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Background: Liver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals. Methods: We designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model’s robustness. Results: The H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9. Conclusion: The proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics. [ABSTRACT FROM AUTHOR]

The simple act of viewing and grasping an object involves complex sensorimotor control mechanisms that have been shown to vary as a function of multiple object and other task features such as object size, shape, weight, and wrist orientation. However, these features have been mostly studied in isolation. In contrast, given the nonlinearity of motor control, its computations require multiple features to be incorporated concurrently. Therefore, the present study tested the hypothesis that grasp computations integrate multiple task features superadditively in particular when these features are relevant for the same action phase. We asked male and female human participants to reach-to-grasp objects of different shapes and sizes with different wrist orientations. Also, we delayed the movement onset using auditory signals to specify which effector to use. Using electroencephalography and representative dissimilarity analysis to map the time course of cortical activity, we found that grasp computations formed superadditive integrated representations of grasp features during different planning phases of grasping. Shape-by-size representations and size-by-orientation representations occurred before and after effector specification, respectively, and could not be explained by single-feature models. These observations are consistent with the brain performing different preparatory, phase-specific computations; visual object analysis to identify grasp points at abstract visual levels; and downstream sensorimotor preparatory computations for reach-to-grasp trajectories. Our results suggest the brain adheres to the needs of nonlinear motor control for integration. Furthermore, they show that examining the superadditive influence of integrated representations can serve as a novel lens to map the computations underlying sensorimotor control. [ABSTRACT FROM AUTHOR]

Visual Attention Prediction (VAP) is widely applied in GIS research, such as navigation task identification and driver assistance systems. Previous studies commonly took color information to detect the visual saliency of natural scene images. However, these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks. To better predict visual attention while driving tasks, in this paper, we firstly propose an Adaptive Feature Integration Fully Convolutional Network (AdaFI-FCN) using Scene-Adaptive Weights (SAW) to integrate RGB-D, motion and semantic features. The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models (AUC-Judd = 0.971, CC = 0.767, KL = 1.046, SIM = 0.579). In addition, the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes. The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes, such as AR-aided navigation, indoor navigation, and street-view image reading. [ABSTRACT FROM AUTHOR]

Understanding the structural similarity between genomes is pivotal in classification and phylogenetic analysis. As the number of known genomes rockets, alignment-free methods have gained considerable attention. Among these methods, the natural vector method stands out as it represents sequences as vectors using statistical moments, enabling effective clustering based on families in biological taxonomy. However, determining an optimal metric that combines different elements in natural vectors remains challenging due to the absence of a rigorous theoretical framework for weighting different k-mers and orders. In this study, we address this challenge by transforming the determination of optimal weights into an optimization problem and resolving it through gradient-based techniques. Our experimental results underscore the substantial improvement in classification accuracy achieved by employing these optimal weights, reaching an impressive 92.73% on the testing set, surpassing other alignment-free methods. On one hand, our method offers an outstanding metric for virus classification, and on the other hand, it provides valuable insights into feature integration within alignment-free methods.

Visual Attention Prediction (VAP) is widely applied in GIS research, such as navigation task identification and driver assistance systems. Previous studies commonly took color information to detect the visual saliency of natural scene images. However, these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks. To better predict visual attention while driving tasks, in this paper, we firstly propose an Adaptive Feature Integration Fully Convolutional Network (AdaFI-FCN) using Scene-Adaptive Weights (SAW) to integrate RGB-D, motion and semantic features. The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models (AUC-Judd = 0.971, CC = 0.767, KL = 1.046, SIM = 0.579). In addition, the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes. The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes, such as AR-aided navigation, indoor navigation, and street-view image reading.

The intention to act influences the computations of various task-relevant features. However, little is known about the time course of these computations. Furthermore, it is commonly held that these computations are governed by conjunctive neural representations of the features. But, support for this view comes from paradigms arbitrarily combining task features and affordances, thus requiring representations in working memory. Therefore, the present study used electroencephalography and a well-rehearsed task with features that afford minimal working memory representations to investigate the temporal evolution of feature representations and their potential integration in the brain. Female and male human participants grasped objects or touched them with a knuckle. Objects had different shapes and were made of heavy or light materials with shape and weight being relevant for grasping, not for “knuckling.” Using multivariate analysis showed that representations of object shape were similar for grasping and knuckling. However, only for grasping did early shape representations reactivate at later phases of grasp planning, suggesting that sensorimotor control signals feed back to the early visual cortex. Grasp-specific representations of material/weight only arose during grasp execution after object contact during the load phase. A trend for integrated representations of shape and material also became graspspecific but only briefly during the movement onset. These results suggest that the brain generates action-specific representations of relevant features as required for the different subcomponents of its action computations. Our results argue against the view that goaldirected actions inevitably join all features of a task into a sustained and unified neural representation. [ABSTRACT FROM AUTHOR]

