GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation

Recently, graph-based and Transformer-based deep learning networks have demonstrated excellent performances on various point cloud tasks. Most of the existing graph methods are based on static graph, which take a fixed input to establish graph relations. Moreover, many graph methods apply maximization and averaging to aggregate neighboring features, so that only a single neighboring point affects the feature of centroid or different neighboring points have the same influence on the centroid's feature, which ignoring the correlation and difference between points. Most Transformer-based methods extract point cloud features based on global attention and lack the feature learning on local neighbors. To solve the problems of these two types of models, we propose a new feature extraction block named Graph Transformer and construct a 3D point point cloud learning network called GTNet to learn features of point clouds on local and global patterns. Graph Transformer integrates the advantages of graph-based and Transformer-based methods, and consists of Local Transformer and Global Transformer modules. Local Transformer uses a dynamic graph to calculate all neighboring point weights by intra-domain cross-attention with dynamically updated graph relations, so that every neighboring point could affect the features of centroid with different weights; Global Transformer enlarges the receptive field of Local Transformer by a global self-attention. In addition, to avoid the disappearance of the gradient caused by the increasing depth of network, we conduct residual connection for centroid features in GTNet; we also adopt the features of centroid and neighbors to generate the local geometric descriptors in Local Transformer to strengthen the local information learning capability of the model. Finally, we use GTNet for shape classification, part segmentation and semantic segmentation tasks in this paper

MyoPS A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/)

Tree-Structured Trajectory Encoding for Vision-and-Language Navigation

Over the past few years, the research on vision-and-language navigation (VLN) has made tremendous progress. Many previous works attempted to improve the performance from different aspects like training strategy, data augmentation, pre-training, etc. This work focuses on a rarely-explored aspect in VLN, namely the trajectory organization and encoding during the navigation. Most of existing state-of-the-art VLN models adopt a vanilla sequential strategy for encoding the trajectories. Such strategy takes the whole trajectory as a single sequence to estimate the current state, no matter whether the agent moved smoothly or perhaps made mistakes and backtracked in the past. We show that the sequential encoding may largely lose this kind of fine-grained structure in the trajectory, which could hamper the later state estimation and decision making. In order to solve this problem, this work proposes a novel tree-structured trajectory encoding strategy. The whole trajectory is organized as a tree rooted from the starting position, and encoded using our Tree-Transformer module to fully extract the fine-grained historical information. Besides, as the spatial topology could be easily embedded in the trajectory tree, we further design a tree-based action space to allow the agent making long-range error-correction in one decision. We implement the holistic agent based on cross-modal transformer and train it with a newly-proposed Tree-nDTW reward. On the benchmark dataset R2R, our model achieves a surpassing success rate (SR) of 68% on val-unseen and 66% on test. We further conduct extensive ablation studies and analyses to provide more insights for the effectiveness our designs

Predictive model of back-wall overpressure behind a cantilever wall

To reduce the hazards posed by blast shock waves, important potential targets are usually shielded by cantilever walls. The main factor that indicates whether there is damage is the back-wall overpressure, and of concern here is predicting the back-wall overpressure behind a rigid cantilever wall. This was measured in full-scale blast experiments using 20 kg of trinitrotoluene (TNT) located on the ground, and the experimental data reveal how the cantilever wall mitigates the shock wave. A 3D numerical model was established to determine how the wall size influences the diffraction of the shock wave and the peaks and attenuation of the back-wall overpressure. Based on the numerical results and dimensional analysis, a model is proposed that provides an effective means of predicting the back-wall overpressure rapidly from the TNT equivalent, the standoff distance, and the height of the wall

CX3CL1 promotes M1 macrophage polarization and osteoclast differentiation through NF-κB signaling pathway in ankylosing spondylitis in vitro

