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

The protective effect of serum carotenoids on cardiovascular disease: a cross-sectional study from the general US adult population

BackgroundCardiovascular disease (CVD) has become a key global health issue. Serum carotenoids are associated with CVD, while their effects on different diseases remain unclear. Herein, the relationship between the concentration of serum carotenoid and the CVD risk was investigated using nationwide adult samples obtained from the USA.Materials and methodsData of National Health and Nutrition Examination Survey (NHANES) in 2001–2006 were employed. The association of serum carotenoids (total, lycopene, β-carotene, α-carotene, lutein/zeaxanthin, and β-cryptoxanthin) with CVD was explored by using multivariate logistic, linear and weighted quantile sum (WQS) regression analyses. Eventually, data from 12,424 volunteers were analyzed for this study.ResultsMultivariate model data showed that lutein/zeaxanthin, α-carotene, lycopene, and β-cryptoxanthin were negatively associated with the prevalence of CVD (p < 0.05). In comparison with the first quartile, the fourth quartile was associated with α-carotene ([OR] = 0.61 [0.47–0.79]), β-cryptoxanthin (OR = 0.67 [0.50–0.89]), lutein (OR = 0.69 [0.54–0.86]), and lycopene (OR = 0.53 [0.41–0.67]). WQS analysis revealed that the combination of serum carotenoids had negative correlation with the prevalence of total CVD (OR = 0.88, 95% CI: 0.85–0.92, p < 0.001). Additionally, dose–response analysis demonstrated a negative linear association of hypertension with all the carotenoids involved (p > 0.05 for non-linearity).ConclusionThe concentration of serum carotenoids had negative correlation with the prevalence of CVD, with a more significant negative effect against heart attack and stroke

Status of Muon Collider Research and Development and Future Plans

The status of the research on muon colliders is discussed and plans are outlined for future theoretical and experimental studies. Besides continued work on the parameters of a 3-4 and 0.5 TeV center-of-mass (CoM) energy collider, many studies are now concentrating on a machine near 0.1 TeV (CoM) that could be a factory for the s-channel production of Higgs particles. We discuss the research on the various components in such muon colliders, starting from the proton accelerator needed to generate pions from a heavy-Z target and proceeding through the phase rotation and decay ($\pi \to \mu \nu_{\mu}$) channel, muon cooling, acceleration, storage in a collider ring and the collider detector. We also present theoretical and experimental R & D plans for the next several years that should lead to a better understanding of the design and feasibility issues for all of the components. This report is an update of the progress on the R & D since the Feasibility Study of Muon Colliders presented at the Snowmass'96 Workshop [R. B. Palmer, A. Sessler and A. Tollestrup, Proceedings of the 1996 DPF/DPB Summer Study on High-Energy Physics (Stanford Linear Accelerator Center, Menlo Park, CA, 1997)].Comment: 95 pages, 75 figures. Submitted to Physical Review Special Topics, Accelerators and Beam

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Positive solutions of higher-order Sturm–Liouville boundary value problems with fully nonlinear terms

Abstract In this paper we consider the existence of positive solutions of nth-order Sturm–Liouville boundary value problems with fully nonlinear terms, in which the nonlinear term f involves all of the derivatives u′,…,u(n−1) $u',\ldots, u^{(n-1)}$ of the unknown function u. Such cases are seldom investigated in the literature. We present some inequality conditions guaranteeing the existence of positive solutions. Our inequality conditions allow that f(t,x0,x1,…,xn−1) $f(t, x_{0}, x_{1},\ldots, x_{n-1})$ is superlinear or sublinear growth on x0,x1,…,xn−1 $x_{0}, x_{1},\ldots, x_{n-1}$. Our discussion is based on the fixed point index theory in cones

An Affinity Propagation-Based Clustering Method for the Temporal Dynamics Management of High-Speed Railway Passenger Demand

The number of passengers in a high-speed railway line normally varies significantly by the time periods, such as the peak and nonpeak hours. A reasonable classification of railway operation time intervals is essential for an adaptive adjustment of the train schedule. However, the passenger flow intervals are usually classified manually based on experience, which is subjective and inaccurate. Based on the time samples of actual passenger demand data for 365 days, this paper proposes an affinity propagation (AP) algorithm to automatically classify the passenger flow intervals. Specifically, the AP algorithm first merges time samples into different categories together with the passenger transmit volume of the stations, which are used as descriptive variables. Furthermore, clustering validity indexes, such as Calinski–Harabasz, Hartigan, and In-Group Proportion, are employed to examine the clustering results, and reasonable passenger flow intervals are finally obtained. A case study of the Zhengzhou-Xi’an high-speed railway indicates that our proposed AP algorithm has the best performance. Moreover, based on the passenger flow interval classification results obtained using the AP algorithm, the train operation plan fits the passenger demand better. As a result, the indexes of passenger demand satisfaction rate, average train occupancy rate, and passenger flow rate are improved by 7.6%, 16.7%, and 14.1%, respectively, in 2014. In 2015, the above three indicators are improved by 5.7%, 18.4%, and 14.4%, respectively