Accurate stock return forecasting remains a central challenge in quantitative finance, as it directly informs the construction of portfolios and the management of risk. Although traditional static factor models are widely used, they are limited by manual factor selection and fixed weight assignments, which makes them vulnerable to evolving market conditions and regime shifts. To overcome these limitations, we introduce Market Regime Aware-Augmented Attention GRU (MRA-AGRU), an automated dynamic factor gating framework that adaptively reweights factors in response to market regime signals. By integrating an attention-enhanced GRU network, MRA-AGRU effectively suppresses obsolete or noisy factors while amplifying those most relevant to the prevailing environment, thereby capturing nuanced temporal and cross-factor dependencies. Extensive experiments on the CSI 300 and NASDAQ 100 demonstrate the superior performance of MRA-AGRU, highlighting machine-driven factor modulation's role in improving robustness to structural breaks and reducing bias in factor engineering.

The camera calibration in monocular vision represents the relationship between the pixels’ units which is obtained from a camera and the object in the real world. As an essential procedure, camera calibration calculates the three-dimensional geometric information from the captured two-dimensional images. Therefore, a modified camera calibration method based on polynomial regression is proposed to simplify. In this method, a parameter vector is obtained by pixel coordinates of obstacles and corresponding distance values using polynomial regression. The set of parameter’s vectors can measure the distance between the camera and the ground object in the field of vision under the camera’s posture and position. The experimental results show that the lowest accuracy of this focal length calibration method for measurement is 97.09%, and the average accuracy was 99.02%.

In this paper, a novel triplet network is proposed for face sketch recognition. A spatial pyramid pooling layer is introduced into the network to deal with different sizes of images, and an attention model on the image space is proposed to extract features from the same location in the photo and sketch. Our attention mechanism builds and improves recognition accuracy by searching similar regions of the images, which include abundant information in order to distinguish different persons in photos and sketches. So that the cross-modality differences between photo and sketch images are reduced when they are mapped into a common feature space. Our proposed solution is tested on composite face photo-sketch datasets, including UoM-SGFS and e-PRIP dataset, and achieves better performance than the state-of-the-art result. Especially for Set B in UoM-SGFS dataset, the accuracy is higher than 81%.

Accurate Air Quality Index (AQI) forecasting is essential for public health protection and sustainable urban planning. Existing data-driven forecasting models often provide limited interpretability of pollutant interactions, while conventional Fuzzy Cognitive Map (FCM) learning algorithms may struggle to optimize causal weights effectively. To address these limitations, this study proposes IGWO-FCM, an evolutionary-cognitive framework that integrates an Improved Grey Wolf Optimizer (IGWO) with FCMs for interpretable causal structure learning and AQI forecasting under uncertainty. The framework employs FCMs to represent pollutant interactions and feedback mechanisms, and introduces a hierarchical transformation operator and adaptive learning strategy to enhance causal weight optimization. On a real-world urban AQI dataset comprising 744 hourly records and six pollutants, as well as two public benchmarks, IGWO-FCM achieves the lowest MAE and RMSE among the compared FCM learning algorithms for five of the six pollutants, reducing these errors by up to 29.6% and 40.2%, respectively, relative to the strongest baseline. These results demonstrate the predictive performance of IGWO-FCM and its potential to provide interpretable insights into pollutant interactions for AQI forecasting.