Abstract Background Ankylosing spondylitis (AS) is an autoimmune disease with a genetic correlation and is characterized by inflammation in the axial skeleton and sacroiliac joints. Many AS patients also have inflammatory bowel diseases (IBD), but the underlying causes of intestinal inflammation and osteoporosis in AS are not well understood. CX3CL1, a protein involved in inflammation, has been found to be up-regulated in AS patients and AS-model mice. Methods The authors investigated the effects of CX3CL1 on AS by studying its impact on macrophage polarization, inflammation factors, and osteoclast differentiation. Furthermore, the effects of inhibiting the NF-κB pathway and blocking CX3CL1 were assessed using BAY-117082 and anti-CX3CL1 mAb, respectively. AS model mice were used to evaluate the effects of anti-CX3CL1 mAb on limb thickness, spine rupture, and intestinal tissue damage. Results The authors found that CX3CL1 increased the expression of M1-type macrophage markers and inflammation factors, and promoted osteoclast differentiation. This effect was mediated through the NF-κB signaling pathway. Inhibition of the NF-κB pathway prevented M1-type macrophage polarization, reduced inflammation levels, and inhibited osteoclast differentiation. Injection of anti-CX3CL1 mAb alleviated limb thickness, spine rupture, and intestinal tissue damage in AS model mice by inhibiting M1-type macrophage polarization and reducing intestinal tissue inflammation. Conclusions The study demonstrated that up-regulated CX3CL1 promotes M1-type macrophage polarization and osteoclast differentiation through the NF-κB signaling pathway. Inhibition of this pathway and blocking CX3CL1 can alleviate inflammation and bone destruction in AS. These findings contribute to a better understanding of the pathogenesis of AS and provide a basis for clinical diagnosis and treatment

Experimental Study on the Time-Dependent Characteristics of MLPS Transparent Soil Strength

The time-dependent characteristics of transparent soil strength, composed of magnesium lithium phyllosilicate, is important for applying a thixotropic clay surrogate. The gas injection method was employed to obtain the strength, represented as cracking pressure, which was then correlated to variables including rest time, disturbance time, and recovery time. Three concentrations (3, 4, and 5%) were tested. The results show that the strength was directly proportional to the rest time, recovery time, and concentration while the disturbance time reversed. The calculated limit strengths for 3%, 4%, and 5% transparent soils were 3.831 kPa, 8.849 kPa, and 12.048 kPa, respectively. Experimental data also showed that the residual strength for higher concentration transparent soil was more significant than the lower ones. The elastic property immediately generated partial strength recovery after disturbance, while the viscosity property resulted in a slow recovery stage similar to the rest stage. The strength recovery rate was also sensitive to concentration. Furthermore, the strength with 3%, 4%, and 5% concentrations could regain limit values after sufficient recovery, which were calculated as 4.303 kPa, 8.255 kPa, and 14.884 kPa, respectively

Photoluminescence and Fluorescence Quenching of Graphene Oxide: A Review

In recent decades, photoluminescence (PL) material with excellent optical properties has been a hot topic. Graphene oxide (GO) is an excellent candidate for PL material because of its unique optical properties, compared to pure graphene. The existence of an internal band gap in GO can enrich its optical properties significantly. Therefore, GO has been widely applied in many fields such as material science, biomedicine, anti-counterfeiting, and so on. Over the past decade, GO and quantum dots (GOQDs) have attracted the attention of many researchers as luminescence materials, but their luminescence mechanism is still ambiguous, although some theoretical results have been achieved. In addition, GO and GOQDs have fluorescence quenching properties, which can be used in medical imaging and biosensors. In this review, we outline the recent work on the photoluminescence phenomena and quenching process of GO and GOQDs. First, the PL mechanisms of GO are discussed in depth. Second, the fluorescence quenching mechanism and regulation of GO are introduced. Following that, the applications of PL and fluorescence quenching of GO–including biomedicine, electronic devices, material imaging–are addressed. Finally, future development of PL and fluorescence quenching of GO is proposed, and the challenges exploring the optical properties of GO are summarized