Multiscale Global Adaptive Attention Graph Neural Network

Dynamic multiscale graph neural networks have high motion prediction errors due to the low correlation between the internal joints of body parts and the limited perceptual fields. A multiscale global adaptive attention graph neural network for human motion prediction is proposed to reduce motion prediction errors. Firstly, a multi-distance partitioning strategy for dividing skeleton joint is proposed to improve the degree of temporal and spatial correlation of body joint information. Secondly, a global adaptive attention spatial temporal graph convolutional network is designed to dynamically enhance the network??s attention to the spatial temporal joints contributing to a motion in combination with global adaptive attention. Finally, this paper integrates the above two improvements into the graph convolutional neural network gate recurrent unit to enhance the state propagation performance of the decoding network and reduce prediction errors. Experimental results show that the prediction error of the proposed method is decreased on Human 3.6M dataset, CMU Mocap dataset and 3DPW dataset compared with state-of-the-art methods

The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg

Flea beetles (Coleoptera, Chrysomelidae, Galerucinae, Alticini) are a hyperdiverse group of organisms with approximately 9900 species worldwide. In addition to walking as most insects do, nearly all the species of flea beetles have an ability to jump and this ability is commonly understood as one of the key adaptations responsible for its diversity. Our investigation of flea beetle jumping is based on high-speed filming, micro- CT scans and 3D reconstructions, and provides a mechanical description of the jump. We reveal that the flea beetle jumping mechanism is a catapult in nature and is enabled by a small structure in the hind femur called an ‘elastic plate’ which powers the explosive jump and protects other structures from potential injury. The explosive catapult jump of flea beetles involves a unique ‘high-efficiency mechanism’ and ‘positive feedback mechanism’. As this catapult mechanism could inspire the design of bionic jumping limbs, we provide a preliminary design for a robotic jumping leg, which could be a resource for the bionics industry

TransAttention U-Net for Semantic Segmentation of Poppy

This work represents a new attempt to use drone aerial photography to detect illegal cultivation of opium poppy. The key of this task is the precise segmentation of the poppy plant from the captured image. To achieve segmentation mask close to real data, it is necessary to extract target areas according to different morphological characteristics of poppy plant and reduce complex environmental interference. Based on RGB images, poppy plants, weeds, and background regions are separated individually. Firstly, the pixel features of poppy plant are enhanced using a hybrid strategy approach to augment the too-small samples. Secondly, the U-Shape network incorporating the self-attention mechanism is improved to segment the enhanced dataset. In this process, the multi-head self-attention module is enhanced by using relative position encoding to deal with the special morphological characteristics between poppy stem and fruit. The results indicated that the proposed method can segmented out the poppy plant precisely

Temporal Context Modeling Network with Local-Global Complementary Architecture for Temporal Proposal Generation

Temporal Action Proposal Generation (TAPG) is a promising but challenging task with a wide range of practical applications. Although state-of-the-art methods have made significant progress in TAPG, most ignore the impact of the temporal scales of action and lack the exploitation of effective boundary contexts. In this paper, we propose a simple but effective unified framework named Temporal Context Modeling Network (TCMNet) that generates temporal action proposals. TCMNet innovatively uses convolutional filters with different dilation rates to address the temporal scale issue. Specifically, TCMNet contains a BaseNet with dilated convolutions (DBNet), an Action Completeness Module (ACM), and a Temporal Boundary Generator (TBG). The DBNet aims to model temporal information. It handles input video features through different dilated convolutional layers and outputs a feature sequence as the input of ACM and TBG. The ACM aims to evaluate the confidence scores of densely distributed proposals. The TBG is designed to enrich the boundary context of an action instance. The TBG can generate action boundaries with high precision and high recall through a local–global complementary structure. We conduct comprehensive evaluations on two challenging video benchmarks: ActivityNet-1.3 and THUMOS14. Extensive experiments demonstrate the effectiveness of the proposed TCMNet on tasks of temporal action proposal generation and temporal action detection

Temporal Context Modeling Network with Local-Global Complementary Architecture for Temporal Proposal Generation

Temporal Action Proposal Generation (TAPG) is a promising but challenging task with a wide range of practical applications. Although state-of-the-art methods have made significant progress in TAPG, most ignore the impact of the temporal scales of action and lack the exploitation of effective boundary contexts. In this paper, we propose a simple but effective unified framework named Temporal Context Modeling Network (TCMNet) that generates temporal action proposals. TCMNet innovatively uses convolutional filters with different dilation rates to address the temporal scale issue. Specifically, TCMNet contains a BaseNet with dilated convolutions (DBNet), an Action Completeness Module (ACM), and a Temporal Boundary Generator (TBG). The DBNet aims to model temporal information. It handles input video features through different dilated convolutional layers and outputs a feature sequence as the input of ACM and TBG. The ACM aims to evaluate the confidence scores of densely distributed proposals. The TBG is designed to enrich the boundary context of an action instance. The TBG can generate action boundaries with high precision and high recall through a local–global complementary structure. We conduct comprehensive evaluations on two challenging video benchmarks: ActivityNet-1.3 and THUMOS14. Extensive experiments demonstrate the effectiveness of the proposed TCMNet on tasks of temporal action proposal generation and temporal action detection

