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Monocular depth estimation plays an important role in autonomous driving, virtual reality, augmented reality, and other fields. Self-supervised monocular depth estimation has received much attention because it does not require hard-to-obtain depth labels during training. The previously used convolutional neural network (CNN) has shown limitations in modeling large-scale spatial dependencies. A new idea for monocular depth estimation is replacing the CNN architecture or merging it with a Vision Transformer (ViT) architecture that can model large-scale spatial dependencies in images. However, there are still problems with too many parameters and calculations, making deployment difficult on mobile platforms. In response to these problems, we propose TinyDepth, a lightweight self-supervised monocular depth estimation method based on Transformer that employs hierarchical representation learning suitable for dense prediction, uses mobile convolution to reduce parameters and computational overhead. and includes a novel decoder based on multi-scale fusion attention that improves the local and global inference capability of the network through scale-wise attention processing and layer-wise fusion sampling for more accurate depth prediction. In experiments, TinyDepth achieved state-of-the-art results with few parameters on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) dataset, and exhibited good generalization ability on the challenging indoor New York University (NYU) dataset. Source code is available at https://github.com/ZYCheng777/TinyDepth. [ABSTRACT FROM AUTHOR]

Attention mechanisms have gradually become necessary to enhance the representational power of convolutional neural networks (CNNs). Despite recent progress in attention mechanism research, some open problems still exist. Most existing methods ignore modeling multi-scale feature representations, structural information, and long-range channel dependencies, which are essential for delivering more discriminative attention maps. This study proposes a novel, low-overhead, high-performance attention mechanism with strong generalization ability for various networks and datasets. This mechanism is called Multi-Scale Spatial Pyramid Attention (MSPA) and can be used to solve the limitations of other attention methods. For the critical components of MSPA, we not only develop the Hierarchical-Phantom Convolution (HPC) module, which can extract multi-scale spatial information at a more granular level utilizing hierarchical residual-like connections, but also design the Spatial Pyramid Recalibration (SPR) module, which can integrate structural regularization and structural information in an adaptive combination mechanism, while employing the Softmax operation to build long-range channel dependencies. The proposed MSPA is a powerful tool that can be conveniently embedded into various CNNs as a plug-and-play component. Correspondingly, using MSPA to replace the 3 × 3 convolution in the bottleneck residual blocks of ResNets, we created a series of simple and efficient backbones named MSPANet, which naturally inherit the advantages of MSPA. Without bells and whistles, our method substantially outperforms other state-of-the-art counterparts in all evaluation metrics based on extensive experimental results from CIFAR-100 and ImageNet-1K image recognition. When applying MSPA to ResNet-50, our model achieves top-1 classification accuracy of 81.74% and 78.40% on the CIFAR-100 and ImageNet-1K benchmarks, exceeding the corresponding baselines by 3.95% and 2.27%, respectively. We also obtained promising performance improvements of 1.15% and 0.91% compared to the competitive EPSANet-50. In addition, empirical research results in autonomous driving engineering applications also demonstrate that our method can significantly improve the accuracy and real-time performance of image recognition with cheaper overhead. Our code is publicly available at https://github.com/ndsclark/MSPANet. [ABSTRACT FROM AUTHOR]

A broad range of prior research has demonstrated that attention mechanisms offer great potential in advancing the performance of deep convolutional neural networks (CNNs). However, most existing approaches either ignore modeling attention in both channel and spatial dimensions or introduce higher model complexity and heavier computational burden. To alleviate this dilemma, in this paper, we propose a lightweight and efficient multidimensional collaborative attention, MCA, a novel method for simultaneously inferring attention in channel, height, and width dimensions with almost free additional overhead by using a three-branch architecture. For the essential components of MCA, we not only develop an adaptive combination mechanism for merging dual cross-dimension feature responses in squeeze transformation , enhancing the informativeness and discriminability of feature descriptors but also design a gating mechanism in excitation transformation that adaptively determines the coverage of interaction to capture local feature interactions, overcoming the paradox of performance and computational overhead trade-off. Our MCA is simple yet general and can be easily plugged into various classic CNNs as a plug-and-play module and trained along with the vanilla networks in an end-to-end manner. Extensive experimental results for image recognition on CIFAR and ImageNet-1K datasets demonstrate the superiority of our method over other state-of-the-art (SOTA) counterparts. In addition, we also provide insight into the practical benefits of MCA by visually inspecting the GradCAM++ visualization results. The code is available at https://github.com/ndsclark/MCANet. [ABSTRACT FROM AUTHOR]

