The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named
Point Cloud Transformer
(PCT) for ...point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.
Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this ...aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository
https://github.com/MenghaoGuo/Awesome-Vision-Attentions
is dedicated to collecting related work. We also suggest future directions for attention mechanism research.
Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks. Self-attention updates the feature at each position by ...computing a weighted sum of features using pair-wise affinities across all positions to capture the long-range dependency within a single sample. However, self-attention has quadratic complexity and ignores potential correlation between different samples. This article proposes a novel attention mechanism which we call external attention , based on two external, small, learnable, shared memories, which can be implemented easily by simply using two cascaded linear layers and two normalization layers; it conveniently replaces self-attention in existing popular architectures. External attention has linear complexity and implicitly considers the correlations between all data samples. We further incorporate the multi-head mechanism into external attention to provide an all-MLP architecture, external attention MLP (EAMLP), for image classification. Extensive experiments on image classification, object detection, semantic segmentation, instance segmentation, image generation, and point cloud analysis reveal that our method provides results comparable or superior to the self-attention mechanism and some of its variants, with much lower computational and memory costs.
Chronic stress refers to the non-specific systemic reaction that occurs when the body is stimulated by various internal and external negative factors over a long time. The physiological response to ...chronic stress exposure has long been recognized as a potent modulator in the occurrence of atherosclerosis. Furthermore, research has confirmed the correlation between atherosclerosis and cardiovascular events. Chronic stress is pervasive during negative life events and may lead to the formation of plaque. Several epidemiological studies have shown that chronic stress is an independent risk factor for the development of vascular disease and for increased morbidity and mortality in patients with pre-existing coronary artery disease. One possible mechanism for this process is that chronic stress causes endothelial injury, directly activating macrophages, promoting foam cell formation and generating the formation of atherosclerotic plaque. This mechanism involves numerous variables, including inflammation, signal pathways, lipid metabolism and endothelial function. The mechanism of chronic stress in atherosclerosis should be further investigated to provide a theoretical basis for efforts to eliminate the effect of chronic stress on the cardiocerebral vascular system.
Developing therapeutics against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be guided by the distribution of epitopes, not only on the receptor binding domain (RBD) of the ...Spike (S) protein but also across the full Spike (S) protein. We isolated and characterized monoclonal antibodies (mAbs) from 10 convalescent COVID-19 patients. Three mAbs showed neutralizing activities against authentic SARS-CoV-2. One mAb, named 4A8, exhibits high neutralization potency against both authentic and pseudotyped SARS-CoV-2 but does not bind the RBD. We defined the epitope of 4A8 as the N-terminal domain (NTD) of the S protein by determining with cryo-eletron microscopy its structure in complex with the S protein to an overall resolution of 3.1 angstroms and local resolution of 3.3 angstroms for the 4A8-NTD interface. This points to the NTD as a promising target for therapeutic mAbs against COVID-19.
Although the electrochemical catalytic conversion process is effective in increasing the reversible capacity of lithium‐ion batteries, the low contact efficiency between metal catalyst and substrate ...and pulverization of the solid electrolyte interface (SEI) film without protection are not beneficial for the electrochemical reactions. Herein, Fe7S8 nanoparticles are confined by both reduced graphene oxide (RGO) and in‐situ‐formed amorphous carbon (C) to form dual‐carbon‐confined Fe7S8 as a lithium‐ion anode. The dual‐carbon‐confined structure provides a confined space to prevent pulverization of the SEI film and increases the local concentration of intermediate phases, which could be electrocatalytically decomposed by Fe nanoparticles formed in situ to increase the reversibility of the electrochemical reactions and gain high reversible capacity. In addition, the dual‐carbon‐confined structure ensures fast transfer of electrons and boosts transport of lithium ions due to the highly conductive dual‐carbon shell. Thus, the Fe7S8/C/RGO anode delivers an excellent rate performance and long cycling stability. At current densities of 2000 and 5000 mA g−1, the reversible capacities are 520 mA h g−1 over 1500 cycles and 294 mA h g−1 over 2000 cycles, respectively.
Dual confinement: Fe7S8 nanoparticles dually confined by both reduced graphene oxide (RGO) and in‐situ‐formed amorphous carbon (C) were fabricated as Li‐ion battery anodes (see figure). The dual‐carbon‐confined structure prevents pulverization of the solid electrolyte interface film, increases the local concentration of intermediate phases, makes the electrochemical catalytic conversion reaction occur easily, and ensures fast transfer of electrons and increased transport of lithium ions.
While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three ...challenges for applying self-attention in computer vision: (1) treating images as 1D sequences neglects their 2D structures; (2) the quadratic complexity is too expensive for high-resolution images; (3) it only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings. Furthermore, we present a neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN achieves comparable results with similar size convolutional neural networks (CNNs) and vision transformers (ViTs) in various tasks, including image classification, object detection, semantic segmentation, panoptic segmentation, pose estimation, etc. For example, VAN-B6 achieves 87.8% accuracy on ImageNet benchmark, and sets new state-of-the-art performance (58.2 PQ) for panoptic segmentation. Besides, VAN-B2 surpasses Swin-T 4 mIoU (50.1 vs. 46.1) for semantic segmentation on ADE20K benchmark, 2.6 AP (48.8 vs. 46.2) for object detection on COCO dataset. It provides a novel method and a simple yet strong baseline for the community. The code is available at
https://github.com/Visual-Attention-Network
.
As an alternative for polymethyl methacrylate, poly (propylene fumarate) (PPF) has been considered as injectable and biodegradable bone cement; therefore, its mechanical and degradation properties ...are particularly important. As an environmentally friendly PPF‐based copolymer, which is reinforced by hydroxyapatite, was studied for the properties of the composite. The composites were investigated by evaluating their mechanical properties, hydrophilicity, in vitro degradability, and cytocompatibility. Furthermore, via applying MC3T3‐E1 cells, their cell interaction, including adhesion, proliferation, and in vitro cytotoxicity assay, were assessed. The composite reinforced with 3 wt% hydroxyapatite showed a higher lap‐shear strength (0.71 MPa) and bonding strength (5.24 MPa), a maximum compression strength at fracture (92.44 MPa), and compression strength at yield (35.44 MPa), respectively. Also, mechanical strength, hydrophilicity, and in vitro degradation of composites were enhanced in the presence of hydroxyapatite. In this condition, after a period of immersion (52 weeks) in PBS, the weight loss and degradation rate of composites were found to increased. Cell culture tests indicated that the composite was nontoxic and meaningfully facilitated cell attachment and proliferation. Accordingly, controllable strength and degradation of the composite, along with its proven biocompatibility, make the composite a candidate for the treatment of comminuted fractures.
Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close ...relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopantomograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.
•An automatic sex estimation model based on the CNN and a large sample of OPGs has a better effect than other algorithms.•The model has better performance for adults from Han population in northern China, with an accuracy of 90.97%.•The model could also provide a reference for sex estimation of children and adolescents, with an accuracy of 82.64%.
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