Line digraphs and coreflexive vertex sets

The concept of coreflexive set is introduced to study the structure of digraphs. New characterizations of line digraphs and nth-order line digraphs are given. Coreflexive sets also lead to another natural way of forming an intersection digraph from a given digraph.Comment: 8 pages, 3 figure

Effects of different inlet velocity on the polishing quality of abrasive flow machining

In order to study the effect of different inlet velocity on the polishing quality of abrasive flow machining, this paper takes the variable diameter pipe as an example. The fluid dynamic pressure and total energy of abrasive particles under coupling field with different inlet velocities were carried out by using computational fluid dynamics software. The results of numerical analysis show that the polishing quality becomes better with the increase of the inlet velocity. At the same inlet velocity, the smaller the pipe diameter is, the higher the polishing quality will be. Therefore, the optimum inlet velocity can be selected by numerical simulation according to the size of the aperture of workpiece in the actual processing, which can provide technical support for the production

Learning to Count Isomorphisms with Graph Neural Networks

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational costs. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on an input graph, the space of possible query graphs is enormous, and different parts of the input graph will be triggered to match different queries. Thus, expecting a fixed representation of the input graph to match diversely structured query graphs is unrealistic. In this paper, we propose a novel GNN called Count-GNN for subgraph isomorphism counting, to deal with the above challenges. At the edge level, given that an edge is an atomic unit of encoding graph structures, we propose an edge-centric message passing scheme, where messages on edges are propagated and aggregated based on the edge adjacency to preserve fine-grained structural information. At the graph level, we modulate the input graph representation conditioned on the query, so that the input graph can be adapted to each query individually to improve their matching. Finally, we conduct extensive experiments on a number of benchmark datasets to demonstrate the superior performance of Count-GNN.Comment: AAAI-23 main trac

HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning

Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised framework greatly depends on the availability of task-specific supervision. To reduce the labeling cost, pre-training on self-supervised pretext tasks has become a popular paradigm,but there is often a gap between the pre-trained model and downstream tasks, stemming from the divergence in their objectives. To bridge the gap, prompt learning has risen as a promising direction especially in few-shot settings, without the need to fully fine-tune the pre-trained model. While there has been some early exploration of prompt-based learning on graphs, they primarily deal with homogeneous graphs, ignoring the heterogeneous graphs that are prevalent in downstream applications. In this paper, we propose HGPROMPT, a novel pre-training and prompting framework to unify not only pre-training and downstream tasks but also homogeneous and heterogeneous graphs via a dual-template design. Moreover, we propose dual-prompt in HGPROMPT to assist a downstream task in locating the most relevant prior to bridge the gaps caused by not only feature variations but also heterogeneity differences across tasks. Finally, we thoroughly evaluate and analyze HGPROMPT through extensive experiments on three public datasets.Comment: Accepted by AAAI202

The neutrophil-lymphocyte ratio to predict poor prognosis of critical acute myocardial infarction patients: a retrospective cohort study

IntroductionInflammation is closely related to adverse outcomes of acute myocardial infarction (AMI). This study aimed to evaluate whether neutrophil-lymphocyte ratio (NLR) can predict poor prognosis of critical AMI patients. Materials and methodsWe designed a retrospective cohort study and extracted AMI patients from the “Medical Information Mart for Intensive Care-III” database. The primary outcome was 1-year all-cause mortality. The secondary outcomes were 90-day and in-hospital all-cause mortalities, and acute kidney injury (AKI) incidence. The optimal cut-offs of NLR were picked by X-tile software according to the 1-year mortality and patient groups were created: low-NLR ( 21.1). Cox and modified Poisson regression models were used to evaluate the effect of NLR on outcomes in critically AMI patients. ResultsFinally, 782 critical AMI patients were enrolled in this study, and the 1-year mortality was 32% (249/782). The high- and very high-NLR groups had a higher incidence of outcomes than the low-NLR group (P < 0.05). The multivariate regression analyses found that the high- and very high-NLR groups had a higher risk of 1-year mortality (Hazard ratio (HR) = 1.59, 95% CI: 1.12 to 2.24, P = 0.009 and HR = 1.73, 95% CI: 1.09 to 2.73, P = 0.020), 90-day mortality (HR = 1.69, 95% CI: 1.13 to 2.54, P = 0.011 and HR = 1.90, 95% CI: 1.13 to 3.20, P = 0.016), in-hospital mortality (Relative risk (RR) = 1.77, 95% CI: 1.14 to 2.74, P = 0.010 and RR = 2.10, 95% CI: 1.23 to 3.58, P = 0.007), and AKI incidence (RR = 1.44, 95% CI: 1.06 to 1.95, P = 0.018 and RR = 1.34, 95% CI: 0.87 to 2.07, P = 0.180) compared with low-NLR group. NLR retained stable predictive ability in sensitivity analyses. ConclusionBaseline NLR is an independent risk factor for 1-year mortality, 90-day mortality, in-hospital mortality, and AKI incidence in AMI patients

Human posture recognition based on multiple features and rule learning

The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random sub-Weili Ding space methods to create different samples and features for improved classification of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs