatial location information, demonstrating great potential in detection. In our network, the IOU Loss (Intersection of Union) has been replaced with CIOU Loss (Complete Intersection of Union). Further experiments demonstrate the effectiveness of our proposed methods on the BDD100k dataset and the Caltech Pedestrian dataset. UAH-YOLOX achieves state-of-the-art results by improving the detection accuracy of the BDD100k dataset by 1.70% and increasing processing speed by 3.37 frames per second (FPS). Visualization provides specific examples in various scenarios. [ABSTRACT FROM AUTHOR]

Selective attention is often studied separately as spatial, feature-based, and object-based attention. Recent research on feature-based attention in particular demonstrates that many selection principles are shared across these domains, suggesting that a core set of mechanisms supports effective information processing in the human brain, regardless of domain. Understanding the underlying representational structures, such as the spatial organization, various feature spaces, and object-based coding in visual cortex, will aid unifying theories of attention. Considering the structures of perceptual representations will also open up the possibility to study how selection sculpts entire representational spaces to support efficient information processing. Our visual system consciously processes only a subset of the incoming information. Selective attention allows us to prioritize relevant inputs, and can be allocated to features, locations, and objects. Recent advances in feature-based attention suggest that several selection principles are shared across these domains and that many differences between the effects of attention on perceptual processing can be explained by differences in the underlying representational structures. Moving forward, it can thus be useful to assess how attention changes the structure of the representational spaces over which it operates, which include the spatial organization, feature maps, and object-based coding in visual cortex. This will ultimately add to our understanding of how attention changes the flow of visual information processing more broadly. [ABSTRACT FROM AUTHOR]

The question of whether spatial attention can modulate initial afferent activity in area V1, as measured by the earliest visual event‐related potential (ERP) component "C1", is still the subject of debate. Because attention always enhances behavioral performance, previous research has focused on finding evidence of attention‐related enhancements in visual neural responses. However, recent psychophysical studies revealed a complex picture of attention's influence on visual perception: attention amplifies the perceived contrast of low‐contrast stimuli while dampening the perceived contrast of high‐contrast stimuli. This evidence suggests that attention may not invariably augment visual neural responses but could instead exert inhibitory effects under certain circumstances. Whether this bi‐directional modulation of attention also manifests in C1 and whether the modulation of C1 underpins the attentional influence on contrast perception remain unknown. To address these questions, we conducted two experiments (N = 67 in total) by employing a combination of behavioral and ERP methodologies. Our results did not unveil a uniform attentional enhancement or attenuation effect of C1 across all subjects. However, an intriguing correlation between the attentional effects of C1 and contrast appearance for high‐contrast stimuli did emerge, revealing an association between attentional modulation of C1 and the attentional modulation of contrast appearance. This finding offers new insights into the relationship between attention, perceptual experience, and early visual neural processing, suggesting that the attentional effect on subjective visual perception could be mediated by the attentional modulation of the earliest visual cortical response. Our findings reveal a positive correlation between attentional modulations on the C1 component and subjective contrast perception. Although the averaged C1 attention effect across participants did not reach statistical significance, our finding implies that the attentional modulation of neural responses in V1 may contribute to the attention effect on visual perception. [ABSTRACT FROM AUTHOR]

Alpha power modulations and slow negative potentials have previously been associated with anticipatory processes in spatial and temporal top‐down attention. In typical experimental designs, however, neural responses triggered by transient stimulus onsets can interfere with attention‐driven activity patterns and our interpretation of such. Here, we investigated these signatures of spatio‐temporal attention in a dynamic paradigm free from potentially confounding stimulus‐driven activity using electroencephalography. Participants attended the cued side of a bilateral stimulus rotation and mentally counted how often one of two remembered sample orientations (i.e., the target) was displayed while ignoring the uncued side and non‐target orientation. Afterwards, participants performed a delayed match‐to‐sample task, in which they indicated if the orientation of a probe stimulus matched the corresponding sample orientation (previously target or non‐target). We observed dynamic alpha power reductions and slow negative waves around task‐relevant points in space and time (i.e., onset of the target orientation in the cued hemifield) over posterior electrodes contralateral to the locus of attention. In contrast to static alpha power lateralization, these dynamic signatures correlated with subsequent memory performance (primarily detriments for matching probes of the non‐target orientation), suggesting a preferential allocation of attention to task‐relevant locations and time points at the expense of reduced resources and impaired performance for information outside the current focus of attention. Our findings suggest that humans can naturally and dynamically focus their attention at relevant points in space and time and that such spatio‐temporal attention shifts can be reflected by dynamic alpha power modulations and slow negative potentials. Psychophysiological processes like spatio‐temporal top‐down attention and its underlying neural signatures rarely operate in isolation and can thus be easily confounded by secondary processes like stimulus‐triggered neural activity inherent to traditional research designs. Here, we used a dynamic paradigm free from such interference to reveal previously hidden and behaviorally relevant dynamics of alpha power and slow negative potentials, thereby providing a significant contribution to our knowledge about these key signatures of spatio‐temporal top‐down attention. [ABSTRACT FROM AUTHOR]

It is known for a long time that some drawings composed of points, lines, and areas are systematically misperceived. The origin of these geometrical illusions is still unknown. Here we outline how a recent progress in attentional research contributes to a better understanding of such perceptual distortions. The basic idea behind this approach is that crucial elements of a drawing are differently attended. These changes in the allocation of spatial attention go along with systematic changes in low-level spatial coding. As a result, changes in the perception of spatial extent, angles, positions, and shapes can arise. How this approach can be applied to individual illusions is discussed. [ABSTRACT FROM AUTHOR]