Understanding the components that shape the rhizosphere community is vital for sustainable disease management. This study evaluated an integrated Verticillium wilt management in eggplant and its influence on the soil microbiome. Six treatments; Self-rooted (control; CLA) and sole grafted (CLB) eggplants, Brassica + Self-rooted plant (BrA), and Brassica + Grafted plants (BrB), with Biochar (10 t/ha) + Brassica + Self-rooted plant (BBrA) and Biochar (10 t/ha) + Brassica + Grafted Plant (BBrB) were used. Soil microbiome was characterized using high-throughput sequencing. The grafted treatments significantly reduced the Verticillium abundance, disease index and improved the yield of eggplant compared with CLA (18.13 t/ha), with BBrB (41.54 t/ha) as the best treatment. Results showed that treatments CLB, BrB, and BBrB stimulated more beneficial microbes, especially Arthrobacter, Bacillus, and Sphingomonas for bacteria; and Mortierella, Tausonia, and Chaetomium for fungi. Treatment BBrB was biomarked by phylum Chloroflexi (o_SBR1031) , Acidobacteria, Planctomycetes, and Patescibacteria, but only Chloroflexi (o_SBR1031) was found in BrB, and none of them in CLB and treatment BBrB also contained more biomarkers than other treatments. Similarly, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis revealed that treatment BBrB contained more genes (17.5%) regulating disease resistance followed by BrB (17.3%) and CLB (16.6%) treatments. In conclusion, grafting with Brassica (biofumigation) under a biochar regime could reduce eggplant Verticillium wilt. This study expands knowledge on how soil microbiota can be enhanced using integrated disease management practices to exploit sustainable food production. [Display omitted] • Suppression of Verticillium wilt disease with different control methods was studied. • The combination of grafting, biochar, and brassica manuring significantly increased eggplant yield. • The genera Bacillus, Sphingomonas, and Arthrobacter increased in the rhizosphere with grafting. • Integrated control methods may be required for the long-term management of Verticillium wilt disease. [ABSTRACT FROM AUTHOR]

Student performance prediction is a critical task in supporting decision-making for Intelligent Tutoring Systems (ITS). Correct predictions of student performance are prerequisites for ITS to supply intelligent services and optimize learning efficiency, e.g., recommending the most appropriate learning resources for each student. Existing methods mainly include cognitive diagnosis and knowledge tracing, both of which focus on students' cognitive modeling based on their interactions on a sequence of items and give predictions by assessing if their cognitive states can meet the item requirements. Specifically, cognitive diagnosis only considers students' global static cognitive states, while knowledge tracing focuses on students' local dynamics. However, both global and local features are critical for predicting student performance. Therefore, in this paper, we propose a novel G lobal and L ocal N eural C ognitive (GLNC) model to capture both global and local features in student-item interactions for more accurate predictions. Specifically, we first learn students' global cognitive level according to all student-item interactions. Then, we propose a self-attentive encoder based on the scaled dot-product attention mechanism to extract the local cognitive dynamics and the dependencies between students' recent interactions. Finally, to obtain better predictions, we design a fused gate based on the similarity between students' recently responded items and the item to be predicted to adaptively combine the global and local features. To evaluate the effectiveness of GLNC, we compare it with both cognitive diagnosis and knowledge tracing methods. All experiments are conducted on three public datasets that contain real student-item interactions on mathematics or language courses from various ITS. The experimental results demonstrate that GLNC achieves an average score of 0.7810 on the AUC metric, 0.7627 on the ACC metric, 0.3987 on the RMSE metric, 0.2023 on the r 2 metric, respectively achieving an average improvement of 1.84%, 1.07%, 1.87%, and 11.38% in contrast to existing state-of-the-art methods (i.e., NCD and LPKT). Moreover, we further analyze the performance of GLNC under different probabilities of guessing and slipping, the results indicate that GLNC is more robust against the influence of noisy data while considering both global and local features. Benefiting from the superior accuracy and stability, our proposed GLNC has a wide range of potential implications for ITS, which can be easily applied to improve students' learning efficiency and experience. • Cognitive diagnosis focuses on students' global static cognitive states. • Knowledge tracing measures students' local dynamics in learning. • It is critical to make trade-offs between global and local features. • Our method achieves more precise and robust results on performance prediction. [ABSTRACT FROM AUTHOR]