Many studies have shown that the human visual system has two major functionally distinct cortical visual pathways: a ventral pathway, thought to be important for object recognition, and a dorsal pathway, thought to be important for spatial cognition. According to our and others previous studies, artificial neural networks with two segregated pathways can determine objects' identities and locations more accurately and efficiently than one-pathway artificial neural networks. In addition, we showed that these two segregated artificial cortical visual pathways can each process identity and spatial information of visual objects independently and differently. However, when using such networks to process multiple objects' identities and locations, a binding problem arises because the networks may not associate each object's identity with its location correctly. In a previous study, we constrained the binding problem by training the artificial identity pathway to retain relative location information of objects. This design uses a location map to constrain the binding problem. One limitation of that study was that we only considered two attributes of our objects (identity and location) and only one possible map (location) for binding. However, typically the brain needs to process and bind many attributes of an object, and any of these attributes could be used to constrain the binding problem. In our current study, using visual objects with multiple attributes (identity, luminance, orientation, and location) that need to be recognized, we tried to find the best map (among an identity map, a luminance map, an orientation map, or a location map) to constrain the binding problem. We found that in our experimental simulations, when visual attributes are independent of each other, a location map is always a better choice than the other kinds of maps examined for constraining the binding problem. Our findings agree with previous neurophysiological findings that show that the organization or map in many visual cortical areas is primarily retinotopic or spatial. [ABSTRACT FROM AUTHOR]

Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi'an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi'an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions. [ABSTRACT FROM AUTHOR]

Abstract Due to the ignoring of rich spatio-temporal and global contextual information with convolutional neural networks in features extraction, the traditional method is prone to tracking drift or even failure in complex scenario, especially for the tiny targets in aerial photography scenario. In this work, it proposes a transformer feature integration network (TFITrack) to obtain diverse and comprehensive target feature for the robust object tracking. Based on the typical transformer architecture, it optimizes encoder and decoder structure for aggregating discriminative spatio-temporal information and global context-awareness feature. Furthermore, the encoder introduces the similarity calculation layer and dual-attention module; the aim is to deepen the similarity between features and make corrections for channel and spatial dimensions, and feature representation is improved. Finally, with the introduction of the temporal context filtering layer, unimportant feature information is ignored adaptively, obtaining a balance between the parameters number reduction and stable performance. Experimental results show that the proposed tracking algorithm exhibits excellent tracking performance on seven benchmark datasets, especially on the aerial dataset UAV123, UAV20L, and UAV123@10fps, which presents the advantages of the novel method in dealing with fast motion and external interference.

The field of person re-identification (re-ID) encounters challenges such as inter-person similarity and occlusion. In this paper, we introduce an occluded pedestrian re-ID method based on Multiple Fusion and Semantic feature Mining (MFSM) to mitigate the adverse effects of occlusion on feature representation. Specifically, we devise a Multi-branch Strategic Enhancement Module (SEM) to bolster the robustness of data augmentation in addressing the issues posed by data memorization more consistently. This module emulates various real-world disturbances by implementing distinct and independent augmentation strategies. Furthermore, we propose the Triplet Cross Unit (TCU) to comprehensively exploit and consolidate both regional and relational visual cues, as well as multi-level features. The TCU facilitates transformers in acquiring an early understanding of translation invariance in images by transferring local patterns such as edges, textures, and colors from the lower CNN layers to the shallower transformer layers. Simultaneously, deeper transformer features offer more abstracted semantic visual representations that complement CNN's high-level semantic features. Lastly, we introduce the Global Squeeze-Excitation Fusion (GSEF) module to address the challenge of global feature segregation across different models in final outputs. The GSEF selectively merges features based on attention mechanisms, prioritizing the comprehensive utilization of valid global features. Extensive experiments demonstrate the state-of-the-art performance of our model in handling pedestrian occlusion, thus validating the efficacy of our method.

The field of person re-identification (re-ID) encounters challenges such as inter-person similarity and occlusion. In this paper, we introduce an occluded pedestrian re-ID method based on Multiple Fusion and Semantic feature Mining (MFSM) to mitigate the adverse effects of occlusion on feature representation. Specifically, we devise a Multi-branch Strategic Enhancement Module (SEM) to bolster the robustness of data augmentation in addressing the issues posed by data memorization more consistently. This module emulates various real-world disturbances by implementing distinct and independent augmentation strategies. Furthermore, we propose the Triplet Cross Unit (TCU) to comprehensively exploit and consolidate both regional and relational visual cues, as well as multi-level features. The TCU facilitates transformers in acquiring an early understanding of translation invariance in images by transferring local patterns such as edges, textures, and colors from the lower CNN layers to the shallower transformer layers. Simultaneously, deeper transformer features offer more abstracted semantic visual representations that complement CNN's high-level semantic features. Lastly, we introduce the Global Squeeze-Excitation Fusion (GSEF) module to address the challenge of global feature segregation across different models in final outputs. The GSEF selectively merges features based on attention mechanisms, prioritizing the comprehensive utilization of valid global features. Extensive experiments demonstrate the state-of-the-art performance of our model in handling pedestrian occlusion, thus validating the efficacy of our method. • MFSM framework enables CNN-Transformer interaction, improving person re-identification accuracy, especially under occlusion. • Our strategic enhancement module expands data richness, suppressing noise and improving occlusion handling. • TCU combines CNN and Transformer features, improving re-ID performance without overemphasizing local features. • GSEF module consolidates diverse semantic features, enriching re-ID representation and improving accuracy. [ABSTRACT FROM AUTHOR]

