Abstract Simulation-based compositional abstraction effectively mitigates state space explosion in model checking, particularly for timed systems. However, existing approaches do not support broadcast synchronization, an important mechanism for modeling non-blocking one-to-many communication in multi-component systems. Consequently, they also lack a parallel composition operator that simultaneously supports broadcast synchronization, binary synchronization, shared variables, and committed locations. To address this, we propose a simulation-based compositional abstraction framework for timed systems, which supports these modeling concepts and is compatible with the popular UPPAAL model checker. Our framework is general, with the only additional restriction being that the timed automata are prohibited from updating shared variables when receiving broadcast signals. Through two case studies, our framework demonstrates superior verification efficiency compared to traditional monolithic methods.

Abstract Compositional verification, such as the technique of assume-guarantee reasoning (AGR), is to verify a property of a system from the properties of its components. It is essential to address the state explosion problem associated with model checking. However, obtaining the appropriate assumption for AGR is always a highly mental challenge, especially in the case of timed systems. In this paper, we propose a learning-based compositional verification framework for deterministic timed automata. In this framework, a modified learning algorithm is used to automatically construct the assumption in the form of a deterministic one-clock timed automaton, and an effective scheme is implemented to obtain the clock reset information for the assumption learning. We prove the correctness and termination of the framework and present two kinds of improvements to speed up the verification. We discuss the results of our experiments to evaluate the scalability and effectiveness of the framework. The results show that the framework we propose can reduce state space effectively, and it outperforms traditional monolithic model checking for most cases.

Researches on analyzing graphs with Graph Neural Networks (GNNs) have been receiving more and more attention because of the great expressive power of graphs. GNNs map the adjacency matrix and node features to node representations by message passing through edges on each convolution layer. However, the message passed through GNNs is not always beneficial for all parts in a graph. Specifically, as the data distribution is different over the graph, the receptive field (the farthest nodes that a node can obtain information from) needed to gather information is also different. Existing GNNs treat all parts of the graph uniformly, which makes it difficult to adaptively pass the most informative message for each unique part. To solve this problem, we propose two regularization terms that consider message passing locally: (1) Intra-Energy Reg and (2) Inter-Energy Reg. Through experiments and theoretical discussion, we first show that the speed of smoothing of different parts varies enormously and the topology of each part affects the way of smoothing. With Intra-Energy Reg, we strengthen the message passing within each part, which is beneficial for getting more useful information. With Inter-Energy Reg, we improve the ability of GNNs to distinguish different nodes. With the proposed two regularization terms, GNNs are able to filter the most useful information adaptively, learn more robustly and gain higher expressiveness. Moreover, the proposed LEReg can be easily applied to other GNN models with plug-and-play characteristics. Extensive experiments on several benchmarks verify that GNNs with LEReg outperform or match the state-of-the-art methods. The effectiveness and efficiency are also empirically visualized with elaborate experiments.

The abundant information in graphs helps us to learn more expressive node representations. Different nodes in the neighborhood have different importance to the central node. Thus, average weight aggregation in most Graph Neural Networks would fail to model such difference. GAT-based models introduce the attention mechanism to solve this problem, but they ignore the rich structural information and may suffer from the problem of over-smoothing. In this paper, we propose Graph Neural Networks with STructural Adaptive Receptive fields (STAR-GNN), which adaptively construct a receptive field for each node with structural information and further achieve better aggregation of information. Firstly, we model local structural distribution based on anonymous random walks, followed by using the structural information to construct receptive fields guided with mutual information. Then, as the generated receptive fields are irregular, we design a sub-graph aggregator to boost node representations and theoretically prove that it has the ability to capture the complex structures in receptive fields. Experimental results demonstrate the power of STAR-GNN in learning structural receptive fields adaptively and encoding more informative structural characteristics in real-world networks.

This letter is aimed to compare the performance of canopy height models (CHMs) derived from airborne laser scanning (ALS) data and unmanned aerial vehicle (UAV) stereo images in the extraction of individual tree height and crown size. Treetops were identified using the local maximum algorithm from the Gaussian filtered CHMs. A parabola fitting was used to determine the crown size. Factors affecting the delineation results, such as point cloud density and the spatial distribution and growing status of trees, were analyzed. The results showed that the UAV stereo images, together with the ALS-derived digital elevation model (DEM), can achieve better performance than ALS data alone based on our data set. The match ratio between delineated and field-measured trees varied significantly, with the highest ratio of 66.94% obtained by UAV in the young aspen forest and the lowest ratio of 33.76% obtained by ALS in the old forest. Aside from the influence of point density, this letter also shed light on the important role that the spatial distribution and growing status of trees play in the delineation of individual trees. To conclude, integrating UAV stereo images with the ALS-derived DEM is effective in delineating individual tree attributes in small-scale windbreaks, which provides some suggestions for the future management of agriculture land.