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Due to the subjective nature of people's aesthetic experiences with respect to images, personalized image aesthetics assessment (PIAA), which can simulate the aesthetic experiences of individual users to estimate images, has received extensive attention from researchers in the computational intelligence and computer vision communities. Existing PIAA models are usually built on prior knowledge that directly learns the generic aesthetic results of images from most people or the personalized aesthetic results of images from a large number of individuals. However, the learned prior knowledge ignores the mutual influence of the multiple attributes of images and users in their personalized aesthetic experiences. To this end, this paper proposes a personalized image aesthetics assessment method via multi-attribute interactive reasoning. Different from existing PIAA models, the multi-attribute interaction constructed from both images and users is used as more effective prior knowledge. First, we designed a generic aesthetics extraction module from the perspective of images to obtain the aesthetic score distribution and multiple objective attributes of images rated by most users. Then, we propose a multi-attribute interactive reasoning network from the perspective of users. By interacting multiple subjective attributes of users with multiple objective attributes of images, we fused the obtained multi-attribute interactive features and aesthetic score distribution to predict personalized aesthetic scores. Experimental results on multiple PIAA datasets demonstrated our method outperformed state-of-the-art PIAA methods. [ABSTRACT FROM AUTHOR]

Facial action unit (AU) recognition remains a challenging task, due to the subtlety and non-rigidity of AUs. A typical solution is to localize the correlated regions of each AU. Current works often predefine the region of interest (ROI) of each AU via prior knowledge, or try to capture the ROI only by the supervision of AU recognition during training. However, the predefinition often neglects important regions, while the supervision is insufficient to precisely localize ROIs. In this paper, we propose a novel AU recognition method by prior and adaptive attention. Specifically, we predefine a mask for each AU, in which the locations farther away from the AU centers specified by prior knowledge have lower weights. A learnable parameter is adopted to control the importance of different locations. Then, we element-wise multiply the mask by a learnable attention map, and use the new attention map to extract the AU-related feature, in which AU recognition can supervise the adaptive learning of a new attention map. Experimental results show that our method (i) outperforms the state-of-the-art AU recognition approaches on challenging benchmark datasets, and (ii) can accurately reason the regional attention distribution of each AU by combining the advantages of both the predefinition and the supervision. [ABSTRACT FROM AUTHOR]

The main objective of this study is to present a methodology to model the microstructure and mechanical properties of ZE41-xCa-ySr alloys for integrated optimization calculation of the heat treatment process of gearbox casting. Firstly, the models of microstructure and mechanical properties of ZE41-xCa-ySr alloys (0 ≤ x ≤ 2, 0 ≤ y ≤ 0.2) are developed using an artificial neural network (ANN) and multivariate regression. The dataset for ANN and regression models is generated by investigating the microstructures and mechanical properties of the ZE41-xCa-ySr alloys. The inputs for ANN and regression models are Ca and Sr contents, aging temperature and aging time. The outputs are grain size, ultimate tensile strength, elongation and microhardness. The optimal ANN model is obtained by testing the performance of different network architectures. In addition, multivariate regression models have been built based on the Least Squares method. Secondly, based on SiPESC software, an Integrated Computing Platform is constructed by combining the scripting language with the command line operation of simulation software, realizing the "process—microstructure—property" optimization calculation. Finally, based on the developed regression model, an Integrated Computing Platform batch called MATLAB achieves the heat treatment process optimization based on mechanical property prediction. The optimum aging temperature of the ZE41-0.17Ca-0.2Sr alloy is 322 °C, and the corresponding aging time is 11 h. Furthermore, the optimized results are validated by the ANN model, suggesting that ANN predicted results are in good agreement with optimized results. As a consequence, this work provides a new strategy for the research and development of Mg alloys, contributing to acceleration in the development of magnesium alloys. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) aims to automatically evaluate image perceptual quality by simulating the human visual system, which is an important research topic in the field of image processing and computer vision. Although existing deep-learning-based IQA models have achieved significant success, these IQA models usually require input images with a fixed size, which varies the perceptual quality of images. To this end, this paper proposes an aspect-ratio-embedded Transformer-based image quality assessment method, which can implant the adaptive aspect ratios of input images into the multihead self-attention module of the Swin Transformer. In this way, the proposed IQA model can not only relieve the variety of perceptual quality caused by size changes in input images but also leverage more global content correlations to infer image perceptual quality. Furthermore, to comprehensively capture the impact of low-level and high-level features on image quality, the proposed IQA model combines the output features of multistage Transformer blocks for jointly inferring image quality. Experimental results on multiple IQA databases show that the proposed IQA method is superior to state-of-the-art methods for assessing image technical and aesthetic quality. [ABSTRACT FROM AUTHOR]

In this work, Mg-3Y sheet was prepared by high temperature cross-rolling and subsequent short-term annealing. The effect of annealing on microstructure, texture, mechanical properties, and stretch formability of Mg-3Y sheet was primarily investigated. Micro-nano size coexistence of β-Mg24Y5 phases can be well deformed with matrix. The as-rolled Mg-3Y sheet exhibited a homogeneous deformation microstructure consisting of deformed grains with extensive kink bands and dispersed β-Mg24Y5 phases. A double peak texture character appeared in as-rolled Mg-3Y sheet with a split of the texture peaks of about ±20° tilted to rolling direction. After annealing, the as-annealed Mg-3Y sheet presented complete static recrystallized (SRXed) microstructure consisting of uniform equiaxed grains. The texture orientation distribution was more dispersed and a weakened multiple-peak texture orientation distribution appeared. In addition, the maximum intensity of basal plane decreased from 5.2 to 3.1. The change of texture character was attributed to static recrystallization (SRX) induced by kink bands and grain boundaries. The as-annealed Mg-3Y sheet with high Schmid factor (SF) for basal slip, prismatic slip, pyramidal slip, and pyramidal slip exhibited high ductility (~25.6%). Simultaneously, enhanced activity of basal slip and randomized grain orientation played a significant role in decreasing anisotropy for the as-annealed Mg-3Y sheet, which contributed to the formation of high stretch formability (~6.2 mm) at room temperature. [ABSTRACT FROM AUTHOR]

In this study, an artificial neural network approach and a regression model are adopted to predict the mechanical properties of heat-treated Mg-Zn-RE-Zr-Ca-Sr magnesium alloys. The dataset for artificial neural network (ANN) modeling is generated by investigating the microhardness of heat-treated Mg-Zn-RE-Zr-Ca-Sr alloys using Vickers hardness tests. A back-propagation (BP) neural network is established using experimental data that enable the prediction of mechanical properties as a function of the composition and heat treatment process. The input variables for the BP network model are Ca and Sr contents, ageing temperature and ageing time. The output variable corresponds to the microhardness. The optimal BP network model is acquired by optimizing the number of the hidden layer nodes. The results indicate that a reliable correlation coefficient is above 0.95 for architecture (4-8-1), which has a high level of accuracy for prediction. In addition, a second-order polynomial regression model is developed based on the least squares method. The results of determination coefficients and Fisher's criterion indicate that the regression model is capable of modeling mechanical properties as a function of composition and the ageing process. Furthermore, supplemental experiments are conducted to check the accuracy of the BP model and the regression model, suggesting that the model predictions are well in accordance with experimental results. Therefore, both the BP network and regression models have high accuracy in modeling and predicting mechanical properties of heat-treated Mg-Zn-RE-Zr-Ca-Sr alloys. [ABSTRACT FROM AUTHOR]