Plaids consist of two orthogonally oriented gratings. However, their structure may be perceived as a checkerboard with an oblique orientation or as two overlaid gratings. We used a visual search paradigm to investigate how plaids with different perceived structures influence search efficiency for determining whether a preattentive plaid-specific or grating-specific mechanism is involved in representing the plaid. Participants searched for a grating among plaids (Experiment 1) or a plaid among gratings (Experiment 2). Contrast (low vs. high), composed spatial frequency (SF) of the plaid (coherent vs. mixed), and target--distractor orientation relationship (component orientation-- aligned vs. structure orientation--aligned) were manipulated to vary the perceived structure of the plaid. Parallel search was observed when the target was a coherent checkerboard plaid, and orientation manipulation did not influence search efficiency. However, search performance was less efficient when the target plaid was perceived as overlaid gratings. When searching for a grating among plaids, low-SF gratings were located more quickly than high-SF gratings, regardless of the plaid distractor type. The results provide evidence for the existence of a preattentive plaid-specific mechanism that is distinct from the grating-sensitive mechanism. [ABSTRACT FROM AUTHOR]

view of the large amount of garbage and the fact that a picture contains multiple garbage objects, this study proposes a garbage detection and classification algorithm based on the improved MobileNet network, which integrates the MobileNet network into YOLOv5(You Only Look Oncev5) target detection algorithm. At the same time, the CBAM(Convolutional Block Attention Modul) module is introduced in the backbone to filter meaningful information, and the vision transformer is used to aggregate and form image features. In addition, the weighted bidirectional feature pyramid network is used to distinguish the contribution of different features. At the same time, the ECA(Efficient Channel Attention) module is introduced to combine the image features and transmit them to the prediction layer. Finally, in order to obtain better performance when there is occlusion between garbage targets, soft-NMS(soft-Non Maximum Suppression) method and Alpha-IoU(Alpha-Intersection over Union) loss function is used to predict the extracted features. The experimental results show that the method proposed in this study can realize the location and recognition of multi-target and multi-category garbage., and the mAP(mean Average Percision) value reaches 90.31%, which is 4.95% higher than that of YOLOv5 network, and the processing speed is shortened by about 2.4 seconds. Compared with the Faster R-CNN(Region-based Convolutional Neural Network) algorithm which integrates ResNet(Residual Network) network, the algorithm proposed in this study improves the processing efficiency on the premise of ensuring the accuracy. [ABSTRACT FROM AUTHOR]

Our sensory system is able to build a unified perception of the world, which although rich, is limited and inaccurate. Sometimes, features from different objects are erroneously combined. At the neural level, the role of the parietal cortex in feature integration is well‐known. However, the brain dynamics underlying correct and incorrect feature integration are less clear. To explore the temporal dynamics of feature integration, we studied the modulation of different frequency bands in trials in which feature integration was correct or incorrect. Participants responded to the color of a shape target, surrounded by distractors. A calibration procedure ensured that accuracy was around 70% in each participant. To explore the role of expectancy in feature integration, we introduced an unexpected feature to the target in the last blocks of trials. Results demonstrated the contribution of several frequency bands to feature integration. Alpha and beta power was reduced for hits compared to illusions. Moreover, gamma power was overall larger during the experiment for participants who were aware of the unexpected target presented during the last blocks of trials (as compared to unaware participants). These results demonstrate that feature integration is a complex process that can go wrong at different stages of information processing and is influenced by top‐down expectancies. When exploring the brain dynamics of feature integration, previous studies have only explored one frequency band. We explore if and how different cognitive failures can lead to incorrect feature integration by exploring the modulation of different frequency bands. Our results support that feature integration can go wrong at different stages including pre‐stimulus attentional processes (alpha), perceptual representations (post‐stimulus, alpha and beta), and working memory (gamma). [ABSTRACT FROM AUTHOR]

Accurate understanding of maritime traffic flow is crucial for optimizing waterway and port resources. However, the complex spatiotemporal characteristics and non-stationary sequences in maritime traffic pose challenges for feature extraction. This study proposes a deep learning approach that integrates Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit Network (BIGRU) for predicting maritime traffic flow. CNN is employed to extract feature information, while BIGRU captures temporal dependencies, enhancing the understanding of intrinsic features’ temporal changes. To further improve predictive performance, this study introduce the Whale Optimization Algorithm (WOA) for multiple objective optimization, which fine-tunes the CNN-BIGRU model parameters. By using eight years of monthly traffic flow data collected from the Dayaowan Port area in Dalian, the validity of this optimized model was rigorously verified. The results demonstrate that the WOA-optimized CNN-BIGRU provides an efficient forecast of maritime traffic flow with the Mean Absolute Error (MAE) of 4.084, Root Mean Square Error (RMSE) of 7.9719 and Mean Absolute Percentage Error (MAPE) of 1.262%. Compared to the original CNN-BiGRU models, this optimized approach reduced the prediction errors by 59.4%, 33.6%, and 54.2%, respectively. This further highlights its effectiveness in addressing the complexities of maritime traffic flow and provides valuable references for further research in this region.

