RFAConv: Innovating Spatital Attention and Standard Convolutional Operation

Spatial attention has been widely used to improve the performance of convolutional neural networks by allowing them to focus on important information. However, it has certain limitations. In this paper, we propose a new perspective on the effectiveness of spatial attention, which is that it can solve the problem of convolutional kernel parameter sharing. Despite this, the information contained in the attention map generated by spatial attention is not sufficient for large-size convolutional kernels. Therefore, we introduce a new attention mechanism called Receptive-Field Attention (RFA). While previous attention mechanisms such as the Convolutional Block Attention Module (CBAM) and Coordinate Attention (CA) only focus on spatial features, they cannot fully address the issue of convolutional kernel parameter sharing. In contrast, RFA not only focuses on the receptive-field spatial feature but also provides effective attention weights for large-size convolutional kernels. The Receptive-Field Attention convolutional operation (RFAConv), developed by RFA, represents a new approach to replace the standard convolution operation. It offers nearly negligible increment of computational cost and parameters, while significantly improving network performance. We conducted a series of experiments on ImageNet-1k, MS COCO, and VOC datasets, which demonstrated the superiority of our approach in various tasks including classification, object detection, and semantic segmentation. Of particular importance, we believe that it is time to shift focus from spatial features to receptive-field spatial features for current spatial attention mechanisms. By doing so, we can further improve network performance and achieve even better results. The code and pre-trained models for the relevant tasks can be found at https://github.com/Liuchen1997/RFAConv.Comment: 14 pages, 5 figure

AKConv: Convolutional Kernel with Arbitrary Sampled Shapes and Arbitrary Number of Parameters

Neural networks based on convolutional operations have achieved remarkable results in the field of deep learning, but there are two inherent flaws in standard convolutional operations. On the one hand, the convolution operation be confined to a local window and cannot capture information from other locations, and its sampled shapes is fixed. On the other hand, the size of the convolutional kernel is fixed to k $\times$ k, which is a fixed square shape, and the number of parameters tends to grow squarely with size. It is obvious that the shape and size of targets are various in different datasets and at different locations. Convolutional kernels with fixed sample shapes and squares do not adapt well to changing targets. In response to the above questions, the Alterable Kernel Convolution (AKConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance. In AKConv, we define initial positions for convolutional kernels of arbitrary size by means of a new coordinate generation algorithm. To adapt to changes for targets, we introduce offsets to adjust the shape of the samples at each position. Moreover, we explore the effect of the neural network by using the AKConv with the same size and different initial sampled shapes. AKConv completes the process of efficient feature extraction by irregular convolutional operations and brings more exploration options for convolutional sampling shapes. Object detection experiments on representative datasets COCO2017, VOC 7+12 and VisDrone-DET2021 fully demonstrate the advantages of AKConv. AKConv can be used as a plug-and-play convolutional operation to replace convolutional operations to improve network performance. The code for the relevant tasks can be found at https://github.com/CV-ZhangXin/AKConv.Comment: 10 pages, 5 figure

Acoustic Vortex in Waveguide with Chiral Gradient Sawtooth Metasurface

The acoustic vortex states with spiral phase dislocation that can carry orbital angular moment (OAM) have aroused many research interests in recent years. The mainstream methods of generating acoustic vortex are based on Huygens-Fresnel principle to modulate the wavefront to create spatial spiral phase dislocation. In this work, we propose an entirely new scenario to generate acoustic vortex in a waveguide with chiral gradient sawtooth metasurface. The physical mechanism of our method is to lift the degenerate dipole eigenmodes through the scattering effect of the chiral surface structure, and then the superposition of them will generate both and order vortices in place. Compared to the existing methods of acoustic vortex production, our design has many merits, such as easy to manufacture and control, the working frequency is broadband, sign of vortex order can be readily flipped. Both the full-wave simulations and experimental measurements validate the existence of the acoustic vortices. The torque effect of the acoustic vortices is also successfully performed by rotating a foam disk as a practical application. Our work opens up a new route for generating acoustic vortex and could have potential significances in microfluidics, acoustic tweezers and ultrasonic communication, etc

Feedback control strategies for a nonholonomic mobile robot using a nonlinear oscillator

Among control problems for mobile robots, point-to-point stabilization is the most challenging since it does not admit designs with smooth static state feedback laws. Stabilization strategies for mobile robots, and nonholonomic systems generally, are smooth, time-varying or nonsmooth, time-invariant. Time-varying control strategies are designed with umdamped linear oscillators but their fixed structure offer limited flexibility in control design. The central theme of this paper lies in use of nonlinear oscillators for mobile robot control. Large numbers of qualitatively different control strategies can be designed using nonlinear oscillators since stiffness and damping can be functions of robot states. We demonstrate by designing two fundamentally differ-ent controllers for two-wheeled mobile robot using two variants of a particular nonlinear oscillator. First controller is dynamic and generates smooth control action. * To whom all correspondence should be addressed

Classification and Object Detection of 360° Omnidirectional Images Based on Continuity-Distortion Processing and Attention Mechanism