The Evolution of Land Resource Carrying Capacity in 35 Major Cities in China

With the rapid development of urbanization, it is necessary to understand the evolution of land resource carrying capacity (LRCC), so as to avoid irreversible damage to the land resources system in a specific region. Therefore, this paper aims to study the evolution of LRCC by four carrying status intervals of land resources. LRCC based on an evolutionary perspective can help the government manage land resources dynamically and rationally. This study defines LRCC from a carrier–load perspective and considers a higher or lower LRCC when facing the unbalanced relationship between socio-economic development and the supply capacity of land resources. Then, boxplots are used to investigate the LRCC in 35 major cities in China at different time points from 2012 to 2017. The results indicate that there was an increase in the number of cities with LRCC values in the unbalanced interval, with socio-economic development higher than the supply capacity of land resources. Shijiazhuang, Dalian, Harbin, Fuzhou, Chongqing, Kunming, and Taiyuan had LRCC values leaning towards an unbalanced situation. The main drivers that cause the phenomena mentioned above include policy, socio-economic development, and land use change. This study not only improves the understanding of the relationship between socio-economic development and the supply capacity of land resources and identifies the main drivers, but also provides a basis for control of LRCC according to the identifications of the main driversReal Estate Managemen

A Simple Strategy for the Simultaneous Determination of Dopamine, Uric Acid, L-Tryptophan and Theophylline Based on a Carbon Nano-Onions Modified Electrode

In this work, carbon nano-onions (CNOs) with particle sizes of 5–10 nm were prepared by the multi-potential step method. High-resolution transmission electron microscopy, infrared spectroscopy and Raman spectroscopy characterize the effective synthesis of CNOs. CNOs/GCEs were prepared by depositing the prepared CNOs onto glassy carbon electrodes (GCEs) by a drop-coating method. Examination of the electrocatalytic activity of the CNOs/GCE sensor by simultaneously detecting dopamine (DA), uric acid (UA), L-tryptophan (Trp) and theophylline (TP) using a differential pulse voltammetry technique. The results showed that the linear ranges of DA, UA, Trp and TP were DA 0.01–38.16 μM, UA 0.06–68.16 μM, Trp 1.00–108.25 μM, and TP 8.16–108.25 μM, and the detection limits (S/N = 3) were 0.0039 μM, 0.0087 μM, 0.18 μM and 0.35 μM, respectively. The CNOS/GCE sensor had good stability and could be used for the detection of actual samples

Morphological diversity and altitudinal differentiation of Aethopyga species

Abstract The morphological characteristics of birds are an important tool for studying their adaptation and evolution. The morphological evolution of a clade is not only constrained by the phylogenetic relationship, but also influenced by ecological factors and interspecific competition. Aethopyga is a group of small nectar‐eating birds with obvious sexual dimorphism. They have slender and decurved beaks, which reflect their unique diet and foraging mode. Traditional and geometric morphometrics were combined to characterize the body morphology and beak shape of six species of Aethopyga distributed in China. We aim to assess the roles of phylogeny, altitude, and species interactions to morphological evolution. The main distinguishing characteristic among these six species were overall body size, the ratio of body weight, culmen and tarsal length to body length, tail length and wing length, and beak shape (slender/straight vs. thick/decurved). Although these dimensions cannot distinguish all species, they can show a clear distribution trend, and there is a significant Mahalanobis distance between each pair of species. There were no significant phylogenetic signals in morphological traits. The results of PGLS analysis show that altitude is significantly correlated with log‐transformed tarsus length and beak‐shaped PC1 (slender/straight vs thick/decurved dimensions) across the six species analyzed. Mantel test shows that the distance matrix of beak morphological characteristics showed a significant correlation with the altitudinal distance matrix. The results indicated no significant phylogenetic signal in the morphological characteristics of six species. In terms of beak shape, species with greater overlap in elevation distribution have more similar morphological characteristics, that is, less morphological differentiation

Tohjm-Trained Multiscale Spatial Temporal Graph Convolutional Neural Network for Semi-Supervised Skeletal Action Recognition

In recent years, spatial-temporal graph convolutional networks have played an increasingly important role in skeleton-based human action recognition. However, there are still three major limitations to most ST-GCN-based approaches: (1) They only use a single joint scale to extract action features, or process joint and skeletal information separately. As a result, action features cannot be extracted dynamically through the mutual directivity between the scales. (2) These models treat the contributions of all joints equally in training, which neglects the problem that some joints with difficult loss-reduction are critical joints in network training. (3) These networks rely heavily on a large amount of labeled data, which remains costly. To address these problems, we propose a Tohjm-trained multiscale spatial-temporal graph convolutional neural network for semi-supervised action recognition, which contains three parts: encoder, decoder and classifier. The encoder’s core is a correlated joint–bone–body-part fusion spatial-temporal graph convolutional network that allows the network to learn more stable action features between coarse and fine scales. The decoder uses a self-supervised training method with a motion prediction head, which enables the network to extract action features using unlabeled data so that the network can achieve semi-supervised learning. In addition, the network is also capable of fully supervised learning with the encoder, decoder and classifier. Our proposed time-level online hard joint mining strategy is also used in the decoder training process, which allows the network to focus on hard training joints and improve the overall network performance. Experimental results on the NTU-RGB + D dataset and the Kinetics-skeleton dataset show that the improved model achieves good performance for action recognition based on semi-supervised training, and is also applicable to the fully supervised approach