A proposed method, Enhancement, integration, and Expansion, aims to activate the representation of detailed features for occluded person re-identification. Region and context are two important and complementary features, and integrating them in an occluded environment can effectively improve the robustness of the model. Firstly, a self-enhancement module is designed. Based on the constructed multi-stream architecture, rich and meaningful feature interference is introduced in the feature extraction stage to enhance the model's ability to perceive noise. Next, a collaborative integration module similar to cascading cross-attention is proposed. By studying the intrinsic interaction patterns of regional and contextual features, it adaptively fuses features across streams and enhances the diverse and complete representation of internal information. The module is not only robust to complex occlusions, but also mitigates the feature interference problem due to similar appearances or scenes. Finally, a matching expansion module that enhances feature discriminability and completeness is proposed. Providing more stable and accurate features for recognition. Compared with state-of-the-art methods on two occluded and holistic datasets, the proposed method is proved to be advanced and the effectiveness of the module is proved by extensive ablation studies. [ABSTRACT FROM AUTHOR]

Semantic memory is characterized by a hierarchical organization of concepts based on shared properties. However, this aspect is insufficiently dealt with in recent neurocomputational models. Moreover, in many cognitive problems that exploit semantic memory, gamma-band synchronization can be relevant in favoring information processing and feature binding. In this work, we propose an attractor network model of semantic memory. Each computational unit, coding for a different feature, is described with a neural mass circuit oscillating in the gamma range. The model is trained with an original nonsymmetric Hebb rule based on a presynaptic gating mechanism. After training, the network creates a taxonomy of categories, distinguishes between subordinate and superordinate concepts, and discriminates between salient and marginal features. Examples are provided concerning a fourteen-animal taxonomy, including several subcategories. A sensitivity analysis reveals the robustness of the network but also points out conditions leading to confusion among categories, similar to the one observed in dreaming and some neurological disorders. Finally, the analysis emphasizes the role of fast GABAergic interneurons and inhibitory-excitatory balance to allow the correct synchronization of features. The model represents an original attempt to deal with a hierarchical organization of objects in semantic memory and correlated patterns, still exploiting gamma-band synchronization to favor neural processing. The same ideas, introduced in a more sophisticated multilayer network, can deepen our knowledge of semantic memory organization in the brain. Finally, they can open new perspectives in quantitatively analyzing neurological disorders connected with distorted semantics. [ABSTRACT FROM AUTHOR]

Previous studies of illusory conjunction (IC) mainly focused on alphabetic languages, while researchers have poorly understood the IC mechanism of Chinese words as an ideographic writing system. In the present study, we aimed to investigate the dynamic changes of IC effects for Chinese words under different stimulus exposure times and spatial arrangements. We conducted two experiments with a 3 (Condition: IC, non-IC-same, non-IC-different) × 3 (Exposure time: 38 ms, 88 ms, 138 ms) within-subject design. The results showed that in the IC condition, the two characters recombined regardless of exposure time as long as they could form an orthographically correct new word, demonstrating the universality of IC. In non-IC conditions, increasing exposure time decreased response time and significantly reduced error rate, indicating that attention played a decisive role in perceptual processing. The spatial arrangement had no impact on IC production. These findings support the feature confirmation account model, suggesting that attention modulates IC through top-down feature confirmation processes. These data expand an understanding of IC mechanisms, validate the role of attention in feature confirmation, and elucidate the inimitable mechanism of the Chinese word IC influenced by both low-level visual processing and high-level cognitive control. [ABSTRACT FROM AUTHOR]

Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi’an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi’an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions.

This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex environments. The article proposes a global feature extraction method and a local texture feature extraction method to extract global and regional features by enhancing feature reuse and information flow through dense connectivity and ShuffleNet V2 framework. Then, a multiscale feature exchange method is used to fuse different scale features to enhance the capture of detail information. Finally, efficient feature integration is achieved by the multiscale feature fusion method. Experimental results on the CK+ and FER2013 datasets show that the accuracy of this method on face expression recognition reaches 97.24% and 95.93%, respectively, and the average recognition accuracy in the face attribute recognition experiments on the CelebA dataset is 97.11%, which is significantly better than the comparison algorithm. In addition, the analysis of the recognition effect on low-resolution faces shows that this paper’s method achieves a recognition accuracy of 54.03% at a resolution of 15 × 15 and a high accuracy of over 99% at resolutions of 70 × 70 and above. These results show that the face attribute recognition technique based on fine-grained features proposed in this paper significantly improves recognition accuracy.