The use of 360° omnidirectional images has occurred widely in areas where comprehensive visual information is required due to their large visual field coverage. However, many extant convolutional neural networks based on 360° omnidirectional images have not performed well in computer vision tasks. This occurs because 360° omnidirectional images are processed into plane images by equirectangular projection, which generates discontinuities at the edges and can result in serious distortion. At present, most methods to alleviate these problems are based on multi-projection and resampling, which can result in huge computational overhead. Therefore, a novel edge continuity distortion-aware block (ECDAB) for 360° omnidirectional images is proposed here, which prevents the discontinuity of edges and distortion by recombining and segmenting features. To further improve the performance of the network, a novel convolutional row-column attention block (CRCAB) is also proposed. CRCAB captures row-to-row and column-to-column dependencies to aggregate global information, enabling stronger representation of the extracted features. Moreover, to reduce the memory overhead of CRCAB, we propose an improved convolutional row-column attention block (ICRCAB), which can adjust the number of vectors in the row-column direction. Finally, to verify the effectiveness of the proposed networks, we conducted experiments on both traditional images and 360° omnidirectional image datasets. The experimental results demonstrated that better performance than for the baseline model was obtained by the network using ECDAB or CRCAB

RNA Sequencing and Coexpression Analysis Reveal Key Genes Involved in α-Linolenic Acid Biosynthesis in Perilla frutescens Seed

Perilla frutescen is used as traditional food and medicine in East Asia. Its seeds contain high levels of α-linolenic acid (ALA), which is important for health, but is scarce in our daily meals. Previous reports on RNA-seq of perilla seed had identified fatty acid (FA) and triacylglycerol (TAG) synthesis genes, but the underlying mechanism of ALA biosynthesis and its regulation still need to be further explored. So we conducted Illumina RNA-sequencing in seven temporal developmental stages of perilla seeds. Sequencing generated a total of 127 million clean reads, containing 15.88 Gb of valid data. The de novo assembly of sequence reads yielded 64,156 unigenes with an average length of 777 bp. A total of 39,760 unigenes were annotated and 11,693 unigenes were found to be differentially expressed in all samples. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, 486 unigenes were annotated in the “lipid metabolism” pathway. Of these, 150 unigenes were found to be involved in fatty acid (FA) biosynthesis and triacylglycerol (TAG) assembly in perilla seeds. A coexpression analysis showed that a total of 104 genes were highly coexpressed (r > 0.95). The coexpression network could be divided into two main subnetworks showing over expression in the medium or earlier and late phases, respectively. In order to identify the putative regulatory genes, a transcription factor (TF) analysis was performed. This led to the identification of 45 gene families, mainly including the AP2-EREBP, bHLH, MYB, and NAC families, etc. After coexpression analysis of TFs with highly expression of FAD2 and FAD3 genes, 162 TFs were found to be significantly associated with two FAD genes (r > 0.95). Those TFs were predicted to be the key regulatory factors in ALA biosynthesis in perilla seed. The qRT-PCR analysis also verified the relevance of expression pattern between two FAD genes and partial candidate TFs. Although it has been reported that some TFs are involved in seed development, more direct evidence is still needed to verify their function. However, these findings can provide clues to reveal the possible molecular mechanisms of ALA biosynthesis and its regulation in perilla seed

Effects of Agronomic Measures on Decomposition Characteristics of Wheat and Maize Straw in China

The utilization of crop straw resources has been highly emphasized by governments and academia in recent decades. The growing importance of straw decomposition in the wheat-maize rotation system and the remarkable diversity of accumulated information on this topic inspired us to quantitatively explore variations in the outcomes of individual studies. We conducted a data analysis of 46 experimental studies reporting the effects of agronomic measures on the straw decomposition rates of wheat (14 studies) and maize (38 studies). Statistical results showed that maize straw crushed and buried in soil with turn-over or rotary tillage can significantly increase straw decomposition rates. Further, with the increase in nitrogen input and straw burial depth in the soil, the maize straw decomposition rate increased significantly, while the amount of straw return showed the opposite trend. Among all agronomic measures in this research, burial depth has demonstrated a significant positive effect on the wheat straw decomposition rate. The random forest analysis identified decomposition time as the most important predictor of straw decomposition rates for wheat and maize. In addition, some agronomic measures and straw decomposition time jointly affect the decomposition rate of straw. In general, agronomic measures are effective factors in controlling straw decomposition in a wheat-maize rotation system

Effects of Agronomic Measures on Decomposition Characteristics of Wheat and Maize Straw in China

The utilization of crop straw resources has been highly emphasized by governments and academia in recent decades. The growing importance of straw decomposition in the wheat-maize rotation system and the remarkable diversity of accumulated information on this topic inspired us to quantitatively explore variations in the outcomes of individual studies. We conducted a data analysis of 46 experimental studies reporting the effects of agronomic measures on the straw decomposition rates of wheat (14 studies) and maize (38 studies). Statistical results showed that maize straw crushed and buried in soil with turn-over or rotary tillage can significantly increase straw decomposition rates. Further, with the increase in nitrogen input and straw burial depth in the soil, the maize straw decomposition rate increased significantly, while the amount of straw return showed the opposite trend. Among all agronomic measures in this research, burial depth has demonstrated a significant positive effect on the wheat straw decomposition rate. The random forest analysis identified decomposition time as the most important predictor of straw decomposition rates for wheat and maize. In addition, some agronomic measures and straw decomposition time jointly affect the decomposition rate of straw. In general, agronomic measures are effective factors in controlling straw decomposition in a wheat-maize rotation system