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Sex differences in brain activity evoked by sexual stimuli remain elusive despite robust evidence for stronger enjoyment of and interest toward sexual stimuli in men than in women. To test whether visual sexual stimuli evoke different brain activity patterns in men and women, we measured hemodynamic brain activity induced by visual sexual stimuli in two experiments with 91 subjects (46 males). In one experiment, the subjects viewed sexual and nonsexual film clips, and dynamic annotations for nudity in the clips were used to predict hemodynamic activity. In the second experiment, the subjects viewed sexual and nonsexual pictures in an event‐related design. Men showed stronger activation than women in the visual and prefrontal cortices and dorsal attention network in both experiments. Furthermore, using multivariate pattern classification we could accurately predict the sex of the subject on the basis of the brain activity elicited by the sexual stimuli. The classification generalized across the experiments indicating that the sex differences were task‐independent. Eye tracking data obtained from an independent sample of subjects (N = 110) showed that men looked longer than women at the chest area of the nude female actors in the film clips. These results indicate that visual sexual stimuli evoke discernible brain activity patterns in men and women which may reflect stronger attentional engagement with sexual stimuli in men. To test whether visual sexual stimuli evoke different brain activity patterns in men and women, we measured hemodynamic brain activity induced by visual sexual stimuli in two experiments with 91 subjects (46 males). Males showed stronger activation than females in the visual and prefrontal cortices and dorsal attention network for both erotic movies and still pictures. Furthermore, using multivariate pattern classification we could accurately predict the sex of the subject on the basis of the brain activity elicited by the sexual stimuli.

Defect clusters on the wafer map can provide important clue to identify the process failures so that it is important to accurately classify the defect patterns into corresponding pattern types. In this research, we present an image-based wafer map defect pattern classification method. The presented method consists of two main steps: without any specific preprocessing, high-level features are extracted from convolutional neural network and then the extracted features are fed to combination of error-correcting output codes and support vector machines for wafer map defect pattern classification. To the best of our knowledge, no prior work has applied the presented method for wafer map defect pattern classification. Experimental results tested on 20,000 wafer maps show the superiority of presented method and the overall classification accuracy is up to 98.43%.

The microphysical evolution of the East Asian summer monsoon precipitation during its northward advance across China remains unclear, due to the mixing of diverse weather systems in past studies. Applying objective synoptic classification to a decade of satellite observations, we isolate canonical monsoon‐type heavy precipitation across South, East, and North China. We find its microphysics are highly uniform to first order, consistently exhibiting maritime‐like high concentrations of small‐to‐medium raindrops through dominated warm‐rain accretion process. This uniformity arises from a consistent synoptic environment of deep moisture transport. In contrast, non‐monsoon systems (e.g., cold troughs which comprise >50% of heavy precipitation in North China) favor ice‐phase processes and produce larger raindrops. Merging these regimes biases domain‐wide statistics, explaining prior reports of regional disparity. Our findings underscore the necessity of synoptic pattern classification to accurately characterize monsoon precipitation microphysics and to improve the capacity of region‐specific quantitative precipitation estimation and modeling.

Decreasing the number of matching comparisons between presented fingerprints and their respective templates in automated fingerprint identification systems (AFIS) is salient, especially when dealing with large databases. Fingerprint classification abets the achievement of this goal by stratifying fingerprints into their respective pattern profiles. However, the significant inter-class variation among patterns and minor intra-class variations among fingerprint patterns belonging to a similar class remains a obstacle. Unlike the verification process of fingerprints that requires 1:1 matching of templates, the identification process of fingerprint patterns requires 1:N matching to attest the presence of fingerprint in the database, which leads to a higher number of comparisons. Motivated by this problem, we employed the use of deep transfer learning and data augmentation to develop a fingerprint pattren clasifier to clasify six fingerprint patterns. Three separate models were birth from the utilization of the VGG16, VGG19, and DenseNet121 pre-trained models following some preliminary experiment. Results from the implementation of the proposed deep transfer learning with some data augmentation schemes on the selected VGG16, VGG19, and DenseNet121 pre-trained models manifested classification accuracy of 98.2%, 97%, and 97.8%, respectively, as compared to the 93.9%, 93.7% and 92% rendered by the same models devoid of data augmentation. Hence, experimental results proved that data augmentation improves the efficacy of fingerprint pattern classifier models.

This study developed a gait pattern classification system based on ground contact forces measured by six force sensors embedded inside the shoe sole. The data transmission is facilitated via the Bluetooth module integrated into an STM32 microcontroller. The extreme gradient boosting (XGBoost) algorithm is used to identify the gait patterns, and the basic idea of XGBoost is to use second-order derivatives to make the loss function more precise, incorporate regularization to prevent tree overfitting, and enable block storage for parallel computation. By optimizing the XGBoost algorithm with four algorithms, the exploration capabilities of these algorithms are effectively incorporated into the fusion model. Experimental results indicate that the XGBoost algrithm optimized by Harris' hawks optimization (HHO) outperforms the other optimization algorithms. Specifically, the HHO-XGBoost achieved high values of 97.41%, 97.03%, and 97.22% severally in the metrics of precision, recall, and F1 score. This research illustrates the HHO-XGBoost method's superiority in gait phase recognition.