In action–control research, typically, stimulus sparse displays are used. This might be one reason why previous theorizing focuses on the (top-down) demands of response selection (e.g., what key to press), while often ignoring (bottom-up) demands of stimulus selection (e.g., what stimulus to attend). However, complex perceptual situations may pose selection demands that make additional, response-unrelated feature dimensions relevant for response selection. The major stimulus characteristic affecting perceptual selection is salience. In research on visual attention, there is a debate about the conditions under which salience becomes effective. We related both debates in two experiments, in which we modulated display set size as well as salience in a stimulus–response binding task. In sum, the data of these experiments (pooled N = 138) demonstrated that salience furthered the integration of stimulus features in displays with many stimuli more than in sparse displays. Our results demonstrate the neglected impact of salience on action–control when going beyond the very simplistic displays common in action–control research. Public Significance Statement: In research on the question of how humans act, experimental displays comprise usually only very few stimuli. We argue that this has led to an underestimation of an important variable that influences the selection of sensory information, namely salience. In complex selection situations, that is, most everyday situations outside the laboratory, salience guides human attention toward potentially relevant objects or areas. We show that this holds true for action research if action experiments require more than the typical, artificially low selection demands. [ABSTRACT FROM AUTHOR]

Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the "summation" way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a contextual, relational attention-based recognition network (CRABR-Net) was presented, which extracts different convolutional feature maps from CNN, focuses important feature content by using a simple, parameter-free attention module (SimAM), fuses the adjacent feature maps by using the complementary relationship feature map calculation, improves the feature learning ability by using the enhanced relationship feature map calculation, and finally uses the concatenated feature maps from different layers for RSSOR. Experimental results show that CRABR-Net exploits the relationship between the different CNN layers to improve recognition performance, achieves better results compared to several state-of-the-art algorithms, and the average accuracy on AID, UC-Merced, and RSSCN7 can be up to 96.46%, 99.20%, and 95.43% with generic training ratios. [ABSTRACT FROM AUTHOR]

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural 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.)

Real-time and accurate awareness of the grain situation proves beneficial for making targeted and dynamic adjustments to cleaning parameters and strategies, leading to efficient and effective removal of impurities with minimal losses. In this study, harvested maize was employed as the raw material, and a specialized object detection network focused on impurity-containing maize images was developed to determine the types and distribution of impurities during the cleaning operations. On the basis of the classic contribution Faster Region Convolutional Neural Network, EfficientNetB7 was introduced as the backbone of the feature learning network and a cross-stage feature integration mechanism was embedded to obtain the global features that contained multi-scale mappings. The spatial information and semantic descriptions of feature matrices from different hierarchies could be fused through continuous convolution and upsampling operations. At the same time, taking into account the geometric properties of the objects to be detected and combining the images' resolution, the adaptive region proposal network (ARPN) was designed and utilized to generate candidate boxes with appropriate sizes for the detectors, which was beneficial to the capture and localization of tiny objects. The effectiveness of the proposed tiny object detection model and each improved component were validated through ablation experiments on the constructed RGB impurity-containing image datasets. [ABSTRACT FROM AUTHOR]

Spatial and temporal information are two major feature dimensions of human movements. How these two types of information are represented in working memory—whether as integrated units or as individual features—influences how much information might be retained and how the retained information might be manipulated. In this study, we investigated how spatial (path/trajectory) and temporal (speed/rhythm) information of complex whole-body movements are represented in working memory under a more ecologically valid condition wherein the spatiotemporal continuity of movement sequences was considered. We found that the spatial and temporal information are not automatically integrated but share the storage capacity and compete for a common pool of cognitive resources. The finding rejects the strong form of object-based representation and supports the partial independence of spatial and temporal processing. Nevertheless, we also found that contextual factors, such as the way movements are organized and displayed, can further modulate the level of object-based representation and spatiotemporal integration. [ABSTRACT FROM AUTHOR]

Visual discomfort prediction (VDP) is used to evaluate a user's visual discomfort when viewing stereoscopic images. The focus of this study is to explore more representative and distinctive features to describe complex visual discomfort perception. We consider integrating hand-crafted features and high-level semantic features based on deep learning to describe discomfort perception, and propose a multi-task VDP model (M2VDP) based on multi-view feature fusion. In particular, the model reinforces the positive effects of scene structure and depth difference on visual discomfort, and dynamically learns high-level semantic features at different scales by constructing a multi-scale neural network structure. In addition, using the multi-task learning, the prediction task of discomfort score can be guided by correctly classifying the discomfort level of stereoscopic images. The proposed method is verified on the public IEEE-SA database, and the results show that the proposed method achieves optimal performance. The ablation experimental results show that the multi-view feature fusion structure and multi-task learning are effective. [ABSTRACT FROM AUTHOR]

