Multi-Agent Game Abstraction via Graph Attention Neural Network

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.Comment: Accepted by AAAI202

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.Comment: Accepted by AAAI202

Transcatheter Versus Surgical Closure of Perimembranous Ventricular Septal Defects in Children A Randomized Controlled Trial

ObjectivesThe objective of this study was to evaluate the safety and efficacy of the surgical versus transcatheter approach to correct perimembranous ventricular septal defects (pmVSDs) in a prospective, randomized, controlled clinical trial.BackgroundpmVSD is a common congenital heart disease in children. Surgical closure of pmVSD is a well-established therapy but requires open-heart surgery with cardiopulmonary bypass. Although the transcatheter approach is associated with significant incidence of complete atrioventricular block, it may provide a less invasive alternative. Critical comparison of the safety and efficacy of the 2 interventions necessitates a prospective, randomized, controlled trial.MethodsBetween January 2009 and July 2010, 229 children with pmVSD were randomly assigned to surgical or transcatheter intervention. Clinical, laboratory, procedural, and follow-up data over a 2-year period were compared.ResultsNeither group had mortality or major complications. However, statistical analysis of the 2 groups demonstrated significant differences (p < 0.001) in minor adverse events (32 vs. 7), quantity of blood transfused, duration of the procedure, median hospital stay, median intensive care unit stay, median hospitalization cost, and median blood loss. During a median follow-up of 2 years, the left ventricular end-diastolic dimension of both groups returned to normal and there was no difference in closure rate, adverse events, and complications between groups.ConclusionsTranscatheter device closure and surgical repair are effective interventions with excellent midterm results for treating pmVSD in children. Transcatheter device closure has a lower incidence of myocardial injury, less blood transfused, faster recovery, shorter hospital stay, and lower medical expenses. (Transcatheter Closure Versus Surgery of Perimembranous Ventricular Septal Defects; NCT00890799

Global existence and optimal decay of solutions to the incompressible Oldroyd-B model with only stress tensor dissipation and without damping mechanism

We study the $d$-dimensional ($d\geq2$) incompressible Oldroyd-B model with only stress tensor diffusion and without velocity dissipation as well as the damping mechanism on the stress tensor. Firstly, based upon some new observations on the model, we develope the pure energy argument (independent of spectral analysis) in general $L^p$ framework, and present a small initial data global existence and uniqueness of solutions to the model. Our results yield that the coupling and interaction of the velocity and the non-Newtonian stress actually enhances the regularity of the system. Later, by adding some additional $L^2$ type conditions on the low frequencies of the initial data $(u_0,\tau_0)$, %but without any more smallness restrictions, we obtain the optimal time-decay rates of the global solution $(u,\tau)$. Our result solves the problem proposed in Wang, Wu, Xu and Zhong \cite{Wang-Wu-Xu-Zhong} ({\it J. Funct. Anal.}, 282 (2022), 109332.).Comment: we improve the result, we will give a new resul

Local and Global Context-Enhanced Lightweight CenterNet for PCB Surface Defect Detection

Printed circuit board (PCB) surface defect detection is an essential part of the PCB manufacturing process. Currently, advanced CCD or CMOS sensors can capture high-resolution PCB images. However, the existing computer vision approaches for PCB surface defect detection require high computing effort, leading to insufficient efficiency. To this end, this article proposes a local and global context-enhanced lightweight CenterNet (LGCL-CenterNet) to detect PCB surface defects in real time. Specifically, we propose a two-branch lightweight vision transformer module with local and global attention, named LGT, as a complement to extract high-dimension features and leverage context-aware local enhancement after the backbone network. In the local branch, we utilize coordinate attention to aggregate more powerful features of PCB defects with different shapes. In the global branch, Bi-Level Routing Attention with pooling is used to capture long-distance pixel interactions with limited computational cost. Furthermore, a Path Aggregation Network (PANet) feature fusion structure is incorporated to mitigate the loss of shallow features caused by the increase in model depth. Then, we design a lightweight prediction head by using depthwise separable convolutions, which further compresses the computational complexity and parameters while maintaining the detection capability of the model. In the experiment, the LGCL-CenterNet increased the [email protected] by 2% and 1.4%, respectively, in comparison to CenterNet-ResNet18 and YOLOv8s. Meanwhile, our approach requires fewer model parameters (0.542M) than existing techniques. The results show that the proposed method improves both detection accuracy and inference speed and indicate that the LGCL-CenterNet has better real-time performance and robustness

Whole Exome Sequencing Identifies Two Novel Mutations in a Patient with UC Associated with PSC and SSA

Background. Patients diagnosed with ulcerative colitis (UC) associated with primary sclerosis cholangitis (PSC) and sessile serrated adenoma (SSA) are rare. The present study aimed to identify the potential causative gene mutation in a patient with UC associated with PSC and SSA. Methods. DNA was extracted from the blood sample and tissue sample of SSA, followed by the whole exome sequencing (WES) analysis. Bioinformatics analysis was utilized to predict the deleteriousness of the identified variants. Multiple sequence alignment and conserved protein domain analyses were performed using online software. Sanger sequencing was used to validate the identified variants. Expression and diagnostic analysis of identified mutated genes was performed in the GSE119600 dataset (peripheral blood samples of PSC and UC) and GSE43841 dataset (tumor samples of SSA). Results. In the present study, a total of 842 single nucleotide variants (SNVs) in 728 genes were identified in the blood sample. Two variants, integrin beta 4 (ITGB4) (c.C2503G; p.P835A) and a mucin 3A (MUC3A) (c.C1019T; p.P340L), were further analyzed. MUC3A was associated with inflammatory bowel disease. Sanger sequence in blood revealed that the ITGB4 mutation was fully cosegregated with the result of WES in the patient. Additionally, a variant, tumor protein p53 gene (TP53) (c.86delA; p.N29Tfs∗15) was identified in the tissue sample of SSA. Compared to that in normal controls, ITGB4 was upregulated in both UC and PSC, MUC3A was, respectively, upregulated and downregulated in PSC and UC, and TP53 was downregulated in SSA. ITGB4 and TP53 had a potential diagnostic value for UC, PSC and SSA. Conclusions. The present study demonstrated that the ITGB4 (c.C2503G; p.P835A) and MUC3A (c.C1019T; p.P340L) mutations may be the potential causative variants in a patient with UC associated with PSC and SSA. TP53 (c.86delA; p.N29Tfs∗15) mutation may be associated with SSA in this patient