The Predictive Role of Materialistic Values on Learning Burnout by Pre-service Teachers: A Parallel Channel Model

This study set out to explore the relationship between materialistic values (MVS), ontological security threat (OST), gratitude, and learning burnout (LB) among pre-service teachers enrolled in the Free Teacher Education program in China. MVS, adolescent student burnout, gratitude, and OST questionnaires were administered to 801 pre-service teachers. Data processing was conducted using IBM SPSS 26.0 and AMOS 24.0. The SPSS macro program Model 4 was used to identify mediating mechanisms. Study findings were as follows: (1) MVS was positively correlated with both OST and LB, but negatively correlated with gratitude. (2) OST was positively correlated with LB, while gratitude was negatively correlated with LB. (3) The impact of MVS on pre-service teachers' LB was simultaneously mediated by OST and gratitude. MVS not only directly predicts pre-service teachers' LB, but also influences LB through the independent mediators of OST and gratitude

Consistency Regularization for Generalizable Source-free Domain Adaptation

Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset, making it applicable in a variety of real-world scenarios. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. This oversight leads to overfitting issues and constrains the model's generalization ability. In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets. Our method leverages soft pseudo-labels generated from weakly augmented images to supervise strongly augmented images, facilitating the model training process and enhancing the generalization ability of the adapted model. To leverage more potentially useful supervision, we present a sampling-based pseudo-label selection strategy, taking samples with severer domain shift into consideration. Moreover, global-oriented calibration methods are introduced to exploit global class distribution and feature cluster information, further improving the adaptation process. Extensive experiments demonstrate our method achieves state-of-the-art performance on several SFDA benchmarks, and exhibits robustness on unseen testing datasets.Comment: Accepted by ICCV 2023 worksho

The influence of gratitude on pre-service teachers’ career goal self-efficacy: Chained intermediary analysis of meaning in life and career calling

ObjectiveThe aim of the study was to explore the relationship among gratitude, meaning in life (MIL), career calling, and career goal self-efficacy (CGSE) of the pre-service teachers in the Free Teacher Education program in China and the internal mechanism of action.MethodsIn this study, gratitude, MIL, career calling, and CGSE questionnaires were used to investigate 801 pre-service teachers. IBM SPSS 25.0 and AMOS 24.0 were used for data processing, and SPSS macro program Model 6 was used for the mediating mechanism.Results(1) Gratitude was positively correlated with MIL and career calling. MIL was positively correlated with career calling. Gratitude, MIL, and career calling were significantly and positively associated with CGSE. (2) Gratitude influences pre-service teachers’ CGSE mainly through the independent intermediary of MIL and career calling, and the chain intermediary of MIL→career calling, three indirect effects.ConclusionGratitude indirectly predicts CGSE of pre-service teachers not only through the independent intermediary of MIL and career calling but also through the chain intermediary of MIL and career calling

Towards Interpretable Video Super-Resolution via Alternating Optimization

In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low-resolution blurry video. Such problem often occurs when recording a fast dynamic event with a low-framerate and low-resolution camera, and the captured video would suffer from three typical issues: i) motion blur occurs due to object/camera motions during exposure time; ii) motion aliasing is unavoidable when the event temporal frequency exceeds the Nyquist limit of temporal sampling; iii) high-frequency details are lost because of the low spatial sampling rate. These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences. To address this, we propose an interpretable STVSR framework by leveraging both model-based and learning-based methods. Specifically, we formulate STVSR as a joint video deblurring, frame interpolation, and super-resolution problem, and solve it as two sub-problems in an alternate way. For the first sub-problem, we derive an interpretable analytical solution and use it as a Fourier data transform layer. Then, we propose a recurrent video enhancement layer for the second sub-problem to further recover high-frequency details. Extensive experiments demonstrate the superiority of our method in terms of quantitative metrics and visual quality.Comment: ECCV 202

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved. In this paper, we attempt to solve this problem from the perspective of network architecture design and training data synthesis. Specifically, for the network architecture design, we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, and then plug it as the main building block into the widely-used image-to-image translation UNet architecture. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and processed camera sensor noises) and resizing, and also involves a random shuffle strategy and a double degradation strategy. Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability. We believe our work can provide useful insights into current denoising research.Comment: Codes: https://github.com/cszn/SCUNe

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

The voltage consistency of hundreds of cells in a proton exchange membrane fuel cell stack significantly influences the stack’s performance and lifetime. Using the physics-based model to estimate the cell voltage consistency is highly challenging due to the massive calculation efforts and the complicated fuel cell designs. In this research, an artificial neural network (ANN) model is developed to efficiently predict the cell voltage distribution and the consistency of a commercial-size fuel cell stack. To balance the computation efficiency and accuracy, a dimension-reduced method is proposed with different output-grouping strategies to optimize the ANN structure based on the experiment test of a 100-cell stack. The model’s training time falls nonlinearly from 16 min to 6 s with the output neuron number decreasing from 100 to 5, while the model can still predict the cell voltage distribution trends. With the proposed model, the stack’s cell voltage distributions could be reproduced with significantly lowered computation time, which is beneficial to evaluate the fuel cell status and optimize the control strategies