Some dyslexics cannot process multiple letters simultaneously. It has been argued that this reduced visuo-attentional (VA) letter span could result from poor reading ability and experience. Here, moving away from reading context, we showed that dyslexic group exhibited slower visual search than normal readers group for "symbols", defined as graphic stimuli made up of separable visual features, but not for filled objects. Slowness in symbol visual search was explained by reduced VA field and atypical ocular behaviour when processing those letter-like stimuli and was associated with reduced VA letter span and impaired elementary visuo-spatial perception. Such a basic visual search deficit can hardly be attributed to poor reading ability and experience. Moreover, because it is specific to letter-like stimuli (i.e., "symbols"), it can specifically hinder reading acquisition. Symbol visual search can easily be tested in the pre-reading phase, opening up prospects for early risk detection and prevention of VA dyslexia. [ABSTRACT FROM AUTHOR]

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural 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.)

This work proposes a new approach based on YOLOX model enhancement for the helmet-wearing real-time detection task, which is plagued by low detection accuracy, incorrect detection, and missing detection. First, in the backbone network, recursive gated convolution (gnConv) is utilized instead of traditional convolution, hence addressing the issue of extracting many worthless features due to excessive redundancy in the process of feature extraction using conventional convolution. Replace the original FPN layer in the Neck network with the EfficientNet-BiFPN layer. Realize top–down and bottom–up bidirectional fusion of deep and shallow features to improve the flow of feature data between network layers. Lastly, the SIOU cross-entropy loss function is implemented to address the issue of missed detections in crowded environments and further increase the model's detection precision. Experiments and data comparisons indicate that the modified model's average detection accuracy is 95.5%, which is 5.4% higher than that of the original network model, and that the detection speed has been dramatically increased to suit actual production requirements. [ABSTRACT FROM AUTHOR]

Due to the inherent overlap between the background and rain streaks, most deraining methods have suffered greatly from the over‐smoothing effect, which restrains the deraining models' performance to a great extent. To address this issue, a progressive integration network (PIN) is presented to reduce this effect. Specifically, in our network, the high‐level representations will be used to enhance the low‐level feature maps through our proposed global integration and local integration blocks. Moreover, based on the difficulty of prediction, a novel curriculum‐learning mechanism is explored to ease the procedure of training. Extensive experimental results demonstrate that the proposed method achieves considerable performance in contrast to other deraining methods. [ABSTRACT FROM AUTHOR]

Deep learning-based methods have shown great potential in image inpainting, especially when dealing with large missing regions. However, the inpainted results often suffer from blurring, and improper textures can be created without an understanding of semantic information. In order to extract more features from the known regions, we propose a multi-level feature integration (MFI) network for image inpainting. We complete hole regions by two generators. For each generator, we use the MFI network to fill the hole region with multi-level skip connections. With multi-level feature integration, the network gains more knowledge about the global semantic structures and local fine details. Moreover, instead of a deconvolution layer or an interpolation algorithm, we adopt a sub-pixel layer to up-sample feature maps and produce more coherent results. We use PatchGAN to support the refinement generator network to produce more discriminative detail. Our experiments done with the Paris StreetView, CelebA-HQ and Places2 datasets demonstrate the effectiveness of our MFI network for producing visually pleasing results with semantically ordered textures. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Residual-like networks, such as ResNets, mainly focus on the skip connection to avoid gradient vanishing. However, the skip connection mechanism limits the utilization of intermediate features due to simple iterative updates. To mitigate the redundancy of residual-like networks, we design Attentive Feature Integration (AFI) modules, which are widely applicable to most residual-like network architectures, leading to new architectures named AFI-Nets. AFI-Nets explicitly model the correlations among different levels of features and selectively transfer features with a little overhead. AFI-ResNet-152 obtains a 1.24% relative improvement on the ImageNet dataset while decreases the FLOPs by about 10% and the number of parameters by about 9.2% compared to ResNet-152. [ABSTRACT FROM AUTHOR]

Smart learning has been concerned in the domain of education, and how to correctly predict students' performance is one of meaningful issues. Mature performance prediction methods are promising to be applied to education systems to make educators know more about students' learning states and provide students with academic help in time. Based on students' historical course results and courses' basic information, our work proposes a student's course results prediction model using reasonable data processing and machine learning methods, which can well achieve the goal of out-of-sample results prediction. Our model framework mainly has two parts: numeric and non-numeric features vector embedding algorithms; model optimization based on data augmentation and integration. Firstly, we generate respectively feature embedding vectors for non-numeric and numeric data ; Secondly by applying relative statistical methods we combine the previous embedding vectors to form a synthetic feature matrix, then use the Gramian Angular Field and Markov Transition Field to augment the data information and extend the feature embedding by matrix integration. To verify the validity of our model in experiments, we implement multilayer perceptron as training model to realize prediction task. Our model has quite good interpretability so that it can well be understood what happens in the model framework and how the data is processed throughout the model pipeline. This can well help us make relative adjustments and tunes according to different problems and scenarios, thus get better prediction results. [ABSTRACT FROM AUTHOR]