Wafer map defect pattern classification supports quality monitoring in semiconductor manufacturing, but public benchmark datasets such as WM-811K exhibit extreme class imbalance, where majority classes can dominate standard metrics. This study aims to improve minority class performance while maintaining inference efficiency. Building on an iFormer-based hybrid backbone, we propose the Pattern-Selective Sequential Hybrid Network (PSS-HNet), which redesigns attention blocks to sequentially integrate local interaction (Modulated Convolution) and global interaction (Modulated Axial Attention) and applies sigmoid-based gating to control contextual information injection. Experiments on WM-811K (9 classes) compare iFormer (baseline), Axial-only, Axial+Modulation, and PSS-HNet using macro-averaged metrics as primary indicators, along with class-wise analysis and efficiency evaluation. PSS-HNet improves Macro-Recall by 1.02 percentage points (from 0.8852 to 0.8954) and Macro-F1 by 0.54 percentage points (from 0.9044 to 0.9098) over the baseline while maintaining similar accuracy. It also reduces computational cost and inference latency to 0.754 G FLOPs, 4.381 M parameters, and 7.682 ms, compared with 1.103 G FLOPs, 6.245 M parameters, and 8.666 ms for the baseline. Overall, selective sequential local–global integration provides a favorable balance between minority class performance and efficiency.

Defect pattern analysis of wafer bin maps (WBMs) is an important means of identifying process problems. Recently, automated analysis methods using machine learning or deep learning have been studied as alternatives to manual classification by engineers. In this paper, we propose a method to improve the feature extraction performance of defect patterns by transforming the polar coordinate system instead of the existing WBM image input. To reduce the variability of the location representation, defect patterns in the Cartesian coordinate system, where the location of the distributed defect die is not constant, were converted to a polar coordinate system. The CNN classifier, which uses polar coordinate transformed input, achieved a classification accuracy of 91.3%, which is 4.8% better than the existing WBM image-based CNN classifier. Additionally, a tree-structured classifier model that sequentially connects binary classifiers achieved a classification accuracy of 94%. The method proposed in this paper is also applicable to the defect pattern classification of WBMs consisting of different die sizes than the training data. Finally, the paper proposes an automated pattern classification method that uses individual classifiers to learn defect types and then applies ensemble techniques for multiple defect pattern classification. This method is expected to reduce labor, time, and cost and enable objective labeling instead of relying on subjective judgments of engineers.

The frequent occurrence of typhoons causes geological disasters, such as debris flow and landslide, by bringing extreme rainfall events. Due to the lack of data collection on extreme rainfall events caused by typhoons, the relationship between rainfall patterns and debris flow has not been deeply studied. Therefore, based on hourly rainfall data during typhoons in Wenzhou from 1980 to 2017, this study used a variety of methods to classify the rainfall events and analyze the characteristics of typhoon-induced rainfall events and their impacts on the probability of debris-flow occurrence. Three classification techniques, including dynamic time warping, K-Means cluster, and self-organizing maps, are applied with two ways to normalize rainfall records, including dimensionless rainfall density curves and dimensionless rainfall cumulation curves, for extracting rainfall patterns from recorded 1 h rainfall data. The rainfall patterns are then used for the estimation of typhoon-induced debris-flow occurrence probability. Results show that different methods present different rainfall patterns. The probability of debris flows varies with different patterns of rainfall events. The research results help deepen the understanding of typhoon rainfall events and debris-flow disaster prevention in the region and contribute to regional flood control and disaster reduction.

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.

Human measures many things consciously or subconsciously by touching and sensing without any measurable challenges. However, measuring intangible features like human behavior is a challenging task. Human behavior analysis is an important computer vision technique with a lot of attention which includes human–computer interaction and assisted living. It is essential not only for a wide range of applications but also to understand the abnormal behavior of humans. This survey paper reviews various methods used for the analysis of human behavior by dermatoglyphics fingerprint pattern classifications. This survey studies 50 research papers based on fingerprint pattern classification and presents techniques related overviews such as deep learning (DL)-based methods, convolutional neural network (CNN)-based DL methods, machine learning (ML)-based methods, Support vector machine (SVM)-based ML methods, and optimization methods. The overview of this survey comprised of classification of research methods, year of publication, evaluation metrics, employed datasets, and toolsets for human behavior analysis. The analysis demonstrates that accuracy is the most commonly used evaluation parameter in fingerprint classification which is used by 33 research papers. Finally, the research gap of analyzed methods is explained, which encourages researchers to develop new effective methods for human behavior analysis using fingerprint pattern classification.