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

The voltage consistency of hundreds of cells in a proton exchange membrane fuel cell stack significantly influences the stack’s performance and lifetime. Using the physics-based model to estimate the cell voltage consistency is highly challenging due to the massive calculation efforts and the complicated fuel cell designs. In this research, an artificial neural network (ANN) model is developed to efficiently predict the cell voltage distribution and the consistency of a commercial-size fuel cell stack. To balance the computation efficiency and accuracy, a dimension-reduced method is proposed with different output-grouping strategies to optimize the ANN structure based on the experiment test of a 100-cell stack. The model’s training time falls nonlinearly from 16 min to 6 s with the output neuron number decreasing from 100 to 5, while the model can still predict the cell voltage distribution trends. With the proposed model, the stack’s cell voltage distributions could be reproduced with significantly lowered computation time, which is beneficial to evaluate the fuel cell status and optimize the control strategies

Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping

Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment objects well blended with surrounding environments using sparsely-annotated data for model training. It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning. In this paper, we propose a new WSCOS method to address these two challenges. To tackle the intrinsic similarity challenge, we design a multi-scale feature grouping module that first groups features at different granularities and then aggregates these grouping results. By grouping similar features together, it encourages segmentation coherence, helping obtain complete segmentation results for both single and multiple-object images. For the weak supervision challenge, we utilize the recently-proposed vision foundation model, Segment Anything Model (SAM), and use the provided sparse annotations as prompts to generate segmentation masks, which are used to train the model. To alleviate the impact of low-quality segmentation masks, we further propose a series of strategies, including multi-augmentation result ensemble, entropy-based pixel-level weighting, and entropy-based image-level selection. These strategies help provide more reliable supervision to train the segmentation model. We verify the effectiveness of our method on various WSCOS tasks, and experiments demonstrate that our method achieves state-of-the-art performance on these tasks.Comment: 12 pages, 5 figure

Intolerance of uncertainty fuels preservice teachers’ smartphone dependence through rumination and anxiety during the COVID-19 pandemic: A cross-sectional study

Objectives: We aimed to explore the relationship among intolerance of uncertainty (IU), rumination, anxiety, and smartphone dependence (SPD) in preservice teachers during the COVID-19 pandemic. Methods: Two cross-sectional studies were conducted with Chinese preservice teachers, using questionnaires on IU, rumination, anxiety, and SPD. Data were analyzed using AMOS 24.0 and SPSS 25.0, and the mediating mechanism was tested using the macro program Model 6. Study 1 recruited participants who were forcibly sequestered in a university due to an anti-epidemic policy during the COVID-19 crisis. Study 2 was surveyed online from different universities to replicate and enhance the reliability of Study 1 finding. Results: Study 1 (N = 553, Mage = 20.8 ± 2.3, 30.0% female) and Study 2 (N = 1610, Mage = 21.1 ± 2.1, 51.4% female) both found that IU affected SPD through the independent mediators of rumination and anxiety, as well as the chain mediation of rumination→ anxiety. In Study 1, the indirect effect of IU on SPD was significant through rumination (β = 0.16, 95% CI [0.03, 0.06]), anxiety (β = 0.11, 95% CI [0.03, 0.06]), and the chain mediation (β = 0.02, 95% CI [0.01, 0.04]); in Study 2, the indirect effect of IU on SPD was significant through rumination (β = 0.08, 95% CI [0.05, 0.11]), anxiety (β = 0.10, 95% CI [0.08, 0.13]), and the chain mediation (β = 0.02, 95% CI [0.02, 0.03]). Conclusion: Two cross-sectional studies found that preservice teachers’ SPD is indirectly connected to IU, mediated by rumination and anxiety, and weakly mediated by the chain mediation of rumination and anxiety. Our findings may help educators understand the impact of anti-epidemic policies on preservice teachers and possible inclusive later interventions

Towards Interpretable Video Super-Resolution via Alternating Optimization

In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low-resolution blurry video. Such problem often occurs when recording a fast dynamic event with a low-framerate and low-resolution camera, and the captured video would suffer from three typical issues: i) motion blur occurs due to object/camera motions during exposure time; ii) motion aliasing is unavoidable when the event temporal frequency exceeds the Nyquist limit of temporal sampling; iii) high-frequency details are lost because of the low spatial sampling rate. These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences. To address this, we propose an interpretable STVSR framework by leveraging both model-based and learning-based methods. Specifically, we formulate STVSR as a joint video deblurring, frame interpolation, and super-resolution problem, and solve it as two sub-problems in an alternate way. For the first sub-problem, we derive an interpretable analytical solution and use it as a Fourier data transform layer. Then, we propose a recurrent video enhancement layer for the second sub-problem to further recover high-frequency details. Extensive experiments demonstrate the superiority of our method in terms of quantitative metrics and visual quality.ISSN:0302-9743ISSN:1611-334