The accurate taxonomic identification of fusulinid fossils holds significant scientific value in palaeontology, paleoecology, and palaeogeography. However, imbalanced image samples lead to the model preferring to learn features from categories with many samples while ignoring fewer sample categories, greatly reducing the prediction accuracy of fusulinid fossil identification. Moreover, the textual description of fusulinid fossils contains rich feature information. We collected and created an order fusulinid multimodal (OFM) dataset for research. We proposed a transformer-based multimodal integration framework (TMIF) using deep learning for fusulinid fossil identification. Compared to traditional neural networks, the transformer can create global dependencies between features at different locations. TMIF incorporates image and text branches dedicated to extracting features for both modalities, and a pivotal cross-modal integration module that allows visual features to learn textual semantic features sufficiently to obtain a more comprehensive feature representation. Experimental evaluation using the OFM dataset shows that TMIF achieves a prediction accuracy of 81.7%, which is a 2.8% improvement over the only image-based method. Further comparative analyses across multiple networks affirm that the TMIF performs optimally in addressing the taxonomic identification of fusulinid fossils with imbalanced samples. • We collected and created a multi-version order fusulinid multimodal (OFM) dataset. • We propose a cross-modal integration module without using the common tandem fusion. • We propose a framework to overcome the fossil image sample imbalance problem. [ABSTRACT FROM AUTHOR]

Image Quality Assessment (IQA) has received unprecedented attention due to the extensive applications in benchmarking image processing algorithms and systems. Despite great progress in IQA, most previous frameworks either utilized only high-level semantic features or simply stacked multi-level features to account for the distortions, resulting in limited performance. The initial visual perception is formed after that the information is processed by the primary visual cortex and the advanced visual cortex. However, the information transmission of the cerebral cortex is not unidirectional, that is, the higher brain area can affect the primary brain area in turn, regulate its sensitivity and preference, so as to help the brain to understand the external world more finely. Inspired by this, we propose a novel blind image quality index with high-level Semantic Guidance and low-level fine-grained Representation (SGRNet). First, the versatile backbone, called pyramid vision Transformer, is used to simulate the multilevel information processing in the brain, generating multilevel feature representation. Second, we propose a novel high-level semantic guidance module to simulate the feedback mechanism between levels of the visual cortex. Third, a simple but effective fine-grained feature extraction module is proposed for high-level information compensation and lower-level content perception. After this, an attention mechanism-based enhancement module is proposed to further learn the above two features respectively. Finally, a bilinear pooling-based regression module is proposed to integrate the enhanced features of the two parts and map them to the quality score. Extensive experiments on six challenging public datasets show that the proposed SGRNet can deal well with both the simulated and authentic distortions and achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Change detection (CD) aims to detect change objects of interest from bi-temporal images and is a hot research direction due to its value in human civilization. Existing CD methods usually employ convolution or transformer structures to extract image features. However, they neglect the shortcomings and complementary advantages of different structures. In addition, the potential relationship between bi-temporal input features is not adequately correlated, resulting in the difficulty of learning changed region features. To this end, we propose a novel Semantic-aware Transformer with Feature Integration (STFI) method to adequately correlate bi-temporal features by exploring complementary advantages from convolution or transformer structures for remote sensing change detection. Specifically, the residual module is employed to extract local information in the shallow layer, while the transformer module is adopted to model long-distance dependencies in the deep layer. We skillfully link convolution and transformer operations to learn complementary local and global features. In addition, we propose to integrate high-level bi-temporal semantic information in two aspects. On the one hand, the feature generation module is built to generate fused features in the local view from the convolutional operation. On the other hand, the feature integration module is designed to fuse global features from transformer operation. Furthermore, we develop an objective function containing two items, i.e., the cross-entropy loss and the dice loss, to optimize the whole CD model. Abundant experiments demonstrate that the proposed STFI surpasses other superior methods by 1.18%, 1.99%, and 3.05%, 5.15% in terms of F1 and IoU on LEVIR-CD and WHU-CD, respectively. [Display omitted] • We fuse convolution and transformer modules to model the local and global contexts. • We design feature generation/integration modules to correlate bi-temporal features. • We develop a function with the cross-entropy and dice losses to optimize the model. • We provide an in-depth analysis and prove that STFI achieves superior performance. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of saliency detection, convolutional neural networks have emerged as catalysts for innovation, but remain largely tailored for RGB imagery, falling short in the context of infrared images, particularly in memory-restricted environments. These existing approaches tend to overlook the wealth of contour information vital for a nuanced analysis of infrared images. Addressing this notable gap, we introduce the novel Two-branch Edge Guided Lightweight Network (TBENet), designed explicitly for the robust analysis of infrared image saliency detection. The main contributions of this paper are as follows. First, we formulate the saliency detection task as two subtasks, contour enhancement and foreground segmentation. Therefore, the TBENet is divided into two specialized branches: a contour prediction branch for extracting target contour and a saliency map generation branch for separating the foreground from the background. The first branch employs an encoder–decoder architecture to meticulously delineate object contours, serving as a guiding blueprint for the second branch. This latter segment adeptly integrates spatial and semantic data, creating a precise saliency map that is refined further by an innovative edge-weighted contour loss function. Second, to enhance feature integration capabilities, we propose depthwise multi-scale and multi-cue modules, facilitating sophisticated feature aggregation. Third, a high-level linear bottleneck module is devised to ensure the extraction of rich semantic information, and by replacing the standard convolution with the depthwise convolution, it is beneficial to reduce model complexity. Additional, we reduce the number of channels of the feature maps from each stage of the decoder to further enhance the lightweight of the model. Last, we construct a novel infrared ship dataset Small-IRShip to train and evaluate our proposed model. Experimental results on the homemade dataset Small-IRShip and two publicly available datasets, namely RGB-T and IRSTD-1k, demonstrate TBENet's superior performance over state-of-the-art methods, affirming its effectiveness in harnessing edge information and incorporating advanced feature integration strategies. [ABSTRACT FROM AUTHOR]

Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the “summation” way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a contextual, relational attention-based recognition network (CRABR-Net) was presented, which extracts different convolutional feature maps from CNN, focuses important feature content by using a simple, parameter-free attention module (SimAM), fuses the adjacent feature maps by using the complementary relationship feature map calculation, improves the feature learning ability by using the enhanced relationship feature map calculation, and finally uses the concatenated feature maps from different layers for RSSOR. Experimental results show that CRABR-Net exploits the relationship between the different CNN layers to improve recognition performance, achieves better results compared to several state-of-the-art algorithms, and the average accuracy on AID, UC-Merced, and RSSCN7 can be up to 96.46%, 99.20%, and 95.43% with generic training ratios.

The healthy liver plays more than 500 organic roles in the human body, while a malfunction may be dangerous or even deadly. Early diagnosis and treatment of liver disease can improve the likelihood of survival. Machine learning (ML) is a powerful tool that can assist healthcare professionals during the diagnostic process for a hepatic patient. The standard ML system includes the methods of data pre-processing, feature extraction, and classification. In the feature extraction stage, ML researchers frequently use projection-based feature extraction approaches to remove data redundancy, but this does not produce the desired results. In addition, most statistical projection methods have different purposes when projecting original features. The Indian liver patient dataset (ILPD) from the University of California, Irvin (UCI) repository is used in this study to classify chronic liver disease. The data set has 583 patient disease records; 416 patients have liver disease, and 167 do not. Using several projection methods, we proposed an integrated feature extraction approach to categorize liver patients. In the pipeline, the proposed method first imputes the missing values and outliers for pre-treatment. Then, integrated feature extraction applies the pre-processed data to extract the significant features for classification. A simulation study is also being conducted to strengthen the suggested methodology. The proposed approach incorporates several ML algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), and the ensemble voting classifier. The offered system has an accuracy of 88.10%, a precision of 85.33%, a recall of 92.30%, an F1 score of 88.68%, and an AUC score of 88.20% in predicting liver diseases. Our proposed technique yielded 0.10–18.5% better results than the latest existing studies. The findings suggest that the recommended system could be used to supplement a physician's diagnosis of liver disease.

Little is known about expertise-related plasticity of neural mechanisms for auditory feature integration. Here, we contrast two diverging hypotheses that musical expertise is associated with more independent or more integrated predictive processing of acoustic features relevant to melody perception. Mismatch negativity (MMNm) was recorded with magnetoencephalography (MEG) from 25 musicians and 25 non-musicians, exposed to interleaved blocks of a complex, melody-like multi-feature paradigm and a simple, oddball control paradigm. In addition to single deviants differing in frequency (F), intensity (I), or perceived location (L), double and triple deviants were included reflecting all possible feature combinations (FI, IL, LF, FIL). Following previous work, early neural processing overlap was approximated in terms of MMNm additivity by comparing empirical MMNms obtained with double and triple deviants to modeled MMNms corresponding to summed constituent single-deviant MMNms. Significantly greater subadditivity was found in musicians compared to non-musicians, specifically for frequencyrelated deviants in complex, melody-like stimuli. Despite using identical sounds, expertise effects were absent from the simple oddball paradigm. This novel finding supports the integrated processing hypothesis whereby musicians recruit overlapping neural resources facilitating more integrative representations of contextually relevant stimuli such as frequency (perceived as pitch) during melody perception. More generally, these specialized refinements in predictive processing may enable experts to optimally capitalize upon complex, domain-relevant, acoustic cues. [ABSTRACT FROM AUTHOR]